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Background: Digital tomosynthesis has shown potential for increasing specificity and sensitivity compared to radiography for low‐dose chest imaging. Prior investigation of the s‐DCT system indicated potential, but additional iteration with improved scan speed, power, and angular span was necessary for translation. Purpose: The study aims to demonstrate and characterize a second‐generation stationary digital chest tomosynthesis (s‐DCT) scanner with increased x‐ray energy, tube current, and larger angular span. Methods: The second‐generation s‐DCT system employed a meter‐long linear carbon nanotube (CNT) source array integrated with a digital detector and patient imaging table. Tube output, focal spot size, modulation transfer function (MTF), artifact spread function (ASF), and imaging performance were evaluated. A lung phantom with simulated nodules was imaged for clinical task‐based demonstration. Results: The scanner achieved a 6 s scan time, significantly improved from the prior generation's 16 s. The x‐ray tube exhibited good current stability, with 20.4 ± 0.6 mA tube current and focal spot size aligned with specifications (IEC 0.8). The MTF confirmed enhanced spatial resolution of 2.4 lp/mm, comparable to commercial chest tomosynthesis systems. The ASF indicated improved depth resolution (5.2 mm, previously 9.5 mm). Phantom imaging showcased visualization of both high and low‐attenuation lung nodules. Conclusion: The second‐generation s‐DCT system exhibited improved performance in terms of tube power, scan time, and image quality. Enhanced in‐plane and depth resolution, along with faster imaging, suggest potential clinical benefits for improved diagnoses. Further clinical validation is warranted to ascertain the system's clinical utility. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions In silico clinical trials are becoming more sophisticated and allow for realistic assessment and comparisons of medical image system models. These fully computational models enable fast and affordable trial designs that can closely capture trends seen on real clinical trials.To evaluate three breast imaging system models for digital mammography (DM) and digital breast tomosynthesis (DBT) in a fully‐in‐silico longitudinal study.We developed in silico models for three different breast imaging systems by modeling relevant characteristics such as detector technology, pixel size, number of projections, and angular span. We use a computational image reader to detect masses at different growing stages to compute the relative system performance. Similarly, we compare calcification cluster detectability across systems. The Detectability area under the ROC curve (AUC) was calculated for each combination of breast density, device model, lesion size and type, and search area. We compared the absolute and relative AUC values for DM and DBT. The trial consisted of 45 000 simulated images corresponding to 750 virtual digital patient models.We observed proportional AUC values with increasing mass size. On the other hand, higher breast densities showed lower AUC values. For masses, we found significant performance differences between device models. The highest average AUC difference between DBT and DM was 0.109, benefiting DBT. For calcifications, DM showed higher performance than DBT, especially in highly dense breasts. The highest AUC difference on a model was –0.055, benefiting DM.In this fully‐in‐silico imaging trial, we compared three imaging systems with different detector technologies on the same cohort of virtual digital patient models. We found that breast device systems can lead to visibility differences in masses and calcifications. Our longitudinal, multi‐device in silico study was possible because of the versatility and flexibility of in silico methods. This study shows the advantages of this in silico methodology in lowering the resources needed for device development, optimization, and regulatory evaluation. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Transbronchial needle biopsy is crucial for diagnosing lung cancer, yet its efficacy depends on accurately localizing the target lesion and biopsy needle. Digital tomosynthesis (DTS) is considered a promising imaging modality for guiding bronchoscopy procedures due to its low radiation dose and small footprint relative to cone‐beam computed tomography (CBCT). However, the image quality of DTS is still not sufficient for an accurate guidance, mainly due to its limited‐angle acquisition.Preoperative computed tomography (CT) scans are often performed prior to bronchoscopy interventions for diagnosis or to plan the procedure. The CT images are of high quality and are characterized by a high spatial resolution compared to intraoperative DTS images. These patient‐specific prior CT images and the intraoperative DTS images share a fair amount of anatomical information. The main differences only stem from patient positioning and respiratory motion. When these differences are addressed properly, prior CT images augment intraoperative DTS image reconstruction with strong prior knowledge and potentially enhance DTS image quality.We propose in this work a prior‐aided DTS image reconstruction technique leveraging prior CT images to improve DTS image quality. This technique is based on a recently published deformable CT‐to‐DTS image registration algorithm which is customized for bronchoscopy interventions. The main idea is to register a prior CT image to an intermediate DTS image reconstructed using the standard iterative algebraic reconstruction technique (ART), then to re‐reconstruct the DTS image using ART and the registered prior CT image as a first estimation.The proposed prior‐aided reconstruction method was tested on a physical phantom and six patient bronchoscopy datasets. Real DTS data acquired with a pseudo‐linear (PL) scan geometry and simulated DTS data generated according to a spherical ellipse (SE) scan geometry were considered. Results evaluated qualitatively by visual inspection and quantitatively by computing Pearson's correlation (PC) with respect to the reference CBCT images suggest significant improvements in image quality using the prior‐aided DTS reconstruction compared to the standard zero‐initialized ART reconstruction. PC coefficients of the six patient datasets were on average 0.64±0.13$0.64\pm 0.13$ and 0.55±0.13$0.55\pm 0.13$ using a zero‐initialized ART reconstruction with SE data and PL data, respectively, and 0.82±0.09$0.82\pm 0.09$ and 0.72±0.09$0.72\pm 0.09$ using the proposed prior‐aided reconstruction with SE data and PL data, respectively.While the initial estimation in iterative reconstruction algorithms is often overlooked, we proved that initial estimation is of critical importance in DTS image reconstruction and we have demonstrated the profound advantages of integrating prior CT images in intraoperative DTS image reconstruction. CT‐augmented DTS offers a viable alternative to CBCT in guiding bronchoscopy interventions at a fraction of the radiation dose. Further clinical studies are needed to validate improved diagnostic yield. [ABSTRACT FROM AUTHOR]

Background: Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one‐dimensional (1D) scan geometry, x‐ray source motion during acquisition, and patient motion artifacts from long exam times. Purpose: A thermionic cathode x‐ray system employing two‐dimensional (2D, planar) multiple x‐ray‐source arrays (MXA) is proposed to improve DBT image quality. Methods: A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of Δd${{\Delta}}d$ = 23 mm. The 11 cathodes were paired with 11 molybdenum 50 mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source‐to‐detector distance of 630 mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance (δ$\delta $) ranging from δ$\delta $ = 0 mm (conventional 1D) to δ$\delta $ = 57 mm. All sources operated at 30 kV with 80 mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast‐to‐noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast‐detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Results: Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter = 5.9 A.U., δ$\delta $ = 0 mm) and 2D MXA arrays with increasing δ$\delta $, with statistical significance between the 1D MXA and 2D MXA with δ$\delta $ = 57 mm (σclutter = 5.0 A.U., p < 0.001). The contrast‐detail/clutter phantom demonstrated CNR from the 2D MXA (δ = 57 mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field‐of‐view (away from the 1D MXA position), whereas the 2D MXA geometry (δ = 57 mm) did not exhibit the null cone. The SSP was 15%–50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. Conclusions: The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x‐ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast‐enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications.Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks.The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre‐set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three‐dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two‐dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast‐calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images.The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings.This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures. [ABSTRACT FROM AUTHOR]

Background: A next generation tomosynthesis (NGT) system, capable of two‐dimensional source motion, detector motion in the perpendicular direction, and magnification tomosynthesis, was constructed to investigate different acquisition geometries. Existing position‐based geometric calibration methods proved ineffective when applied to the NGT geometries. Purpose: A line‐based iterative calibration method is developed to perform accurate geometric calibration for the NGT system. Methods: The proposed method calculates the system geometry through virtual line segments created by pairs of fiducials within a calibration phantom, by minimizing the error between the line equations computed from the true and estimated fiducial projection pairs. It further attempts to correct the 3D fiducial locations based on the initial geometric calibration. The method's performance was assessed via simulation and experimental setups with four distinct NGT geometries: X, T, XZ, and TZ. The X geometry resembles a conventional DBT acquisition along the chest wall. The T geometry forms a "T"‐shaped source path in mediolateral (ML) and posteroanterior (PA) directions. A descending detector motion is added to both X and T geometries to form the XZ and TZ geometries, respectively. Simulation studies were conducted to assess the robustness of the method to geometric perturbations and inaccuracies in fiducial locations. Experimental studies were performed to assess the impact of phantom magnification and the performance of the proposed method for various geometries, compared to the traditional position‐based method. Star patterns were evaluated for both qualitative and quantitative analyses; the Fourier spectral distortions (FSDs) graphs and the contrast transfer function (CTF) were extracted. The limit of spatial resolution (LSR) was measured at 5% modulation of the CTF. Results: The proposed method presented is highly robust to geometric perturbation and fiducial inaccuracies. After the line‐based iterative method, the mean distance between the true and estimated fiducial projections was [X, T, XZ, TZ]: [0.01, 0.01, 0.02, 0.01] mm. The impact of phantom magnification was observed; a contact‐mode acquisition of a calibration phantom successfully provided an accurate geometry for 1.85× magnification images of a star pattern, with the X geometry. The FSD graphs for the contact‐mode T geometry acquisition presented evidence of super‐resolution, with the LSR of [0°‐quadrant: 8.57, 90°‐quadrant: 8.47] lp/mm. Finally, a contact‐mode XZ geometry acquisition and a 1.50× magnification TZ geometry acquisition were reconstructed with three calibration methods—position‐based, line‐based, and iterative line‐based. As more advanced methods are applied, the CTF becomes more isotropic, the FSD graphs demonstrate less spectral leakage as super‐resolution is achieved, and the degree of blurring artifacts reduces significantly. Conclusions: This study introduces a robust calibration method tailored to the unique requirements of advanced tomosynthesis systems. By employing virtual line segments and iterative techniques, we ensure accurate geometric calibration while mitigating the limitations posed by the complex acquisition geometries of the NGT system. Our method's ability to handle various NGT configurations and its tolerance to fiducial misalignment make it a superior choice compared to traditional calibration techniques. [ABSTRACT FROM AUTHOR]

Background: Tomosynthesis systems are three‐dimensional (3‐D) medical imaging devices that operate over limited acquisition angles using low radiation dosages. To measure the spatial resolution performance of a tomosynthesis system, the modulation transfer function (MTF) is widely used as a quantitative evaluation metric. Purpose: We previously introduced a method to estimate the full 3‐D MTF of a cone‐beam computed tomography system using two‐dimensional (2‐D) Richardson‐Lucy (RL) deconvolution with Tikhonov–Miller regularization. However, this method can not be applied directly to estimate the 3‐D MTF of a tomosynthesis system, since the unique artifacts (i.e., shadow artifacts, spreading tails, directional blurring, and high‐level noise) of the system produce several errors that lower the estimation performance. Varying positions of the negative pixels due to shadow artifacts and spreading tails cause inconsistent deconvolution performances at each of the directional projections, and the severe noise in the reconstructed images cause noise amplification during estimation. This work proposes several modifications to the previous method to resolve the inconsistent performance and noise amplification errors to increase the full 3‐D MTF estimation accuracy. Methods: Three modifications were introduced to the 2‐D RL deconvolution to prevent estimation errors and improve MTF estimation performance: non‐negativity relaxation function, cost function to terminate the iterative process of RL deconvolution, and regularization strength for noise control. To validate the effectiveness of the proposed modifications, we reconstructed sphere phantoms from simulation and experimental tomosynthesis studies in the iso‐center and offset‐center positions as well as estimated the full 3‐D MTFs using the previous and proposed methods. We compared the 3‐D render images, central plane images, and center profiles of the estimated 3‐D MTFs and calculated the full widths at half and tenth maximum for quantitative evaluation. Results: The previous method cannot estimate the full 3‐D MTF of a tomosynthesis system; its inaccurate negative pixel relaxation produces circular‐shaped errors, and the mean squared error based simple cost function for termination causes inconsistent estimation at each directional projection to diminish the clear edges of the low‐frequency drop and missing sample regions. Noise amplification from lack of noise regularization is also seen in the previous method results. Compared to the previous method, the proposed method shows superior estimation performance at reducing errors in both the simulation and experimental studies regardless of object position. The proposed method preserves the low‐frequency drop, missing sample regions from the limited acquisition angles, and missing cone region from the offset‐center position; the estimated MTFs also show FWHM and FWTM values close to those of the ideal MTFs than with the previous method. Conclusions: This work presents a method to estimate the full 3‐D MTF of a tomosynthesis system. The proposed modifications prevent circular‐shaped errors and noise amplification due to the geometry for limited acquisition angles and high noise levels. Compared to our previous method, the proposed scheme show better performance for estimating the 3‐D MTF of the tomosynthesis system. [ABSTRACT FROM AUTHOR]

Background: X‐ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. Purpose: To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). Methods: Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast‐enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC‐GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons). Results: The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD. Conclusions: For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry. [ABSTRACT FROM AUTHOR]

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast‐enhanced variants. In addition, it includes models of the breast in both the cranio‐caudal and the medio‐lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software. [ABSTRACT FROM AUTHOR]

Background: The Tomosynthesis Mammography Imaging Screening Trial (TMIST), EA1151 conducted by the Eastern Cooperative Oncology Group (ECOG)/American College of Radiology Imaging Network (ACRIN) is a randomized clinical trial designed to assess the effectiveness for breast cancer screening of digital breast tomosynthesis (TM) compared to digital mammography (DM). Equipment from multiple vendors is being used in the study. Purpose: For the findings of the study to be valid and capture the true capacities of the two technology types, it is important that all equipment is operated within appropriate parameters with regard to image quality and dose. A harmonized QC program was established by a core physics team. Since there are over 120 trial sites, a centralized, automated QC program was chosen as the most practical design. This report presents results of the weekly QC testing program. A companion paper will review quality monitoring based on data from the headers of the patient images. Methods: Study images are collected centrally after de‐identification using the "TRIAD" application developed by ACR. The core physics team devised and implemented a minimal set of quality control (QC) tests to evaluate the tomosynthesis and 2D mammography systems. Weekly, monthly and annual testing is performed by the site mammography technologists with images submitted directly to the physics core. The weekly physics QC tests are described: SDNR of a low‐contrast mass object, artifact spread, spatial resolution, tracking of technical factors, and in‐slice noise power spectra. Results: As of December 31, 2022 (5 years), 145 sites with 411 machines had submitted QC data. A total of 136 742 TMIST participant screening imaging studies had been performed. The 5th and 95th percentile mean glandular doses for a single tomosynthesis exposure to a 4.0 cm thick PMMA phantom ("standard breast phantom") were 1.24 and 1.68 mGy respectively. The largest sources of QC non‐conformance were: operator error, not following the QC protocol exactly, unreported software updates and preventive maintenance activities that affected QC setpoints. Noise power spectra were measured, however, standardization of performance targets across machine types and software revisions was difficult. Nevertheless, for each machine type, test measurement results were very consistent when the protocol was followed. Deviations in test results were mostly related to software and hardware changes. Conclusion: Most systems performed very consistently. Although this is a harmonized program using identical phantoms and testing protocols, it is not appropriate to apply universal threshold or target metrics across the machine types because the systems have different non‐linear reconstruction algorithms and image display filters. It was found to be more useful to assess pass/fail criteria in terms of relative deviations from baseline values established when a system is first characterized and after equipment is changed. Generally, systems which needed repair failed suddenly, but in retrospect, for a few cases, drops in SDNR and increases in mAs were observed prior to tube failure. TMIST is registered as NCT03233191 by Clinicaltrials.gov [ABSTRACT FROM AUTHOR]

Purpose: A comprehensive, centrally‐monitored physics quality control (QC) program was developed for the Tomosynthesis Imaging Screening Trial (TMIST), a randomized controlled trial of digital breast tomosynthesis (TM) versus digital mammography (DM) for cancer screening. As part of the program, in addition to a set of phantom‐based tests, de‐identified data on image acquisition and processing parameters were captured from the DICOM headers of all individual patient images in the trial. These data were analyzed to assess the potential usefulness of header data from digital mammograms and tomosynthesis images of patients for quality assurance in breast imaging. Methods: Data were automatically extracted from the headers of all de‐identified patient mammograms and tomosynthesis images in the TMIST study. Image acquisition parameters and estimated radiation doses were tracked for individual sites, systems and across system types. These parameters included (among others) kV, target/filter use, number of acquired views per examination, AEC mode, compression thickness and force and detector temperature. Consistency of manually entered study data parameters (subject ID, screening time‐point) from TMIST was evaluated. Preliminary observations from the program are presented. Results: We report on data from 812 651 images from 135 525 examinations acquired between October, 2017 and December, 2022. Data came from 6 system models from 3 manufacturers. There was greater variability both in the number of views used and in the estimated (proxy) doses received in DM exams compared to TM. Mean proxy doses per examination varied among manufacturers from 2.76–4.54 mGy for DM and 3–4.84 mGy for the tomosynthesis component in the TM arm with maximum examination proxy doses of 20 and 26 mGy for DM and TM respectively. Mean proxy doses per examination for the combination examination in TM (tomosynthesis plus digital mammography) varied from 6.6 to 7.6 mGy among manufacturers with a maximum of 44.5 mGy. Conclusions: Overall, modern digital mammography and tomosynthesis systems used in TMIST have operated very reliably. Doses vary considerably due to variation in the number of views per examination, thickness and fibro‐glandularity of the breast, and choices in the use of synthesized versus actual 2D mammography in the TM examination. These data may also be useful in predicting equipment problems. Header information is valuable not only for automated QC, but also for cross‐checking accuracy and consistency of data in a clinical study. [ABSTRACT FROM AUTHOR]

Background: Limited scan angles cause severe distortions and artifacts in reconstructed tomosynthesis images when the Feldkamp‐Davis‐Kress (FDK) algorithm is used for the purpose, which degrades clinical diagnostic performance. These blurring artifacts are fatal in chest tomosynthesis images because precise vertebrae segmentation is crucial for various diagnostic analyses, such as early diagnosis, surgical planning, and injury detection. Moreover, because most spinal pathologies are related to vertebral conditions, the development of methods for accurate and objective vertebrae segmentation in medical images is an important and challenging research area. Purpose: The existing point‐spread‐function‐(PSF)‐based deblurring methods use the same PSF in all sub‐volumes without considering the spatially varying property of tomosynthesis images. This increases the PSF estimation error, thus further degrading the deblurring performance. However, the proposed method estimates the PSF more accurately by using sub‐CNNs that contain a deconvolution layer for each sub‐system, which improves the deblurring performance. Methods: To minimize the effect of the spatially varying property, the proposed deblurring network architecture comprises four modules: (1) block division module, (2) partial PSF module, (3) deblurring block module, and (4) assembling block module. We compared the proposed DL‐based method with the FDK algorithm, total‐variation iterative reconstruction with GP‐BB (TV‐IR), 3D U‐Net, FBPConvNet, and two‐phase deblurring method. To investigate the deblurring performance of the proposed method, we evaluated its vertebrae segmentation performance by comparing the pixel accuracy (PA), intersection‐over‐union (IoU), and F‐score values of reference images to those of the deblurred images. Also, pixel‐based evaluations of the reference and deblurred images were performed by comparing their root mean squared error (RMSE) and visual information fidelity (VIF) values. In addition, 2D analysis of the deblurred images were performed by artifact spread function (ASF) and full width half maximum (FWHM) of the ASF curve. Results: The proposed method was able to recover the original structure significantly, thereby further improving the image quality. The proposed method yielded the best deblurring performance in terms of vertebrae segmentation and similarity. The IoU, F‐score, and VIF values of the chest tomosynthesis images reconstructed using the proposed SV method were 53.5%, 28.7%, and 63.2% higher, respectively, than those of the images reconstructed using the FDK method, and the RMSE value was 80.3% lower. These quantitative results indicate that the proposed method can effectively restore both the vertebrae and the surrounding soft tissue. Conclusions: We proposed a chest tomosynthesis deblurring technique for vertebrae segmentation by considering the spatially varying property of tomosynthesis systems. The results of quantitative evaluations indicated that the vertebrae segmentation performance of the proposed method was better than those of the existing deblurring methods. [ABSTRACT FROM AUTHOR]

Background: The noise in digital breast tomosynthesis (DBT) includes x‐ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). Purpose: We previously developed a deep‐learning‐based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep‐learning‐based denoising to improve the detection of MCs in DBT. Methods: We have a modular breast phantom set containing seven 1‐cm‐thick heterogeneous 50% adipose/50% fibroglandular slabs custom‐made by CIRS, Inc. (Norfolk, VA). We made six 5‐cm‐thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125–0.150, 0.150–0.180, 0.180–0.212, 0.212–0.250 mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter‐balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. Results: The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p < 0.005, two‐tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9 ± 4.6, 2.8 ± 3.7, and 2.7 ± 3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p ≤ 0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. Conclusions: This observer study using breast phantom images showed that deep‐learning‐based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Since human observer studies are resource‐intensive, mathematical model observers are frequently used to assess task‐based image quality. The most common implementation of these model observers assume that the signal information is exactly known. However, these tasks cannot thoroughly represent situations where the signal information is not exactly known in terms of size and shape. Purpose: Considering the limitations of the tasks for which signal information is exactly known, we proposed a convolutional neural network (CNN)‐based model observer for signal known statistically (SKS) and background known statistically (BKS) detection tasks in breast tomosynthesis images. Methods: A wide parameter search was conducted from six different acquisition angles (i.e., 10°, 20°, 30°, 40°, 50°, and 60°) within the same dose level (i.e., 2.3 mGy) under two separate acquisition schemes: (1) constant total number of projections, and (2) constant angular separation between projections. Two different types of signals: spherical (i.e., SKE tasks) and spiculated (i.e., SKS tasks) were used. The detection performance of the CNN‐based model observer was compared with that of the Hotelling observer (HO) instead of the IO. Pixel‐wise gradient‐weighted class activation mapping (pGrad‐CAM) map was extracted from each reconstructed tomosynthesis image to provide an intuitive understanding of the trained CNN‐based model observer. Results: The CNN‐based model observer achieved a higher detection performance compared to that of the HO for all tasks. Moreover, the improvement in its detection performance was greater for SKS tasks compared to that for SKE tasks. These results demonstrated that the addition of nonlinearity improved the detection performance owing to the variation of the background and signal. Interestingly, the pGrad‐CAM results effectively localized the class‐specific discriminative region, further supporting the quantitative evaluation results of the CNN‐based model observer. In addition, we verified that the CNN‐based model observer required fewer images to achieve the detection performance of the HO. Conclusions: In this work, we proposed a CNN‐based model observer for SKS and BKS detection tasks in breast tomosynthesis images. Throughout the study, we demonstrated that the detection performance of the proposed CNN‐based model observer was superior to that of the HO. [ABSTRACT FROM AUTHOR]

Background: Projection imaging phantoms are often optimized for 2‐dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and established phantoms. Purpose: We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin et al. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess the objectivity. Methods: The container size was increased to a diameter of 24 cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non‐spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two‐component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra‐human observer, inter‐human observer and inter‐phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all three variations. Results: The difference of the attenuation coefficients between the lesion models and the background was 0.20 cm−1 (with W/Al at 32 kV, equivalent to 19–20 keV effective energy) compared to 0.21 cm−1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0 mm for the Siemens Mammomat Revelation. For the masses, the Vintra$V_{intra}$ for the intra‐observer variation was 0.248, the averaged inter‐observer variation, V¯inter$\overline{V}_{inter}$ was 0.383. Vphantom$V_{phantom}$ for phantom variance was 0.321. For the micro‐calcifications, Vintra$V_{intra}$ was 0.0429, V¯inter=$\overline{V}_{inter}=$ 0.0731 and Vphantom=$V_{phantom}=$ 0.0759. Conclusions: Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for quality control. [ABSTRACT FROM AUTHOR]

Background: Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo‐3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations. Purpose: To predict the scatter radiation signal in DBT projections within clinically‐acceptable times and using only clinically‐available data, such as compressed breast thickness and acquisition angle. Methods: MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically‐shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide‐angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously‐published scatter‐to‐primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter‐corrected projection images, were also tracked. Results: The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), −0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of −0.21% (IQR, −0.35% to −0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within ± 15% of the previously‐published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL‐based scatter corrected and anti‐scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from −16% and −11% to −2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01 s. Generating scatter corrected images took 0.03 s per projection for clinical exams and 0.16 s for one entire projection set. Conclusions: This DL‐based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications. [ABSTRACT FROM AUTHOR]

Purpose: High dose rate (HDR) brachytherapy rapidly delivers dose to targets with steep dose gradients. This treatment method must adhere to prescribed treatment plans with high spatiotemporal accuracy and precision, as failure to do so may degrade clinical outcomes. One approach to achieving this goal is to develop imaging techniques to track HDR sources in vivo in reference to surrounding anatomy. This work investigates the feasibility of using an isocentric C‐arm x‐ray imager and tomosynthesis methods to track Ir‐192 HDR brachytherapy sources in vivo over time (4D). Methods: A tomosynthesis imaging workflow was proposed and its achievable source detectability, localization accuracy, and spatiotemporal resolution were investigated in silico. An anthropomorphic female XCAT phantom was modified to include a vaginal cylinder applicator and Ir‐192 HDR source (0.5 × 0.5 × 5.0 mm3), and the workflow was carried out using the MC‐GPU Monte Carlo image simulation platform. Source detectability was characterized using the reconstructed source signal‐difference‐to‐noise‐ratio (SDNR), localization accuracy by the absolute 3D error in its measured centroid location, and spatiotemporal resolution by the full‐width‐at‐half‐maximum (FWHM) of line profiles through the source in each spatial dimension considering a maximum C‐arm angular velocity of 30° per second. The dependence of these parameters on acquisition angular range (θtot = 0°–90°), number of views, angular increment between views (Δθ = 0°–15°), and volumetric constraints imposed in reconstruction was evaluated. Organ voxel doses were tallied to derive the workflow's attributable effective dose. Results: The HDR source was readily detected and its centroid was accurately localized with the proposed workflow and method (SDNR: 10–40, 3D error: 0–0.144 mm). Tradeoffs were demonstrated for various combinations of image acquisition parameters; namely, increasing the tomosynthesis acquisition angular range improved resolution in the depth‐encoded direction, for example from 2.5 mm to 1.2 mm between θtot = 30o and θtot = 90o, at the cost of increasing acquisition time from 1 to 3 s. The best‐performing acquisition parameters (θtot = 90o, Δθ = 1°) yielded no centroid localization error, and achieved submillimeter source resolution (0.57 × 1.21 × 5.04 mm3 apparent source dimensions, FWHM). The total effective dose for the workflow was 263 µSv for its required pre‐treatment imaging component and 7.59 µSv per mid‐treatment acquisition thereafter, which is comparable to common diagnostic radiology exams. Conclusions: A system and method for tracking HDR brachytherapy sources in vivo using C‐arm tomosynthesis was proposed and its performance investigated in silico. Tradeoffs in source conspicuity, localization accuracy, spatiotemporal resolution, and dose were determined. The results suggest this approach is feasible for localizing an Ir‐192 HDR source in vivo with submillimeter spatial resolution, 1–3 second temporal resolution and minimal additional dose burden. [ABSTRACT FROM AUTHOR]

Beginning around 1972 with the introduction of CT, a steady transition from analog to digital imaging in radiology took place. Here, I offer a personal perspective of the exciting multi‐institutional and multidisciplinary team effort of developing digital mammography. That effort required the collaboration of visionary individuals in academic research labs, industry, and the clinical arena, catalyzed by a focused commitment from government (NCI and The Office of Women's Health). This collaboration greatly accelerated the timeline from laboratory prototypes to clinical systems and evaluation, resulting in a new imaging modality and, later, several spinoff applications (CAD, contrast‐enhanced mammography, tomosynthesis) that provide improved earlier detection of breast cancer. [ABSTRACT FROM AUTHOR]

Image quality models based on cascaded systems analysis and task‐based imaging performance were an important aspect of the emergence of 2D and 3D digital x‐ray systems over the last 25 years. This perspective vignette offers cursory review of such developments and personal insights that may not be obvious within previously published scientific literature. The vignette traces such models to the mid‐1990s, when flat‐panel x‐ray detectors were emerging as a new base technology for digital radiography and benefited from the rigorous, objective characterization of imaging performance gained from such models. The connection of models for spatial resolution and noise to spatial‐frequency‐dependent descriptors of imaging task provided a useful framework for system optimization that helped to accelerate the development of new technologies to first clinical use. Extension of the models to new technologies and applications is also described, including dual‐energy imaging, photon‐counting detectors, phase contrast imaging, tomosynthesis, cone‐beam CT, 3D image reconstruction, and image registration. [ABSTRACT FROM AUTHOR]

Background: Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast‐like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms. Purpose: To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts. Methods: SL scans were acquired at our institution during routine craniocaudal‐view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest‐to‐nipple distance (CND), the projected breast area (PBA) and the length along the chest‐wall (LCW). These features were compared using a paired t‐test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest‐wall. Results: The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16 mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6 mm, 1.6 mm, and 2.2 cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68 mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model‐based breast shapes with a specific breast thickness. Conclusion: There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github. [ABSTRACT FROM AUTHOR]

Purpose: Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g., intensity distribution, textures) to the FFDM images. However, the FFDM image has a different texture pattern from DBT. The difference in texture pattern might make the training of the neural network unstable and result in high-intensity distortion, which makes it hard to decrease intensity distortion and increase perceptual similarity (e.g., generate similar textures) at the same time. Clinically, radiologists want to have a 2D synthesized image that feels like an FFDM image in vision and preserves local structures such as both mass and microcalcifications (MCs) in DBT because radiologists have been trained on reading FFDM images for a long time, while local structures are important for diagnosis. In this study, we proposed to use a deep convolutional neural network to learn the transformation to generate SDM from DBT. Method: To decrease intensity distortion and increase perceptual similarity, a multi-scale cascaded network (MSCN) is proposed to generate low-frequency structures (e.g., intensity distribution) and high-frequency structures (e.g., textures) separately. The MSCN consist of two cascaded sub-networks: the first sub-network is used to predict the low-frequency part of the FFDM image; the second sub-network is used to generate a full SDM image with textures similar to the FFDM image based on the prediction of the first sub-network. The meansquared error (MSE) objective function is used to train the first sub-network, termed low-frequency network, to generate a low-frequency SDM image. The gradient-guided generative adversarial network's objective function is to train the second sub-network, termed high-frequency network, to generate a full SDM image with textures similar to the FFDM image. Results: 1646 cases with FFDM and DBT were retrospectively collected from the Hologic Selenia system for training and validation dataset, and 145 cases with masses or MC clusters were independently collected from the Hologic Selenia system for testing dataset. For comparison, the baseline network has the same architecture as the high-frequency network and directly generates a full SDM image. Compared to the baseline method, the proposed MSCN improves the peak-to-noise ratio from 25.3 to 27.9 dB and improves the structural similarity from 0.703 to 0.724, and significantly increases the perceptual similarity. Conclusions: The proposed method can stabilize the training and generate SDM images with lower intensity distortion and higher perceptual similarity. [ABSTRACT FROM AUTHOR]

Background: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. Purpose: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. Methods: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. Formaterial decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms.A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. Results: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. Conclusions: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts. [ABSTRACT FROM AUTHOR]

Background: Xenon-enhanced dual-energy (DE) computed tomography (CT) and hyperpolarized noble-gasmagnetic resonance imaging (MRI) providemaps of lung ventilation that can be used to detect chronic obstructive pulmonary disease (COPD) early in its development and predict respiratory exacerbations. However, xenon-enhanced DE-CT requires high radiation doses and hyper-polarized noble-gas MRI is expensive and only available at a handful of institutions globally. Purpose: To present xenon-enhanced dual-energy tomosynthesis (XeDET) for low-dose, low-cost functional imaging of respiratory disease in an experimental phantom study. Methods: We propose using digital tomosynthesis to produce Xe-enhanced low-energy (LE) and high-energy (HE) coronal images.DE subtraction of the LE and HE images is used to suppress soft tissues. We used an imaging phantom to investigate image quality in terms of the area under the reciever operating characteristic curve (AUC) for the Non-PreWhitening model observer with an Eye filter and internal noise (NPWEi). The phantom simulated anatomic clutter due to lung parenchyma and attenuation due to soft tissue and lung tissue. Aluminum slats were used to simulate rib structures. A stepwedge consisting of an acrylic casing with sealed cylindrical air-filled cavities was used to simulate ventilation defects with step thicknesses of 0.5, 1, and 2 cm and cylindrical radii of 0.5, 0.75, and 1 cm. The phantom was ventilated with Xe and projection data were acquired using a flat-panel detector, a tube-voltage combination of 60/140 kV with 1.2 mm of copper filtration on the HE spectrum and an angular range of ±15 in 1 increments. The AUC of a NPWEi observer that has access only to a single coronal slice was calculated from measurements of the three-dimensional noise power spectrum and signal template. The AUC was calculated as a function of ventilation defect thickness and radius for total patient entrance air kermas ranging from 1.42 to 2.84 mGy with and without rib-simulating Al slats. For the AUC analysis, the observer internal noise level was obtained from an ad hoc calibration to a high-dose data set. Results: XeDET was able to suppress parenchyma-simulating clutter in coronal images enabling visualization of the simulated ventilation defects, but the limited angle acquisition resulted in residual clutter due to out-of -plane bone-mimmicking structures. The signal power of the defects increased linearly with defect radius and showed a ten-fold to fifteen-fold increase in signal power when the defect thickness increased from 0.5 to 2 cm. These trends agreed with theoretical predictions. Along the depth dimension, the power of the defects decreased exponentially with distance from the center of the defects with full-width half maxima that varied from 1.85 to 2.85 cm depending on the defect thickness and radius. The AUCs of the 1-cm-radius defect that was 2 cm in thickness ranged from good (0.8-0.9) to excellent (0.9-1.0) over the range of air kermas considered. Conclusions: Xenon-enhanced DE tomosynthesis has the potential to enable functional imaging of respiratory disease and should be further investigated as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches. [ABSTRACT FROM AUTHOR]

Background: Due to the complex nature of digital breast tomosynthesis (DBT) in imaging techniques, reading times are longer than 2D mammograms. A robust computer‐aided diagnosis system in DBT could help radiologists reduce their workload and reading times. Purpose: The purpose of this study was to develop algorithms for detecting biopsy‐proven breast lesions on DBT using multi‐depth level convolutional models and leveraging non‐biopsied samples. As biopsied positive samples in a lesion dataset are limited, we hypothesized that false positive (FP) findings by detection algorithms from non‐biopsied benign lesions could improve detection algorithms by using them as data augmentation. Approach: We first extracted 2D slices from DBT volumes with biopsy‐proven breast lesions (cancer and benign), with non‐biopsied benign lesions (actionable), and for controls. Then, to provide lesion continuity along the z‐direction, we combined a lesion slice with its immediate adjacent slices to synthesize 2.5‐dimensional (2.5D) images of the lesion by assigning them into R, G, and B color channels. We used 224 biopsy‐proven lesions from 39 cancer and 62 benign patients from a DBTex challenge dataset of 1000 scans. We included the 2.5D images of immediate neighboring slices from the lesion's center to increase the number of training samples. For lesion detection, we used the YOLOv5 algorithm as our base network. We trained a baseline algorithm (medium‐depth level) using biopsied samples to detect actionable FPs in non‐biopsied images. Afterward, we fine‐tuned the baseline model on the augmented image set (actionable FPs added). For lesion inferencing, we processed the DBT volume slice‐by‐slice to estimate bounding boxes in each slice, and then combined them by connecting bounding boxes along the depth via volumetric morphological closing. We trained an additional model (large) with deeper‐depth levels by repeating the above process. Finally, we developed an ensemble algorithm by combining the medium and large detection models. We used the free‐response operating characteristic curve to evaluate our algorithms. We reported mean sensitivity per FPs per DBT volume only for biopsied views and sensitivity at 2‐false positives per image (2FPI) for all views. However, due to the limited accessibility to the truth of the challenge validation and test datasets, we used sensitivity at 2FPI for statistical evaluation. Results: For the DBTex independent validation set, the medium baseline model achieved a mean sensitivity of 0.627 FPs per DBT volume, and a sensitivity of 0.640 at 2FPI. After adding actionable FP lesions, the model had an improved 2FPI of 0.769 over the baseline (p‐value = 0.013). Our ensemble algorithm with multi‐depth levels (medium + large) achieved a mean sensitivity of 0.815 FPs per DBT volume and an improved sensitivity at 2FPI of 0.80 over the baseline (p‐value < 0.001) on the validation set. Finally, our ensemble model achieved a mean sensitivity of 0.786 FPs per DBT volume and a sensitivity of 0.743 at 2FPI on the DBTex independent test set. Conclusions: Our results show that actionable FP findings hold useful information for lesion detection algorithms, and our ensemble detection model with multi‐depth levels improves lesion detection performance. [ABSTRACT FROM AUTHOR]

Purpose: The gold‐standard method for estimation of patient‐specific organ doses in digital tomosynthesis (DT) requires protocol‐specific Monte Carlo (MC) simulations of radiation transport in anatomically accurate computational phantoms. Although accurate, MC simulations are computationally expensive, leading to a turnaround time in the order of core hours for simulating a single exam. This limits their clinical utility. The purpose of this study is to overcome this limitation by utilizing patient‐ and protocol‐specific MC simulations to develop a comprehensive database of air‐kerma‐normalized organ dose coefficients for a virtual population of adult and pediatric patient models over an expanded set of exam protocols in DT for retrospective and prospective estimation of radiation dose in clinical tomosynthesis. Materials and methods: A clinically representative virtual population of 14 patient models was used, with pediatric models (M and F) at ages 1, 5, 10, and 15 and adult patient models (M and F) with body mass index (BMIs) at 10th, 50th, and 90th percentiles of the US population. A graphics processing unit (GPU)‐based MC simulation framework was used to simulate organ doses in the patient models, incorporating the scanner‐specific configuration of a clinical DT system (VolumeRad, GE Healthcare, Waukesha, WI, USA) and an expanded set of exam protocols, including 21 distinct acquisition techniques for imaging a variety of anatomical regions (head and neck, thorax, spine, abdomen, and knee). Organ dose coefficients (hn) were estimated by normalizing organ dose estimates to air kerma at 70 cm (X70cm) from the source in the scout view. The corresponding coefficients for projection radiography were approximated using organ doses estimated for the scout view. The organ dose coefficients were further used to compute air‐kerma‐normalized patient‐specific effective dose coefficients (Kn) for all combinations of patients and protocols, and a comparative analysis examining the variation of radiation burden across sex, age, and exam protocols in DT, and with projection radiography was performed. Results: The database of organ dose coefficients (hn) containing 294 distinct combinations of patients and exam protocols was developed and made publicly available. The values of Kn were observed to produce estimates of effective dose in agreement with prior studies and consistent with magnitudes expected for pediatric and adult patients across the different exam protocols, with head and neck regions exhibiting relatively lower and thorax and C‐spine (apsc, apcs) regions relatively higher magnitudes. The ratios (r = Kn/Kn,rad) quantifying the differences air‐kerma‐normalized patient‐specific effective doses between DT and projection radiography were centered around 1.0 for all exam protocols, with the exception of protocols covering the knee region (pawk, patk). Conclusions: This study developed a database of organ dose coefficients for a virtual population of 14 adult and pediatric XCAT patient models over a set of 21 exam protocols in DT. Using empirical measurements of air kerma in the clinic, these organ dose coefficients enable practical retrospective and prospective patient‐specific radiation dosimetry. The computation of air‐kerma‐normalized patient‐specific effective doses further enables the comparison of radiation burden to the patient populations between protocols and between imaging modalities (e.g., DT and projection radiography), as presented in this study. [ABSTRACT FROM AUTHOR]

Background: Understanding the magnitude and variability of the radiation dose absorbed by the breast fibroglandular tissue during mammography and digital breast tomosynthesis (DBT) is of paramount importance to assess risks versus benefits. Although homogeneous breast models have been proposed and used for decades for this purpose, they do not accurately reflect the actual heterogeneous distribution of the fibroglandular tissue in the breast, leading to biases in the estimation of dose from these modalities. Purpose: To develop and validate a method to generate patient‐derived, heterogeneous digital breast phantoms for breast dosimetry in mammography and DBT. Methods: The proposed phantoms were developed starting from patient‐based models of compressed breasts, generated for multiple thicknesses and representing the two standard views acquired in mammography and DBT, that is, cranio‐caudal (CC) and medio‐lateral‐oblique (MLO). Internally, the breast phantoms were defined as consisting of an adipose/fibroglandular tissue mixture, with a nonspatially uniform relative concentration. The parenchyma distributions were obtained from a previously described model based on patient breast computed tomography data that underwent simulated compression. Following these distributions, phantoms with any glandular fraction (1%–100%) and breast thickness (12–125 mm) can be generated, for both views. The phantoms were validated, in terms of their accuracy for average normalized glandular dose (DgN) estimation across samples of patient breasts, using 88 patient‐specific phantoms involving actual patient distribution of the fibroglandular tissue in the breast, and compared to that obtained using a homogeneous model similar to those currently used for breast dosimetry. Results: The average DgN estimated for the proposed phantoms was concordant with that absorbed by the patient‐specific phantoms to within 5% (CC) and 4% (MLO). These DgN estimates were over 30% lower than those estimated with the homogeneous models, which overestimated the average DgN by 43% (CC), and 32% (MLO) compared to the patient‐specific phantoms. Conclusions: The developed phantoms can be used for dosimetry simulations to improve the accuracy of dose estimates in mammography and DBT. [ABSTRACT FROM AUTHOR]

Background: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X‐ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and in‐plane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation. Purpose: In this study, we propose a deep‐learning‐based methodology for projection‐domain breast thickness estimation and demonstrate a shape‐prior iterative DBT image reconstruction. Methods: We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin‐line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the X‐ray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel‐wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique. Results: The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root‐mean‐squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network‐generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single‐input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction. Conclusions: We presented a novel deep‐learning‐based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation. [ABSTRACT FROM AUTHOR]

Background: In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer. Architectural distortion (AD) is one of the breast lesions that need to be detected. Purpose: To develop a deep‐learning‐based computer‐aided detection (CADe) model for AD in digital breast tomosynthesis (DBT). This model uses the superior–inferior directional context of DBT and anatomic prior knowledge to reduce false positive (FP). It can identify some negative samples that cannot be distinguished by deep learning features. Methods: The proposed CADe model consists of three steps. In the first step, a deep learning detection network detects two‐dimensional (2D) candidates of ADs in DBT slices with the inputs preprocessed by Gabor filters and convergence measure. In the second step, three‐dimensional (3D) candidates are obtained by stacking 2D candidates along superior–inferior direction. In the last step, FP reduction for 3D candidates is implemented based on superior–inferior directional context and anatomic prior knowledge of breast. DBT data from 99 cases with AD were used as the training set to train the CADe model, and data from 208 cases were used as an independent test set (including 108 cases with AD and 100 cases without AD as the control group). The free‐response receiver operating characteristic and mean true positive fraction (MTPF) in the range of 0.05–2.0 FPs per volume are used to evaluate the model. Results: Compared with the baseline model based on convergence measure, our proposed method demonstrates significant improvement (MTPF: 0.2826 ± 0.0321 vs. 0.6640 ± 0.0399). Results of an ablation study show that our proposed context‐ and anatomy‐based FP reduction methods improve the detection performance. The number of FPs per DBT volume reduces from 2.47 to 1.66 at 80% sensitivity after employing these two schemes. Conclusions: The deep learning model demonstrates practical value for AD detection. The results indicate that introducing superior–inferior directional context and anatomic prior knowledge into model can indeed reduce FPs and improve the performance of CADe model. [ABSTRACT FROM AUTHOR]

Purpose: Mobile lung tumors are increasingly being treated with ablative radiotherapy, for which precise motion management is essential. In‐room stereoscopic radiography systems are able to guide ablative radiotherapy for stationary cranial lesions but not optimally for lung tumors unless fiducial markers are inserted. We propose augmenting stereoscopic radiographic systems with multiple small x‐ray sources to provide the capability of imaging with stereoscopic, single frame tomosynthesis. Methods: In single frame tomosynthesis, nine x‐ray sources are placed in a 3 × 3 configuration and energized simultaneously. The beams from these sources are collimated so that they converge on the tumor and then diverge to illuminate nine non‐overlapping sectors on the detector. These nine sector images are averaged together and filtered to create the tomosynthesis effect. Single frame tomosynthesis is intended to be an alternative imaging mode for existing stereoscopic systems with a field of view that is three times smaller and a temporal resolution equal to the frame rate of the detector. We simulated stereoscopic tomosynthesis and radiography using Monte Carlo techniques on 60 patients with early‐stage lung cancer from the NSCLC‐Radiomics dataset. Two board‐certified radiation oncologists reviewed these simulated images and rated them on a 4‐point scale (1: tumor not visible; 2: tumor visible but inadequate for motion management; 3: tumor visible and adequate for motion management; 4: tumor visibility excellent). Each tumor was independently presented four times (two viewing angles from radiography and two viewing angles from tomosynthesis) in a blinded fashion over two reading sessions. Results: The fraction of tumors that were rated as adequate or excellent for motion management (scores 3 or 4) from at least one viewing angle was 53% using radiography and 90% using tomosynthesis. From both viewing angles, the corresponding fractions were 7% for radiography and 48% for tomosynthesis. Readers agreed exactly on 62% of images and within 1 point on 98% of images. The acquisition technique was estimated to be 75 mAs at 120 kVp per treatment fraction assuming one verification image per breath, approximately one order of magnitude less than a standard dose cone beam CT. Conclusions: Stereoscopic tomosynthesis may provide a noninvasive, low dose, intrafraction motion verification technique for lung tumors treated by ablative radiotherapy. The system architecture is compatible with real‐time video capture at 30 frames per second. Simulations suggest that most, but not all, lung tumors can be adequately visualized from at least one viewing angle. [ABSTRACT FROM AUTHOR]

Purpose: Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. Methods: Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I–IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II–IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. Results: The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. Conclusions: For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs. [ABSTRACT FROM AUTHOR]

Purpose: To describe the creation process of a new breast phantom specifically designed to monitor quality control (QC) metrics consistency over several months in digital breast tomosynthesis (DBT). Methods: The semi‐anthropomorphic Tomomam® phantom was designed and evaluated twice monthly on a single Hologic Selenia Dimensions® unit over 5 months. The phantom is manufactured in a one‐piece epoxy resin homogeneous material as the basis for manufacturing, simulating breast tissue as 50% equivalent glandular (GL)/50% equivalent adipose (AD) and compressed thickness of 60 mm. The distribution of test objects on different planes inside the phantom should allow the quantification of 10 image quality metrics: reproducibility, signal difference‐to‐noise ratio (SDNR), geometric distortions in the plane, missing or added tissue at chest wall, at the top and bottom of images stack and lateral sides, in‐plane homogeneity, image scoring, artifact spread function (ASF), geometric distortions in the volume. SDNR was quantified according to GL and AD tissues. Tolerance criteria per parameter were described to analyze results over the study time. Results: Mean scores were equal to 15.4, 15.0, and 11.6 for masses, microcalcifications, and fibers, respectively. A large difference between GL and AD tissues for SDNR metrics was noted over the study time: the best results were obtained from GL tissues. Both geometric distortions and local homogeneity in the plane conformed to expected values. The mean volume value of the triangular prism was 11.3% greater than the expected value due to a reconstruction height equal to 66 mm instead of 60 mm. Conclusions: In this study, we monitored several QC metrics discriminating GL and AD tissues by using a new breast phantom developed by us. The preliminary clinical tests demonstrated that the Tomomam® phantom could be used to reliably and efficiently track 10 QC metrics with a single acquisition. More data need to be acquired to refine tolerance criteria for some metrics. [ABSTRACT FROM AUTHOR]

Purpose: Estimation of organ doses in digital tomosynthesis (DT) is challenging due to the lack of existing tools that accurately and flexibly model protocol‐ and view‐specific collimations and motion trajectories of the source and detector for a variety of exam protocols, and the computational inefficiencies of conducting MC simulations. The purpose of this study was to overcome these limitations by developing and benchmarking a GPU‐accelerated MC simulation framework compatible with patient‐specific computational phantoms for individualized estimation of organ doses in DT. Materials and methods: The framework for individualized estimation of dose in DT was developed as a two‐step workflow: (1) a custom MATLAB code that accepts a patient‐specific computational phantom and exam description (organ markers for defining the extremities of the anatomical region of interest, tube voltage, source‐to‐image distance, angular sweep range, number of projection views, and the pivot point to image distance ‐ PPID) to compute the field of views (FOVs) for a clinical DT system, and (2) a MC tool (developed using MC‐GPU) modeling the configuration of a clinical DT system to estimate organ doses based on the computed FOVs. Using this framework, we estimated organ doses for 28 radiosensitive organs in an adult reference patient model (M; 30 years) imaged using a commercial DT system (VolumeRad, GE Healthcare, Waukesha, WI). The estimates were benchmarked against values from a comparable organ dose estimation framework (reference dataset developed by the Advanced Laboratory for Radiation Dosimetry Studies at University of Florida) for a posterior–anterior chest exam. The resulting differences were quantified as percent relative errors and analyzed to identify any potential sources of bias and uncertainties. The timing performance (run duration in seconds) of the framework was also quantified for the same simulation to gauge the feasibility of the workflow for time‐constrained clinical applications. Results: The organ dose estimates from the developed framework showed a close agreement with the reference dataset, with percent relative errors ranging from −6.9% to 5.0% and a mean absolute percent difference of 1.7% over all radiosensitive organs, with the exception of testes and eye lens, for which the percent relative errors were higher at −18.9% and −27.6%, respectively, due to their relative positioning outside the primary irradiation field, leading to fewer photons depositing energy and consequently higher errors in estimated organ doses. The run duration for the same simulation was 916.3 s, representing a substantial improvement in performance over existing nonparallelized MC tools. Conclusions: This study successfully developed and benchmarked a GPU‐accelerated framework compatible with patient‐specific anthropomorphic computational phantoms for accurate individualized estimation of organ doses in DT. By enabling patient‐specific estimation of organ doses, this framework can aid clinicians and researchers by providing them with tools essential for tracking the radiation burden to patients for dose monitoring purposes and identifying the trends and relationships in organ doses for a patient population to optimize existing and develop new exam protocols. [ABSTRACT FROM AUTHOR]

Purpose: To non‐invasively evaluate the Ki‐67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. Methods: A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual‐ and population‐level clustering. Handcrafted radiomics and deep learning‐based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Results: Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429–0.727] vs. 0.559 [95% CI, 0.405–0.705], p‐value: 0.920; deep RSs: 0.670 [95% CI, 0.516–0.802] vs. 0.551 [95% CI, 0.397–0.698], p‐value: 0.776). The fusion RSs, combining handcrafted and deep learning‐based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714–0.900) and 0.792 (95% CI, 0.647–0.897) for the internal and external validation cohorts, respectively. Conclusions: The subregional radiomics approach can accurately predict the Ki‐67 level based on DBT data; thus, it may be used as a potential non‐invasive tool for preoperative treatment planning in BC. [ABSTRACT FROM AUTHOR]

Purpose: To validate the MC‐GPU Monte Carlo (MC) code for dosimetric studies in X‐ray breast imaging modalities: mammography, digital breast tomosynthesis, contrast enhanced digital mammography, and breast‐CT. Moreover, to implement and validate a phase space file generation routine. Methods: The MC‐GPU code (v. 1.5 DBT) was modified in order to generate phase space files and to be compatible with PENELOPE v. 2018 derived cross‐section database. Simulations were performed with homogeneous and anthropomorphic breast phantoms for different breast imaging techniques. The glandular dose was computed for each case and compared with results from the PENELOPE (v. 2014) + penEasy (v. 2015) and egs_brachy (EGSnrc) MC codes. Afterward, several phase space files were generated with MC‐GPU and the scored photon spectra were compared between the codes. The phase space files generated in MC‐GPU were used in PENELOPE and EGSnrc to calculate the glandular dose, and compared with the original dose scored in MC‐GPU. Results: MC‐GPU showed good agreement with the other codes when calculating the glandular dose distribution for mammography, mean glandular dose for digital breast tomosynthesis, and normalized glandular dose for breast‐CT. The latter case showed average/maximum relative differences of 2.3%/27%, respectively, compared to other literature works, with the larger differences observed at low energies (around 10 keV). The recorded photon spectra entering a voxel were similar (within statistical uncertainties) between the three MC codes. Finally, the reconstructed glandular dose in a voxel from a phase space file differs by less than 0.65%, with an average of 0.18%–0.22% between the different MC codes, agreement within approximately 2σ statistical uncertainties. In some scenarios, the simulations performed in MC‐GPU were from 20 up to 40 times faster than those performed by PENELOPE. Conclusions: The results indicate that MC‐GPU code is suitable for breast dosimetric studies for different X‐ray breast imaging modalities, with the advantage of a high performance derived from GPUs. The phase space file implementation was validated and is compatible with the IAEA standard, allowing multiscale MC simulations with a combination of CPU and GPU codes. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. Methods: Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53 mm were created. Simulated ill‐defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X‐ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four‐alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. Results: The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill‐defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p < 0.001) for all glandularities. Conclusions: We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p = 0.059) and 18% (p = 0.19) glandularities and significantly larger diameters for calcifications (p < 0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to perform a test object‐based comparison of the imaging performance of digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM). Methods: Two test objects were used, the CDMAM and the L1‐structured phantom. Small‐detail detectability was assessed using CDMAM and the microcalcification simulating specks in the L1‐structured background. Detection of spiculated and non‐spiculated mass‐like objects was assessed using the L1 phantom. Six different systems were included: Amulet Innovality (Fujifilm), Senographe Pristina (GEHC), 3Dimensions (Hologic), Giotto Class (IMS), Clarity 2D/3D (Planmed), and Mammomat Revelation (Siemens). Images were acquired under automatic exposure control (AEC) and at adjusted levels of AEC/2 and 2 × AEC level. Threshold gold thickness (Ttr) was established for the 0.13‐mm‐diameter CDMAM discs. Threshold diameters for the calcifications (dtr_c), the spiculated masses (dtr_sm), and for the non‐spiculated masses (dtr_nsm) were established. The threshold condition was defined as the thickness or diameter for a 62.5% correct score. Results: Ttr for DM was generally superior to DBT, which in turn was superior to SM, but for most systems, these differences between modes were not significant. For L1, no significant differences in dtr_c were found between DM and DBT. The increase in dtr_c from DM to SM at AEC dose was 1%, 19%, 11%, 14%, 46%, and 27% for the Fujifilm, GEHC, Hologic, IMS, Planmed, and Siemens, respectively, indicating significantly poorer performance for all vendors except for Fujifilm, Hologic, and IMS. For both mass types, DBT performed better than SM, while SM showed no significant difference with DM (except for Fujifilm spiculated masses). The dose had an impact on small‐detail detectability for both phantoms but did not influence the detection of either mass type. Conclusions: Both phantoms indicated potentially reduced small‐detail detectability for SM versus DM and DBT and should therefore not be used in stand‐alone mode. The L1 phantom demonstrated no significant difference in microcalcification detection between DM and DBT and also demonstrated the superiority of DBT, compared to DM for mass detection, for all six systems. [ABSTRACT FROM AUTHOR]

Purpose: In the reconstruction of volume breast images from x‐ray projections in breast tomosynthesis, some tomographic systems truncate the image data presented to the radiologist such that a non‐negligible amount of tissue may be missing from the breast image. QC tests were conducted to determine if this problem existed in imaging in the TMIST study. Methods: Test tools developed for TMIST containing small objects at known heights were used in routine weekly and annual QC testing of tomosynthesis units to assess the degree to which phantom material that was irradiated in imaging was excluded from the reconstructed image. Results from 318 tests on five system types from three manufacturers are reported. Results: The presence and extent of this problem varied among system types. The cause was most frequently related to machine errors in the determination of breast thickness or to deflection of components during breast compression. In particular, the problem occurred when a compression paddle other than the one calibrated for tomosynthesis was used for the tests. This was also verified to have occurred in some clinical imaging. Conclusions: Missing volume can be avoided by intentionally reconstructing additional image slices above and below the presumed locations of the breast support and compression plate. A compression paddle which has been calibrated for tomosynthesis should be used both for clinical imaging and testing. The prevalence of this phenomenon suggests that more frequent testing for volume coverage may be advisable. [ABSTRACT FROM AUTHOR]

Purpose: To present a dataset of computational digital breast phantoms derived from high‐resolution three‐dimensional (3D) clinical breast images for the use in virtual clinical trials in two‐dimensional (2D) and 3D x‐ray breast imaging. Acquisition and validation methods: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi‐automatic software was developed. The semi‐automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi‐automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. Data format and access: The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). Potential applications: The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x‐ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x‐ray breast imaging and as models for manufacturing anthropomorphic physical phantoms. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this study is to develop a three‐dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. Methods: In this work, the DIR‐DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR‐DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. Results: Results show that the proposed DIR‐DBTnet is able to reduce the in‐plane shadow artifacts and the out‐of‐plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)‐based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR‐DBTnet network is more accurate and consistent with the ground truth. Conclusions: In conclusion, a deep iterative reconstruction network, DIR‐DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR‐DBTnet has superior DBT imaging performance than the conventional algorithms. [ABSTRACT FROM AUTHOR]

Background: This article reports the first x‐ray phase sensitive breast tomosynthesis (PBT) system that is aimed for direct translation to clinical practice for the diagnosis of breast cancer. Purpose: To report the preclinical evaluation and comparison of the newly built PBT system with a conventional digital breast tomosynthesis (DBT) system. Methods and materials: The PBT system is developed based on a comprehensive inline phase contrast theoretical model. The system consists of a polyenergetic microfocus x‐ray source and a flat panel detector mounted on an arm that is attached to a rotating gantry. It acquires nine projections over a 15° angular span in a stop‐and‐shoot manner. A dedicated phase retrieval algorithm is integrated with a filtered back‐projection method that reconstructs tomographic slices. The American College of Radiology (ACR) accreditation phantom, a contrast detail (CD) phantom and mastectomy tissue samples were imaged at the same glandular dose levels by both the PBT and a standard of care DBT system for image quality characterizations and comparisons. Results: The PBT imaging scores with the ACR phantom are in good to excellent range and meet the quality assurance criteria set by the Mammography Quality Standard Act. The CD phantom image comparison and associated statistical analyses from two‐alternative forced‐choice reader studies confirm the improvement offered by the PBT system in terms of contrast resolution, spatial resolution, and conspicuity. The artifact spread function (ASF) analyses revealed a sizable lateral spread of metal artifacts in PBT slices as compared to DBT slices. Signal‐to‐noise ratio values for various inserts of the ACR and CD phantoms further validated the superiority of the PBT system. Mastectomy sample images acquired by the PBT system showed a superior depiction of microcalcifications vs the DBT system. Conclusion: The PBT imaging technology can be clinically employed for improving the accuracy of breast cancer screening and diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies the abilities of a twin‐robotic x‐ray slot‐scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography. Methods: We investigate the dose saving capabilities, especially in terms of the signal‐ and the contrast‐to‐noise ratio, as well as the scatter‐to‐primary ratio of the proposed slot‐scanning method in comparison to the state‐of‐the‐art method for length‐extended imaging. As a baseline, we use x‐ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose. To obtain an estimate of the photon‐related noise directly from an x‐ray image, we implement a Poisson‐Gaussian noise model. This model is used to compare the dose efficiency of two settings and combined with the well‐known KSNR to determine the transmission parameters. We present a method with an associated measurement protocol, utilizing the robotic capabilities of the used system to automatically obtain quasi‐scatter‐free ground‐truth data with exact geometric correspondence to full‐field and slot acquisitions. In total, we investigate two body regions (thoracic spine and lumbar spine) in anterior‐posterior view with two patient sizes (BMI = 22 and 30) in two acquisition modes (conventional and slot scan with a flat‐panel detector) with and without anti‐scatter grid using an anthropomorphic upper‐body phantom. Results: We have shown that it is feasible to combine the proposed approach with the KSNR for the determination of scatter rejection parameters. The use of an anti‐scatter grid is indicated for full‐field acquisitions allowing for dose savings up to 46% compared to their gridless counterparts. When changing the acquisition mode to the investigated slot scan, the use of an anti‐scatter grid has no major impact on the image quality in terms of dose efficiency, in particular for patients with a BMI of 22. However, an increased contrast improvement factor was found. For normal‐sized patients, up to 53% of dose can be saved additionally in comparison to full‐field acquisitions with grid. Moreover, we could demonstrate that a slot size of 5 cm and air gap of 10 cm is sufficient to achieve scatter‐to‐primary ratios, which are equal or better compared to those of the full‐field acquisitions with a grid. Conclusions: We have shown, that the slot‐scanning approach is always superior to the conventional full‐field acquisition in terms of signal‐to‐noise and scatter‐to‐primary ratios. Compared to the state‐of‐the‐art acquisition protocols with a grid, dose savings up to 53% are possible due to the scatter rejection without compromising the SNR. Hence, the use of the slot‐scanning method is indicated, especially when it comes to regularly carried‐out follow‐up acquisitions, for example, in the case of scoliosis monitoring. [ABSTRACT FROM AUTHOR]

Purpose: To develop a scheme to quantitatively assess localization accuracy of tomosynthesis‐guided vacuum‐assisted breast biopsy apparatus. Methods: A phantom containing a metallic pellet on a flexible plastic shaft was constructed and was tested in cranio‐caudal (CC) and lateral (LAT) arm biopsy geometries following the standard clinical breast biopsy workflow. Three points were manually digitized on tomosynthesis images including: the center of the target, and the tip of the needle in pre‐ and postfire positions. The needle trajectory was determined and four error metrics were defined: (1) stroke length error (difference between the nominal and measured stroke lengths); (2) Euclidian distance between the target and center of trough (i.e., aperture); (3) longitudinal distance between target and center of trough; and (4) lateral distance between target and needle. The proposed methodology was also evaluated on a breast gel phantom and the complete biopsy procedure, including vacuum‐assisted biopsy was performed. Results: Three biopsy geometries were investigated: (i) LAT arm on a prone table unit (Hologic, Affirm Prone), (ii) CC‐ and (iii) LAT arm in an upright unit (Hologic Affirm Upright). Both biopsy units passed the vendor‐provided daily localization accuracy test, with <1 mm nominal error in each dimension. The aforementioned error metrics (1) to (4) were (0.6, 1.8, 0.4, 1.7) mm, (0.4, 4.2, 4.1, 1.1) mm, and (0.3, 2.4, 0.7, 2.3) mm, respectively, for geometry‐I, ‐II, and ‐III. The gel phantom was tested on the upright unit with lateral arm and the error metrics (1) to (4) were 0.4, 2.5, 0.8, and 2.4 mm respectively. Conclusions: A framework was developed to evaluate the tomosynthesis‐guided breast biopsy localization error, allowing quantitative comparisons between different systems and biopsy configurations. The proposed framework can also be extended to the stereotactic breast biopsy units. We suggest that a quantitative tolerance level for localization accuracy of breast biopsy units be established. [ABSTRACT FROM AUTHOR]

Purpose: To develop a patient‐based breast density model by characterizing the fibroglandular tissue distribution in patient breasts during compression for mammography and digital breast tomosynthesis (DBT) imaging. Methods: In this prospective study, 88 breast images were acquired using a dedicated breast computed tomography (CT) system. The breasts in the images were classified into their three main tissue components and mechanically compressed to mimic the positioning for mammographic acquisition of the craniocaudal (CC) and mediolateral oblique (MLO) views. The resulting fibroglandular tissue distribution during these compressions was characterized by dividing the compressed breast volume into small regions, for which the median and the 25th and 75th percentile values of local fibroglandular density were obtained in the axial, coronal, and sagittal directions. The best fitting function, based on the likelihood method, for the median distribution was obtained in each direction. Results: The fibroglandular tissue tends to concentrate toward the caudal (about 15% below the midline of the breast) and anterior regions of the breast, in both the CC‐ and MLO‐view compressions. A symmetrical distribution was found in the MLO direction in the case of the CC‐view compression, while a shift of about 12% toward the lateral direction was found in the MLO‐view case. Conclusions: The location of the fibroglandular tissue in the breast under compression during mammography and DBT image acquisition is a major factor for determining the actual glandular dose imparted during these examinations. A more realistic model of the parenchyma in the compressed breast, based on patient image data, was developed. This improved model more accurately reflects the fibroglandular tissue spatial distribution that can be found in patient breasts, and therefore might aid in future studies involving radiation dose and/or cancer development risk estimation. [ABSTRACT FROM AUTHOR]

Purpose: Digital breast tomosynthesis (DBT) is a limited‐angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full‐field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet‐printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme. Method: A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5‐mm spiculated masses fabricated with potassium iodide (KI)‐doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance. Results: For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers. Conclusions: This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet‐printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems. [ABSTRACT FROM AUTHOR]

Purpose: To develop and test the feasibility of a two‐pass iterative reconstruction algorithm with material decomposition designed to obtain quantitative iodine measurements in digital breast tomosynthesis. Methods: Contrast‐enhanced mammography has shown promise as a cost‐effective alternative to magnetic resonance imaging for imaging breast cancer, especially in dense breasts. However, one limitation is the poor quantification of iodine contrast since the true three‐dimensional lesion shape cannot be inferred from the two‐dimensional (2D) projection. Use of limited angle tomography can potentially overcome this limitation by segmenting the iodine map generated by the first‐pass reconstruction using a convolutional neural network, and using this segmentation to restrict the iodine distribution in the second pass of the reconstruction. To evaluate the performance of the algorithms, a set of 2D digital breast phantoms containing targets with varying iodine concentration was used. In each breast phantom, a single simulated lesion with a random size (4 to 8 mm) was placed in a random location within each phantom, with the iodine distribution defined as either homogeneous or rim‐enhanced and blood iodine concentration set between 1.4 and 5.6 mg/mL. Limited angle projection data of these phantoms were simulated for wide and narrow angle geometries, and the proposed reconstruction and segmentation algorithms were applied. Results: The median Dice similarity coefficient of the segmented masks was 0.975 for the wide angle data and 0.926 for the narrow angle data. Using these segmentations during the second reconstruction pass resulted in an improvement in the concentration estimates (mean estimated‐to‐true concentration ratio, before and after second pass: 48% to 73% for wide angle; 30% to 73% for narrow angle), and a reduction in the coefficient of variation of the estimates (55% to 27% for wide angle; 54% to 35% for narrow angle). Conclusion: We demonstrate that the proposed two‐pass reconstruction can potentially improve accuracy and precision of iodine quantification in contrast‐enhanced tomosynthesis. [ABSTRACT FROM AUTHOR]

Purpose: A major technical obstacle to bringing x‐ray multicontrast (i.e., attenuation, phase, and dark‐field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure. The purpose of this work was to introduce a fast acquisition with seamless stage translation (FASST) technique to a prototype multicontrast breast imaging system for reduced image acquisition time that is clinically acceptable. Methods: The prototype system was constructed based on a Hologic full‐field digital mammography + digital breast tomosynthesis combination system. During each FASST acquisition process, a motorized stage holding a diffraction grating travels continuously with a constant velocity, and a train of 15 short x‐ray pulses (35 ms each) was delivered by using the Zero‐Degree Tomo mode of the Hologic system. Standard phase retrieval was applied to the 15 subimages without spatial interpolation to avoid spatial resolution loss. The method was evaluated using a physical phantom, a bovine udder specimen, and a freshly resected mastectomy specimen. The FASST technique was experimentally compared with single‐shot acquisition methods and the standard phase stepping method. Results: The image acquisition time of the proposed method is 3.7 s. In comparison, conventional phase stepping took 105 s using the same prototype imaging system. The mean glandular dose of both methods was matched at 1.3 mGy. No artifacts or spatial resolution loss was observed in images produced by FASST. In contrast, the single‐shot methods led to spatial resolution loss and residual moiré artifacts. Conclusions: The FASST technique reduces the data acquisition time of the prototype multicontrast x‐ray breast imaging system to 3.7 s, such that it is comparable to a clinical digital breast tomosynthesis exam. [ABSTRACT FROM AUTHOR]

Purpose: Digital breast tomosynthesis (DBT) is becoming increasingly used in clinical practice. In DBT, the microcalcification clusters may span across multiple slices, which makes it difficult for radiologists to directly assess these distributed clusters for diagnosis. We investigated a radiomics method to classify microcalcification clusters in DBT based on a semiautomatic segmentation process. Methods: We performed a retrospective study on a cohort of 275 patients (including 79 benign and 196 malignant cases) with a total of 550 DBT volumes. Our method consisted of three steps. The initial step was to semiautomatically segment the microcalcification clusters. Then, radiomics features were extracted from the initially segmented microcalcification clusters. Finally, the benign and malignant microcalcification clusters were differentiated by the random forest (RF) classifier using selected subset features. The radiomics models were evaluated both on view‐based and case‐based modes with features selected from different domains. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the classification performance. Results: Twenty‐six key features were selected from a total of 170 radiomics features and these features show promising classification performance. The highest AUC was 0.834 for view‐based mode and 0.868 for case‐based mode when using features selected from the 3D‐domain. The 2D‐domain radiomics features showed a statistically similar performance to the 3D features (P > 0.05). Conclusion: Radiomics models can provide encouraging performance in classification between malignant and benign microcalcification clusters which are semiautomatically segmented in DBT. [ABSTRACT FROM AUTHOR]

Purpose: Sparse‐data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C‐arm CT, on‐board four‐dimensional cone‐beam CT (4D CBCT), and industrial CT. However, sparse‐data image reconstruction remains challenging due to highly undersampled data. This work develops a data‐driven image reconstruction method for sparse‐data CT using deep neural networks (DNN). Methods: The new method so‐called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward‐backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage; and then AirNet with trained parameters can be used for end‐to‐end image reconstruction. Results: A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state‐of‐art DNN‐based postprocessing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed. The impact of image quality on radiotherapy treatment planning was evaluated for both photon and proton therapy, and AirNet achieved the best treatment plan quality, especially for proton therapy. For example, with limited‐angle data, the maximal target dose for AirNet was 109.5% in comparison with the ground truth 109.1%, while it was significantly elevated to 115.1% and 128.1% for FBPConvNet and LEARN, respectively. Conclusions: A new image reconstruction AirNet is developed for sparse‐data CT image reconstruction. AirNet achieved the best image reconstruction quality both visually and quantitatively among all methods under comparison for all sparse‐data scenarios (sparse‐view and limited‐angle), and provided the best photon and proton treatment plan quality based on sparse‐data CT. [ABSTRACT FROM AUTHOR]