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Background: The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use., Purpose: The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs., Method: MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA)., Results: The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions., Conclusion: The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background: The accuracy and variability of quantification in computed tomography angiography (CTA) are affected by the interplay of imaging parameters and patient attributes. The assessment of these combined effects has been an open engineering challenge., Purpose: In this study, we developed a framework that optimizes imaging parameters for accurate and consistent coronary stenosis quantification in cardiac CTA while accounting for patient-specific variables., Methods: The framework utilizes a task-specific image quality index, the estimability index ( e ' ), approximated by a surrogate estimability polynomial function (EPF) capable of finding the optimal protocol that (1) maximizes image quality with an upper bound for desired radiation dose or (2) minimizes the dose level with a lower bound of acceptable image quality. The optimization process was formulated with the decision variables being subject to a set of constraints. The methodology was verified using CTA data from a prior clinical trial (prospective multi-center imaging study for evaluation of chest pain) by assessing the concordance of its prediction with the trial results. Further, the framework was used to derive an optimum protocol for each case based on the patient attributes, gauging how much improvement would have been possible if the derived optimized protocol would have been deployed., Results: The framework produced results consistent with imaging physics principles with approximated EPFs of 97% accuracy. The feature importance evaluation demonstrated a close match with earlier studies. The verification study found e ' scores closely predicting the cardiologist scores to within 95% in terms of the area under the receiver operating characteristic curve and predicting potential for either an average of fourfold increase in e ' within a targeted dose or a reduction in radiation dose by an average of 57% without reducing the image quality., Conclusions: The protocol optimization framework provides means to assess and optimize CTA in terms of either image quality or radiation dose objectives with its results predicting prior clinical trial findings., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

The history of SPIE Medical Imaging and the legacy of its founders are part of our scientific heritage., (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Purpose : For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI. Approach : Our overarching objective is to demonstrate and highlight the major trajectories of imaging physics and how they are informed by the community and science present and presented at SPIE MI conferences from its inception to now. Results : These contributions range from the development of image science, image quality metrology, and image reconstruction to digital x-ray detectors that have revolutionized MI modalities including radiography, mammography, fluoroscopy, tomosynthesis, and computed tomography (CT). Recent advances in detector technology such as photon-counting detectors continue to enable new capabilities in MI. Conclusion : As we celebrate the past 50 years, we are also excited about what the next 50 years of SPIE MI will bring to the physics of MI., (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Purpose: Accurate classification of COVID-19 in chest radiographs is invaluable to hard-hit pandemic hot spots. Transfer learning techniques for images using well-known convolutional neural networks show promise in addressing this problem. These methods can significantly benefit from supplemental training on similar conditions, considering that there currently exists no widely available chest x-ray dataset on COVID-19. We evaluate whether targeted pretraining for similar tasks in radiography labeling improves classification performance in a sample radiograph dataset containing COVID-19 cases. Approach: We train a DenseNet121 to classify chest radiographs through six training schemes. Each training scheme is designed to incorporate cases from established datasets for general findings in chest radiography (CXR) and pneumonia, with a control scheme with no pretraining. The resulting six permutations are then trained and evaluated on a dataset of 1060 radiographs collected from 475 patients after March 2020, containing 801 images of laboratory-confirmed COVID-19 cases. Results: Sequential training phases yielded substantial improvement in classification accuracy compared to a baseline of standard transfer learning with ImageNet parameters. The test set area under the receiver operating characteristic curve for COVID-19 classification improved from 0.757 in the control to 0.857 for the optimal training scheme in the available images. Conclusions: We achieve COVID-19 classification accuracies comparable to previous benchmarks of pneumonia classification. Deliberate sequential training, rather than pooling datasets, is critical in training effective COVID-19 classifiers within the limitations of early datasets. These findings bring clinical-grade classification through CXR within reach for more regions impacted by COVID-19., (© 2021 The Authors.)

Purpose : Developing, validating, and evaluating a method for measuring noise texture directly from patient liver CT images (i.e., in vivo ). Approach : The method identifies target regions within patient scans that are least likely to have major contribution of patient anatomy, detrends them locally, and measures noise power spectrum (NPS) there using a previously phantom-validated technique targeting perceptual noise-non-anatomical fluctuations in the image that may interfere with the detection of focal lesions. Method development and validation used scanner-specific CT simulations of computational, anthropomorphic phantom (XCAT phantom, three phases of contrast-enhancement) with known ground truth of the NPS. Simulations were based on a clinical scanner (Definition Flash, Siemens) and clinically relevant settings (tube voltage of 120 kV at three dose levels). Images were reconstructed with filtered backprojection (kernel: B31, B41, and B50) and Sinogram Affirmed Iterative Reconstruction (kernel: I31, I41, and I50) using a manufacturer-specific reconstruction software (ReconCT, Siemens). All NPS measurements were made in the liver. Ground-truth NPS were taken as the sum of (1) a measurement in parenchymal regions of anatomy-subtracted (i.e., noise only) scans, and (2) a measurement in the same region of noise-free (pre-noise-insertion) images. To assess in vivo NPS performance, correlation of NPS average frequency ( f avg ), was reported. Sensitivity of accuracy [root-mean-square-error (RMSE)] to number of pixels included in measurement was conducted via bootstrapped pixel-dropout. Sensitivity of NPS to dose and reconstruction kernel was assessed to confirm that ground truth NPS similarities were maintained in patient-specific measurements. Results : Pearson and Spearman correlation coefficients 0.97 and 0.96 for f avg indicated good correlation. Results suggested accurate NPS measurements (within 5% total RMSE) could be acquired with ∼ 10 6    pixels . Conclusions : Relationships of similar NPS due to reconstruction kernel and dose were preserved between gold standard and observed in vivo estimations. The NPS estimation method was further deployed on clinical cases to demonstrate the feasibility of clinical analysis., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Purpose: To characterize variability in image quality and radiation dose across a large cohort of computed tomography (CT) examinations and identify the scan factors with the highest influence on the observed variabilities. Approach: This retrospective institutional-review-board-exempt investigation was performed on 87,629 chest and abdomen-pelvis CT scans acquired for 97 facilities from 2018 to 2019. Images were assessed in terms of noise, resolution, and dose metrics (global noise, frequency in which modulation transfer function is at 0.50, and volumetric CT dose index, respectively). The results were fit to linear mixed-effects models to quantify the variabilities as affected by scan parameters and settings and patient characteristics. A list of factors, ranked by t -value with p < 0.05 , was ascertained for each of the six mixed effects models. A type III p -value test was used to assess the influence of facility. Results: Across different facilities, image quality and dose were significantly different ( p < 0.05 ), with little correlation between their mean magnitudes and consistency (Pearson's correlation coefficient < 0.34 ). Scanner model, slice thickness, recon field-of-view and kernel, mAs, kVp, patient size, and centering were the most influential factors. The two body regions exhibited similar rankings of these factors for noise (Spearman's correlation coefficient = 0.76 ) and dose (Spearman's correlation coefficient = 0.86 ) but not for resolution (Spearman's correlation coefficient = 0.52 ). Conclusions: Clinical CT scans can vary in image quality and dose with broad implications for diagnostic utility and radiation burden. Average scan quality was not correlated with interpatient scan-quality consistency. For a given facility, this variability can be quite large, with magnitude differences across facilities. The knowledge of the most influential factors per body region may be used to better manage these variabilities within and across facilities., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA. Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index ( e ' ) as a task-based measure of image quality in cardiac CTA. The e ' index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA). Results: The e ' index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e ' threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training-test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective. Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images., (© 2021 The Authors.)

Purpose: To utilize a virtual clinical trial (VCT) construct to investigate the effects of beam collimation and pitch on image quality (IQ) in computed tomography (CT) under different respiratory and cardiac motion rates. Approach: A computational human model [extended cardiac-torso (XCAT) phantom] with added lung lesions was used to simulate seven different rates of cardiac and respiratory motions. A validated CT simulator (DukeSim) was used in this study. A supplemental validation was done to ensure the accuracy of DukeSim across different pitches and beam collimations. Each XCAT phantom was imaged using the CT simulator at multiple pitches (0.5 to 1.5) and beam collimations (19.2 to 57.6 mm) at a constant dose level. The images were compared against the ground truth using three task-generic IQ metrics in the lungs. Additionally, the bias and variability in radiomics (morphological) feature measurements were quantified for task-specific lung lesion quantification across the studied imaging conditions. Results: All task-generic metrics degraded by 1.6% to 13.3% with increasing pitch. When imaged with motion, increasing pitch reduced motion artifacts. The IQ slightly degraded (1.3%) with changes in the studied beam collimations. Patient motion exhibited negative effects (within 7%) on the IQ. Among all features across all imaging conditions studies, compactness2 and elongation showed the largest ( - 26.5 % , 7.8%) and smallest ( - 0.8 % , 2.7%) relative bias and variability. The radiomics results were robust across the motion profiles studied. Conclusions: While high pitch and large beam collimations can negatively affect the quality of CT images, they are desirable for fast imaging. Further, our results showed no major adverse effects in morphology quantification of lung lesions with the increase in pitch or beam collimation. VCTs, such as the one demonstrated in this study, represent a viable methodology for experiments in CT., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Texture is a key radiomics measurement for quantification of disease and disease progression. The sensitivity of the measurements to image acquisition, however, is uncertain. We assessed bias and variability of computed tomography (CT) texture feature measurements across many clinical image acquisition settings and reconstruction algorithms. Diverse, anatomically informed textures (texture A, B, and C) were simulated across 1188 clinically relevant CT imaging conditions representing four in-plane pixel sizes (0.4, 0.5, 0.7, and 0.9 mm), three slice thicknesses (0.625, 1.25, and 2.5 mm), three dose levels ( CTDI vol 1.90, 3.75, and 7.50 mGy), and 33 reconstruction kernels. Imaging conditions corresponded to noise and resolution properties representative of five commercial scanners (GE LightSpeed VCT, GE Discovery 750 HD, GE Revolution, Siemens Definition Flash, and Siemens Force) in filtered backprojection and iterative reconstruction. About 21 texture features were calculated and compared between the ground-truth phantom (i.e., preimaging) and its corresponding images. Each feature was measured with four unique volumes of interest (VOIs) sizes (244, 579, 1000, and 1953    mm 3 . To characterize the bias, the percentage relative difference [PRD(%)] in each feature was calculated between the imaged scenario and the ground truth for all VOI sizes. Feature variability was assessed in terms of (1)  σ PRD ( % ) indicating the variability between the ground truth and simulated image scenario based on the PRD(%), (2)  COV f indicating the simulation-based variability, and (3)  COV T indicating the natural variability present in the ground-truth phantom. The PRD ranged widely from - 97 % to 1220%, with an underlying variability ( σ ) of up to 241%. Features such as gray-level nonuniformity, texture entropy, sum average, and homogeneity exhibited low susceptibility to reconstruction kernel effects ( PRD < 3 % ) with relatively small σ PRD ( % ) ( ≤ 5 % ) across imaging conditions. The dynamic range of results indicates that image acquisition and reconstruction conditions of in-plane pixel sizes, slice thicknesses, dose levels, and reconstruction kernels can lead to significant bias and variability in feature measurements.

Anthropomorphic breast phantoms mimic patient anatomy in order to evaluate clinical mammography and digital breast tomosynthesis system performance. Our goal is to create a modular phantom with an anthropomorphic region to allow for improved lesion and calcification detection as well as a uniform region to evaluate standard quality control (QC) metrics. Previous versions of this phantom used commercial photopolymer inkjet three-dimensional printers to recreate breast anatomy using four surfaces that were fabricated with commercial materials spanning only a limited breast density range of 36% to 64%. We use modified printers to create voxelized, dithered breast phantoms with continuous gradations between glandular and adipose tissues. Moreover, the new phantom replicates the low-end density (representing adipose tissue) using third party material, Jf Flexible, and increases the high-end density to the density of glandular tissue and beyond by either doping Jf Flexible with salts and nanoparticles or using a new commercial resin, VeroPureWhite. An insert design is utilized to add masses, calcifications, and iodinated objects into the phantom for increased utility. The uniform chest wall region provides a space for traditional QC objects such as line pair patterns for measuring resolution and scale bars for measuring printer linearity. Incorporating these distinct design modules enables us to create an improved, complete breast phantom to better evaluate clinical mammography systems for lesion and calcification detection and standard QC performance evaluation.

We aimed to design and fabricate synthetic lung nodules with patient-informed internal heterogeneity to assess the variability and accuracy of measured texture features in CT. To that end, 190 lung nodules from a publicly available database of chest CT images (Lung Image Database Consortium) were selected based on size ( > 3    mm ) and malignancy. The texture features of the nodules were used to train a statistical texture synthesis model based on clustered lumpy background. The model parameters were ascertained based on a genetic optimization of a Mahalanobis distance objective function. The resulting texture model defined internal heterogeneity within 24 anthropomorphic lesion models which were subsequently fabricated into physical phantoms using a multimaterial three-dimensional (3-D) printer. The 3-D-printed lesions were imbedded in an anthropomorphic chest phantom and imaged with a clinical scanner using different acquisition parameters including slice thickness, dose level, and reconstruction kernel. The imaged lesions were analyzed in terms of texture features to ascertain the impact of CT imaging on lesion texture quantification. The texture modeling method produced lesion models with low and stable Mahalanobis distance between real and synthetic textures. The virtual lesions were successfully printed into 3-D phantoms. The accuracy and variability of the measured features extracted from the CT images of the phantoms showed notable influence from the imaging acquisition parameters with contrast, energy, and texture entropy exhibiting most sensitivity in terms of accuracy, and contrast, dissimilarity, and texture entropy most variability. Thinner slice thicknesses yielded more accurate and edge reconstruction kernels more stable results. We conclude that printed textured models of lesions can be developed using a method that can target and minimize the mathematical distance between real and synthetic lesions. The synthetic lesions can be used as the basis to investigate how CT imaging conditions might affect radiomics features derived from CT images.

We propose to characterize the bias and variability of quantitative morphology features of lung lesions across a range of computed tomography (CT) imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying three-dimensional blur and adding correlated noise based on the measured noise and resolution properties of five commercial multislice CT systems, representing three dose levels ( CTDI vol of 1.90, 3.75, 7.50 mGy), three slice thicknesses (0.625, 1.25, 2.5 mm), and 33 clinical reconstruction kernels from five clinical scanners. The images were segmented using three segmentation algorithms and each algorithm was evaluated by computing a Sørensen-Dice coefficient between the ground truth and the segmentation. A series of 21 shape-based morphology features were extracted from both "ground truth" (i.e., preblur without noise) and "image rendered" lesions (i.e., postblur and with noise). For each morphology feature, the bias was quantified by comparing the percentage relative error in the morphology metric between the imaged lesions and the ground-truth lesions. The variability was characterized by calculating the average coefficient of variation averaged across repeats and imaging conditions. The active contour segmentation had the highest average Dice coefficient of 0.80 followed by 0.63 for threshold, and 0.39 for fuzzy c-means. The bias of the features was segmentation algorithm and feature-dependent, with sharper kernels being less biased and smoother kernels being more biased in general. The feature variability from simulated images ranged from 0.30% to 10% for repeats of the same condition and from 0.74% to 25.3% for different lesions in the same spiculation class. In conclusion, the bias of morphology features is dependent on the acquisition protocol in combination with the segmentation algorithm used and the variability is primarily dependent on the segmentation algorithm.

The purpose of this study is to (1) develop metrics to characterize the regional anatomical complexity of the lungs, and (2) relate these metrics with lung nodule detection in chest CT. A free-scrolling reader-study with virtually inserted nodules (13 radiologists × 157 total nodules = 2041 responses) is used to characterize human detection performance. Metrics of complexity based on the local density and orientation of distracting vasculature are developed for two-dimensional (2-D) and three-dimensional (3-D) considerations of the image volume. Assessed characteristics included the distribution of 2-D/3-D vessel structures of differing orientation (dubbed "2-D/3-D and dot-like/line-like distractor indices"), contiguity of inserted nodules with local vasculature, mean local gray-level surrounding each nodule, the proportion of lung voxels to total voxels in each section, and 3-D distance of each nodule from the trachea bifurcation. A generalized linear mixed-effects statistical model is used to determine the influence of each these metrics on nodule detectability. In order of decreasing effect size: 3-D line-like distractor index, 2-D line-like distractor index, 2-D dot-like distractor index, local mean gray-level, contiguity with 2-D dots, lung area, and contiguity with 3-D lines all significantly affect detectability ( P < 0.05 ). These data demonstrate that local lung complexity degrades detection of lung nodules.

Using hybrid datasets consisting of patient-derived computed tomography (CT) images with digitally inserted computational tumors, we establish volumetric interchangeability between real and computational lung tumors in CT. Pathologically-confirmed malignancies from 30 thoracic patient cases from the RIDER database were modeled. Tumors were either isolated or attached to lung structures. Patient images were acquired on one of two CT scanner models (Lightspeed 16 or VCT; GE Healthcare) using standard chest protocol. Real tumors were segmented and used to inform the size and shape of simulated tumors. Simulated tumors developed in Duke Lesion Tool (Duke University) were inserted using a validated image-domain insertion program. Four readers performed volume measurements using three commercial segmentation tools. We compared the volume estimation performance of segmentation tools between real tumors in actual patient CT images and corresponding simulated tumors virtually inserted into the same patient images (i.e., hybrid datasets). Comparisons involved (1) direct assessment of measured volumes and the standard deviation between simulated and real tumors across readers and tools, respectively, (2) multivariate analysis, involving segmentation tools, readers, tumor shape, and attachment, and (3) effect of local tumor environment on volume measurement. Volume comparison showed consistent trends (9% volumetric difference) between real and simulated tumors across all segmentation tools, readers, shapes, and attachments. Across all cases, readers, and segmentation tools, an intraclass correlation coefficient = 0.99 indicates that simulated tumors correlated strongly with real tumors ( p = 0.95 ). In addition, the impact of the local tumor environment on tumor volume measurement was found to have a segmentation tool-related influence. Strong agreement between simulated tumors modeled in this study compared to their real counterparts suggests a high degree of similarity. This indicates that, volumetrically, simulated tumors embedded into patient CT data can serve as reasonable surrogates to real patient data.

This study's purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., in vivo ). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient's CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with [Formula: see text] HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model. This method was applied to CT data from a previous study in which diagnostic performance of 16 readers was measured for the task of detecting subtle, hypoattenuating liver lesions ([Formula: see text]), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. In vivo detectability indices were estimated and compared to the human readers' binary 2AFC outcomes using a generalized linear mixed-effects statistical model. The results of this modeling showed that the in vivo detectability indices were strongly related to 2AFC outcomes ([Formula: see text]). Linear comparison between human-detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlation coefficients exceeding 0.84. These results suggest the potential utility of using in vivo estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.

Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index ([Formula: see text]), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function. The nodule properties and segmentation algorithm were modeled in terms of a task function and a template function, respectively. The [Formula: see text] values were benchmarked against experimentally acquired precision values from an anthropomorphic chest phantom across 54 acquisition protocols, 2 nodule sizes, and 2 volume segmentation softwares. [Formula: see text] exhibited correlation with experimental precision across nodule sizes and acquisition protocols but dependence on segmentation software. Compared to the assessment of empirical precision, which required [Formula: see text] to perform the segmentation, the [Formula: see text] method required [Formula: see text] from data collection to mathematical computation. A mathematical modeling of volume quantification provides efficient prediction of quantitative performance. It establishes a method to verify quantitative compliance and to optimize clinical protocols for chest CT volumetry.

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Optimization is one of the key concepts of radiation protection in medical imaging. In practice, it involves compromising between the image quality and dose to the patient; the dose should not be higher than necessary to achieve an image quality (or diagnostic information) needed for the clinical task. Monitoring patient dose is a key requirement toward optimization. The concept of diagnostic reference level (DRL) was introduced by the International Commission on Radiological Protection as a practical tool for optimization. Unfortunately, this concept has not been applied consistently worldwide. To review the current strengths and weaknesses worldwide and to promote improvements, the International Atomic Energy Agency organized a Technical Meeting on patient dose monitoring and the use of DRLs on May 2016. This paper reports a summary of the findings and conclusions from the meeting. The strengths and weaknesses were generally different in less-developed countries compared with developed countries. Possible improvements were suggested in six areas: human resources and responsibilities, training, safety and quality culture, regulations, funding, and tools and methods. An overall conclusion was that radiation protection requires a patient-centric approach and a transfer from purely reactive to increasingly proactive optimization, whereby the best outcome is expected from good teamwork.

This study aimed to estimate the organ dose reduction potential for organ-dose-based tube current modulated (ODM) thoracic computed tomography (CT) with a wide dose reduction arc. Twenty-one computational anthropomorphic phantoms (XCAT) were used to create a virtual patient population with clinical anatomic variations. The phantoms were created based on patient images with normal anatomy (age range: 27 to 66 years, weight range: 52.0 to 105.8 kg). For each phantom, two breast tissue compositions were simulated: [Formula: see text] and [Formula: see text] (glandular-to-adipose ratio). A validated Monte Carlo program (PENELOPE, Universitat de Barcelona, Spain) was used to estimate the organ dose for standard tube current modulation (TCM) (SmartmA, GE Healthcare) and ODM (GE Healthcare) for a commercial CT scanner (Revolution, GE Healthcare) using a typical clinical thoracic CT protocol. Both organ dose and [Formula: see text]-to-organ dose conversion coefficients ([Formula: see text] factors) were compared between TCM and ODM. ODM significantly reduced all radiosensitive organ doses ([Formula: see text]). The breast dose was reduced by [Formula: see text]. For [Formula: see text] factors, organs in the anterior region (e.g., thyroid and stomach) exhibited substantial decreases, and the medial, distributed, and posterior region saw either an increase of less than 5% or no significant change. ODM significantly reduced organ doses especially for radiosensitive superficial anterior organs such as the breasts.

The purpose of this study was to formulate a systematic, evidence-based method to relate quantitative diagnostic performance to radiation dose, enabling a multidimensional system to optimize computed tomography imaging across pediatric populations. Based on two prior foundational studies, radiation dose was assessed in terms of organ doses, effective dose ([Formula: see text]), and risk index for 30 patients within nine color-coded pediatric age-size groups as a function of imaging parameters. The cases, supplemented with added noise and simulated lesions, were assessed in terms of nodule detection accuracy in an observer receiving operating characteristic study. The resulting continuous accuracy-dose relationships were used to optimize individual scan parameters. Before optimization, the nine protocols had a similar [Formula: see text] of [Formula: see text] with accuracy decreasing from 0.89 for the youngest patients to 0.67 for the oldest. After optimization, a consistent target accuracy of 0.83 was established for all patient categories with [Formula: see text] ranging from 1 to 10 mSv. Alternatively, isogradient operating points targeted a consistent ratio of accuracy-per-unit-dose across the patient categories. The developed model can be used to optimize individual scan parameters and provide for consistent diagnostic performance across the broad range of body sizes in children.

This guest editorial introduces the special section on Visions of Safety: Perspectives on Radiation Exposure and Risk in Medical Imaging.

The purpose of this study was to investigate relationships between patient attributes and organ dose for a population of computational phantoms for 20 tomosynthesis and radiography protocols. Organ dose was estimated from 54 adult computational phantoms (age: 18 to 78 years, weight 52 to 117 kg) using a validated Monte-Carlo simulation (PENELOPE) of a system capable of performing tomosynthesis and radiography. The geometry and field of view for each exam were modeled to match clinical protocols. For each protocol, the energy deposited in each organ was estimated by the simulations, converted to dose units, and then normalized by exposure in air. Dose to radiosensitive organs was studied as a function of average patient thickness in the region of interest and as a function of body mass index. For tomosynthesis, organ doses were also studied as a function of x-ray tube position. This work developed comprehensive information for organ dose dependencies across a range of tomosynthesis and radiography protocols. The results showed a protocol-dependent exponential decrease with an increasing patient size. There was a variability in organ dose across the patient population, which should be incorporated in the metrology of organ dose. The results can be used to prospectively and retrospectively estimate organ dose for tomosynthesis and radiography.

Currently, computed tomography (CT) dosimetry relies on surrogates for dose, such as CT dose index and size-specific dose estimates, rather than dose per se . Organ dose is considered as the gold standard for radiation dosimetry. However, organ dose estimation requires precise knowledge of organ locations. Regional imparted energy and dose can also be used to quantify radiation burden and are beneficial because they do not require knowledge of organ size or location. This work investigated an automated technique to retrospectively estimate the imparted energy from tube current-modulated (TCM) CT exams across 13 protocols. Monte Carlo simulations of various head and body TCM CT examinations across various tube potentials and TCM strengths were performed on 58 adult computational extended cardiac-torso phantoms to develop relationships between scanned mass and imparted energy normalized by dose length product. Results from the Monte Carlo simulations indicate that normalized imparted energy increases with increasing both scanned mass and tube potential, but it is relatively unaffected by the strength of the TCM. The automated algorithm was tested on 40 clinical datasets with a 98% success rate.

Although transmission-based x-ray imaging is the most commonly used imaging approach for breast cancer detection, it exhibits false negative rates higher than 15%. To improve cancer detection accuracy, x-ray coherent scatter computed tomography (CSCT) has been explored to potentially detect cancer with greater consistency. However, the 10-min scan duration of CSCT limits its possible clinical applications. The coded aperture coherent scatter spectral imaging (CACSSI) technique has been shown to reduce scan time through enabling single-angle imaging while providing high detection accuracy. Here, we use Monte Carlo simulations to test analytical optimization studies of the CACSSI technique, specifically for detecting cancer in ex vivo breast samples. An anthropomorphic breast tissue phantom was modeled, a CACSSI imaging system was virtually simulated to image the phantom, a diagnostic voxel classification algorithm was applied to all reconstructed voxels in the phantom, and receiver-operator characteristics analysis of the voxel classification was used to evaluate and characterize the imaging system for a range of parameters that have been optimized in a prior analytical study. The results indicate that CACSSI is able to identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) in tissue samples with a cancerous voxel identification area-under-the-curve of 0.94 through a scan lasting less than 10 s per slice. These results show that coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue within ex vivo samples. Furthermore, the results indicate potential CACSSI imaging system configurations for implementation in subsequent imaging development studies.

The purpose of this study was to compare computed tomography (CT) low-contrast detectability from human readers with observer model-based surrogates of image quality. A phantom with a range of low-contrast signals (five contrasts, three sizes) was imaged on a state-of-the-art CT scanner (Siemens' force). Images were reconstructed using filtered back projection and advanced modeled iterative reconstruction and were assessed by 11 readers using a two alternative forced choice method. Concurrently, contrast-to-noise ratio (CNR), area-weighted CNR (CNRA), and observer model-based metrics were estimated, including nonprewhitening (NPW) matched filter, NPW with eye filter (NPWE), NPW with internal noise, NPW with an eye filter and internal noise (NPWEi), channelized Hotelling observer (CHO), and CHO with internal noise (CHOi). The correlation coefficients (Pearson and Spearman), linear discriminator error, [Formula: see text], and magnitude of confidence intervals, [Formula: see text], were used to determine correlation, proper characterization of the reconstruction algorithms, and model precision, respectively. Pearson (Spearman) correlation was 0.36 (0.33), 0.83 (0.84), 0.84 (0.86), 0.86 (0.88), 0.86 (0.91), 0.88 (0.90), 0.85 (0.89), and 0.87 (0.84), [Formula: see text] was 0.25, 0.15, 0.2, 0.25, 0.3, 0.25, 0.4, and 0.45, and [Formula: see text] was [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for CNR, CNRA, NPW, NPWE, NPWi, NPWEi, CHO, and CHOi, respectively.

Dynamic perfusion imaging can provide the morphologic details of the scanned organs as well as the dynamic information of blood perfusion. However, due to the polyenergetic property of the x-ray spectra, beam hardening effect results in undesirable artifacts and inaccurate CT values. To address this problem, this study proposes a segmentation-free polyenergetic dynamic perfusion imaging algorithm (pDP) to provide superior perfusion imaging. Dynamic perfusion usually is composed of two phases, i.e., a precontrast phase and a postcontrast phase. In the precontrast phase, the attenuation properties of diverse base materials (e.g., in a thorax perfusion exam, base materials can include lung, fat, breast, soft tissue, bone, and metal implants) can be incorporated to reconstruct artifact-free precontrast images. If patient motions are negligible or can be corrected by registration, the precontrast images can then be employed as a priori information to derive linearized iodine projections from the postcontrast images. With the linearized iodine projections, iodine perfusion maps can be reconstructed directly without the influence of various influential factors, such as iodine location, patient size, x-ray spectrum, and background tissue type. A series of simulations were conducted on a dynamic iodine calibration phantom and a dynamic anthropomorphic thorax phantom to validate the proposed algorithm. The simulations with the dynamic iodine calibration phantom showed that the proposed algorithm could effectively eliminate the beam hardening effect and enable quantitative iodine map reconstruction across various influential factors. The error range of the iodine concentration factors ([Formula: see text]) was reduced from [Formula: see text] for filtered back-projection (FBP) to [Formula: see text] for pDP. The quantitative results of the simulations with the dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from [Formula: see text] for FBP to less than [Formula: see text] for pDP, which suggested that the proposed algorithm could not only effectively eliminate beam hardening artifacts but also significantly reduce the influence of the metal artifacts and accurately reconstruct the iodine map regardless of the influential factors. A segmentation-free polyenergetic dynamic perfusion imaging algorithm was proposed and validated via simulations. This method can accurately reconstruct artifact-free iodine maps for quantitative analyses.

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Contrast enhancement is a key component of computed tomography (CT) imaging and offers opportunities for optimization. The design and optimization of techniques, however, require orchestration with the scan parameters and, further, a methodology to relate contrast enhancement and injection function. We used such a methodology to develop a method, the analytical inverse method, to predict the required injection function to achieve a desired contrast enhancement in a given organ by incorporation of a physiologically based compartmental model. The method was evaluated across 32 different target contrast enhancement functions for aorta, kidney, stomach, small intestine, and liver. The results exhibited that the analytical inverse method offers accurate performance with error in the range of 10% deviation between the predicted and desired organ enhancement curves. However, this method is incapable of predicting the injection function based on the liver enhancement. The findings of this study can be useful in optimizing contrast medium injection function as well as scan timing to provide more consistency in the way contrast-enhanced CT examinations are performed. To our knowledge, this work is one of the first attempts to predict the contrast material injection function for a desired organ enhancement curve.

A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice.