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To provide our oncology-specific adult abdominal-pelvic CT reference levels for image noise and radiation dose from a high-volume, oncologic, tertiary referral center.The portal venous phase abdomen-pelvis acquisition was assessed for image noise and radiation dose in 13,320 contrast-enhanced CT examinations. Patient size (effective diameter) and radiation dose (CTDIThe noise reference level was 11.25 HU with a reference range of 10.25-12.25 HU. The dose reference level at a median effective diameter of 30.7 cm was 26.7 mGy with a reference range of 19.6-37.0 mGy. Dose increased with patient size; however, image noise remained approximately constant within the noise reference range. The doses were 2.1-2.5 times than the doses in the ACR DIR registry for corresponding patient sizes. The image noise was 0.63-0.75 times the previously published reference level in abdominal-pelvic CT examinations.Our oncology-specific abdominal-pelvic CT dose reference levels are higher than in the ACR dose index registry and our oncology-specific image noise reference levels are lower than previously proposed image noise reference levels.This study reports reference image noise and radiation dose levels appropriate for the indication of abdomen-pelvis CT examination for cancer diagnosis and staging. The difference in these reference levels from non-oncology-specific CT examinations highlight a need for indication-specific, dose index and image quality reference registries.

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 (h(n)) were estimated by normalizing organ dose estimates to air kerma at 70 cm (X(70cm)) 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 (K(n)) 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 (h(n)) containing 294 distinct combinations of patients and exam protocols was developed and made publicly available. The values of K(n) 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 = K(n)/K(n,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.

BACKGROUND: Navigation guidance in cardiac interventions is provided by X-ray angiography. Cumulative radiation exposure is a serious concern for pediatric cardiac interventions. PURPOSE: A generative learning-based approach is proposed to predict X-ray angiography frames to reduce the radiation exposure for pediatric cardiac interventions while preserving the image quality. METHODS: Frame predictions are based on a model-free motion estimation approach using a long short-term memory architecture and a content predictor using a convolutional neural network structure. The presented model thus estimates contrast-enhanced vascular structures such as the coronary arteries and their motion in X-ray sequences in an end-to-end system. This work was validated with 56 simulated and 52 patients’ X-ray angiography sequences. RESULTS: Using the predicted images can reduce the number of pulses by up to three new frames without affecting the image quality. The average required acquisition can drop by 30% per second for a 15 fps acquisition. The average structural similarity index measurement was 97% for the simulated dataset and 82% for the patients’ dataset. CONCLUSIONS: Frame prediction using a learning-based method is promising for minimizing radiation dose exposure. The required pulse rate is reduced while preserving the frame rate and the image quality. With proper integration in X-ray angiography systems, this method can pave the way for improved dose management.

Shoulder computed tomography (CT) is commonly utilized in preoperative planning for total shoulder arthroplasty. Conventional-dose shoulder CT may expose patients to more ionizing radiation than is necessary to provide high-quality images for this procedure. The purpose of this study was to evaluate the utility of simulated low-dose CT images for preoperative planning using manual measurements and common preoperative planning software.Eighteen shoulder CT scans obtained for preoperative arthroplasty planning were used to generate CT images as if they had been acquired at reduced radiation dose (RD) levels of 75%, 50%, and 25% using a simulation technique that mimics decreased x-ray tube current. This technique was validated by quantitative comparison of simulated low-dose scans of a cadaver with actual low-dose scans. Glenoid version, glenoid inclination, and humeral head subluxation were measured using 2 commercially available software platforms and were also measured manually by 3 physicians. These measurements were then analyzed for agreement across RD levels for each patient. Tolerances of 5° of glenoid version, 5° of glenoid inclination, and 10% humeral head subluxation were used as equivalent for preoperative planning purposes.At all RD levels evaluated, the preoperative planning software successfully segmented the CT images. Semiautomated software measurement of 25% RD images was within tolerances in 99.1% of measurements; for 50% RD images, within tolerances in 96.3% of measurements; and for 75% RD images, within tolerances in 100% of measurements. Manual measurements of 25% RD images were within these tolerances in 95.1% of measurements; for 50% RD images, in 98.8% of measurements; and for 75% RD images, in 99.4% of measurements.Simulated low-dose CT images were sufficient for reliable measurement of glenoid version, glenoid inclination, and humeral head subluxation by preoperative planning software as well as by physician-observers. These findings suggest the potential for substantial reduction in RD in preoperative shoulder CT scans without compromising surgical planning.The adoption of low-dose techniques in preoperative shoulder CT may lower radiation exposure for patients undergoing shoulder arthroplasty, without compromising image quality.

PURPOSE: Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver. METHODS: Intra-organ contrast perfusion depends on the organ’s vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood–contrast mixture was compared to clinical data. RESULTS: The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves (TCC). Measurements of simulated CT ROIs were found to agree with clinically-observed values of early arterial phase contrast enhancement of the parenchyma ([Formula: see text] HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement [Formula: see text] HU). CONCLUSIONS: The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.

The purpose of this work was to characterize and improve the ability of fused filament fabrication to create anthropomorphic physical phantoms for CT research. Specifically, we sought to develop the ability to create multiple levels of X-ray attenuation with a single material.CT images of 3D printed cylinders with different infill angles and printing patterns were assessed by comparing their 2D noise power spectra to determine the conditions that produced minimal and uniform noise. A backfilling approach in which additional polymer was extruded into an existing 3D printed background layer was developed to create multiple levels of image contrast.A print with nine infill angles and a rectilinear infill pattern was found to have the best uniformity, but the printed objects were not as uniform as a commercial phantom. An HU dynamic range of 600 was achieved by changing the infill percentage from 40% to 100%. The backfilling technique enabled control of up to eight levels of contrast within one object across a range of 200 HU, similar to the range of soft tissue. A contrast detail phantom with six levels of contrast and an anthropomorphic liver phantom with four levels of contrast were printed with a single material.This work improves the uniformity and levels of contrast that can be achieved with fused filament fabrication, thereby enabling researchers to easily create more detailed physical phantoms, including realistic, anthropomorphic textures.

Inspired by the principles of Medical Physics 3.0, this paper frames a new model of clinical physics practice, anchored to clinical realities, clinical priorities, and advanced physics. The model is based on the conviction that the physicist is vested with the expertise and the responsibility to ensure each patient gets the optimum imaging exam towards the best clinical outcome. Key expectations and activities are encapsulated into 12 areas: scientific perspective, quality and safety assurance, regulatory compliance, technology assessment, use optimization, performance monitoring, technology acquisition, technology commissioning, vendor cooperation, translational practice, research consultancy, and technology education. The paper further highlights key challenges to effective clinical physics practice of increased scope of competency, balancing rigor and relevance, managing metrological surrogates of quality and safety, and integrating principle- and data-informed approaches. Mindful to practically mitigate these challenges, clinical imaging physics can play an essential role to enable evidence-based imaging care.

Medical Physics 3.0 is a grassroots movement within the medical physics community to re-envision a new commitment and engagement of physics in the care process. In this model, a physicist, present in either the clinic or the laboratory, is to practice physics for the direct benefit of the patient, either immediately in the clinical setting or eventually through research. As a demonstration of this shift of focus, this article offers a framework by which medical physics can better serve the rigor and quality of radiological practice. This is accomplished through (1) patient-centered metrics of imaging dose and quality that are generalizable across and relatable to varying tasks; (2) prospectively prescribing protocols and dose based on needed quality; and (3) retrospectively monitoring and targeting dose and quality across practice. The article also suggests a model for this opportunity in which the current "consultant" physicist is vested with the responsibility to fully contribute his or her essential expertise in the care of individual patients.

Background Diagnostic reference levels (DRLs) and achievable doses (ADs) were developed for the 10 most commonly performed pediatric CT examinations in the United States using the American College of Radiology Dose Index Registry. Purpose To develop robust, current, national DRLs and ADs for the 10 most commonly performed pediatric CT examinations as a function of patient age and size. Materials and Methods Data on 10 pediatric (ie, patients aged 18 years and younger) CT examinations performed between 2016 and 2020 at 1625 facilities were analyzed. For head and neck examinations, dose indexes were analyzed based on patient age; for body examinations, dose indexes were analyzed for patient age and effective diameter. Data from 1 543 535 examinations provided medians for AD and 75th percentiles for DRLs for volume CT dose index (CTDI

Objective. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. Approach. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. Main results. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. Significance. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions.

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.

Respiratory X-ray imaging enhanced by phase contrast has shown improved airway visualization in animal models. Limitations in current X-ray technology have nevertheless hindered clinical translation, leaving the potential clinical impact an open question. Here, we explore phase-contrast chest radiography in a realistic in silico framework. Specifically, we use preprocessed virtual patients to generate in silico chest radiographs by Fresnel-diffraction simulations of X-ray wave propagation. Following a reader study conducted with clinical radiologists, we predict that phase-contrast edge enhancement will have a negligible impact on improving solitary pulmonary nodule detection (6 to 20 mm). However, edge enhancement of bronchial walls visualizes small airways (< 2 mm), which are invisible in conventional radiography. Our results show that phase-contrast chest radiography could play a future role in observing small-airway obstruction (e.g., relevant for asthma or early-stage chronic obstructive pulmonary disease), which cannot be directly visualized using current clinical methods, thereby motivating the experimental development needed for clinical translation. Finally, we discuss quantitative requirements on distances and X-ray source/detector specifications for clinical implementation of phase-contrast chest radiography.

PURPOSE: To determine whether photon-counting CT (PCCT) acquisition of whole-body CT images provides similar quantitative image quality and reader satisfaction for multiple myeloma screening at lower radiation doses than does standard energy-integrating detector (EID) CT. MATERIALS AND METHODS: Patients with monoclonal gammopathy of undetermined significance prospectively underwent clinical noncontrast whole-body CT with EID and same-day PCCT (August–December 2021). Five axial scan locations were evaluated by seven radiologists, with 11% (eight of 70) of images including osteolytic lesions. Images were shown in randomized order, and each reader rated the following: discernibility of the osseous cortex and osseous trabeculae, perceived image noise level, and diagnostic confidence. Presence of lytic osseous lesions was indicated. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated. Comparisons were made using paired t tests and mixed linear effects models. RESULTS: Seven participants (four women) were included (mean age, 66 years ± 9 [SD]; body mass index, 30.1 kg/m(2 )± 5.2). Mean cortical definition, trabecular definition, image noise, and image quality scores were 83, 67, 75, and 78 versus 84, 66, 74, and 76 for EID and PCCT, respectively (P = .65, .11, .26, and .11, respectively). PCCT helped identify more lesions (79% [22 of 28]) than did EID (64% [18 of 28]). CNRs and SNRs were similar between modalities. PCCT had lower radiation doses than EID (volume CT dose index: EID, 11.37 ± 2.8 vs PCCT, 1.8 ± 0.6 [P = .06]; dose-length product: EID, 1654.1 ± 409.6 vs PCCT, 253.4 ± 89.6 [P = .05]). CONCLUSION: This pilot investigation suggests that PCCT affords similar quantitative and qualitative scores as EID at significantly lower radiation doses. Keywords: CT, CT-Spectral, Skeletal-Axial, Spine, Hematologic Diseases, Whole-Body Imaging, Comparative Studies Supplemental material is available for this article. © RSNA, 2022

A novel routine dual‐energy computed tomography (DECT) quality control (QC) program was developed to address the current deficiency of routine QC for this technology. The dual‐energy quality control (DEQC) program features (1) a practical phantom with clinically relevant materials and concentrations, (2) a clinically relevant acquisition, reconstruction, and postprocessing protocol, and (3) a fully automated analysis software to extract quantitative data for database storage and trend analysis. The phantom, designed for easy set up for standalone or adjacent imaging next to the ACR phantom, was made in collaboration with an industry partner and informed by clinical needs to have four iodine inserts (0.5, 1, 2, and 5 mg/ml) and one calcium insert (100 mg/ml) equally spaced in a cylindrical water‐equivalent background. The imaging protocol was based on a clinical DECT abdominal protocol capable of producing material specific concentration maps, virtual unenhanced images, and virtual monochromatic images. The QC automated analysis software uses open‐source technologies which integrates well with our current automated CT QC database. The QC program was tested on a GE 750 HD scanner and two Siemens SOMATOM FLASH scanners over a 3‐month period. The automated algorithm correctly identified the appropriate region of interest (ROI) locations and stores measured values in a database for monitoring and trend analysis. Slight variations in protocol settings were noted based on manufacturer. Overall, the project proved to provide a convenient and dependable clinical tool for routine oversight of DE CT imaging within the clinic.