Digital wrist tomosynthesis (DWT)-based finite element analysis of ultra-distal radius differentiates patients with and without a history of osteoporotic fracture.

Despite effective therapies for those at risk of osteoporotic fracture, low adherence to screening guidelines and limited accuracy of bone mineral density (BMD) in predicting fracture risk preclude identification of those at risk. Because of high adherence to routine mammography, bone health screening at the time of mammography using a digital breast tomosynthesis (DBT) scanner has been suggested as a potential solution. BMD and bone microstructure can be measured from the wrist using a DBT scanner. However, the extent to which biomechanical variables can be derived from digital wrist tomosynthesis (DWT) has not been explored. Accordingly, we measured stiffness from a DWT based finite element (DWT-FE) model of the ultra-distal (UD) radius and ulna, and correlate these to reference microcomputed tomography image based FE (µCT-FE) from five cadaveric forearms. Further, this method is implemented to determine in vivo reproducibility of FE derived stiffness of UD radius and demonstrate the in vivo utility of DWT-FE in bone quality assessment by comparing two groups of postmenopausal women with and without a history of an osteoporotic fracture (Fx; n = 15, NFx; n = 51). Stiffness obtained from DWT and µCT had a strong correlation (R2 = 0.87, p < 0.001). In vivo repeatability error was <5 %. The NFx and Fx groups were not significantly different in DXA derived minimum T-scores (p > 0.3), but stiffness of the UD radius was lower for the Fx group (p < 0.007). Logistic regression models of fracture status with stiffness of the nondominant arm as the predictor were significant (p < 0.01). In conclusion this study demonstrates the feasibility of fracture risk assessment in mammography settings using DWT imaging and FE modeling in vivo. Using this approach, bone and breast screening can be performed in a single visit, with the potential to improve both the prevalence of bone health screening and the accuracy of fracture risk assessment.

Variations in signal-to-noise characteristics of tissue-equivalent attenuators for mammographic automatic exposure control system performance evaluation.

PURPOSE: This work investigates the impact of tissue-equivalent attenuator choice on measured signal-to-noise ratio (SNR) for automatic exposure control (AEC) performance evaluation in digital mammography. It also investigates how the SNR changes for each material when used to evaluate AEC performance across different mammography systems. METHODS: AEC performance was evaluated for four mammography systems using seven attenuator sets at two thicknesses (4 and 8 cm). All systems were evaluated in 2D imaging mode, and one system was evaluated in digital breast tomosynthesis (DBT) mode. The methodology followed the 2018 ACR digital mammography quality control (DMQC) manual. Each system-attenuator-thickness combination was evaluated using For Processing images in ImageJ with standard ROI size and location. The closest annual physicist testing results were used to explore the impact of varying measured AEC performance on image quality. RESULTS: The measured SNR varied by 44%-54% within each system across all attenuators at 4 cm thickness in 2D mode. The variation appeared to be largely due to changes in measured noise, with variations of 46%-67% within each system across all attenuators at 4 cm thickness in 2D mode. Two systems had failing SNR levels for two of the materials using the minimum SNR criterion specified in the ACR DMQC manual. Similar trends were seen in DBT mode and at 8 cm thickness. Within each material, there was 115%-131% variation at 4 cm and 82%-114% variation at 8 cm in the measured SNR across the four imaging systems. Variation in SNR did not correlate with system operating level based on visual image quality and average glandular dose (AGD). CONCLUSION: Choice of tissue-equivalent attenuator for AEC performance evaluation affects measured SNR values. Depending on the material, the difference may be enough to result in failure following the longitudinal and absolute thresholds specified in the ACR DMQC manual.

Estimated size of the clinical medical imaging physics workforce in the United States.

There is no current authoritative accounting of the number of clinical imaging physicists practicing in the United States. Information about the workforce is needed to inform future efforts to secure training pathways and opportunities. In this study, the AAPM Diagnostic Demand and Supply Projection Working Group collected lists of medical physicists from several state registration and licensure programs and the Conference of Radiation Control Program Directors (CRCPD) registry. By cross-referencing individuals among these lists, we were able to estimate the current imaging physics workforce in the United States by extrapolating based on population. The imaging physics workforce in the United States in 2019 consisted of approximately 1794 physicists supporting diagnostic X-ray (1073 board-certified) and 934 physicists supporting nuclear medicine (460 board-certified), with a number of individuals practicing in both subfields. There were an estimated 235 physicists supporting nuclear medicine exclusively (150 board-certified). The estimated total workforce, accounting for overlap, was 2029 medical physicists. These estimates are in approximate agreement with other published studies of segments of the workforce.

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.

Practical application of AAPM Report 270 in display quality assurance: A report of Task Group 270.

Published in January 2019, AAPM Report 270 provides an update to the recommendations of the AAPM's "TG18" report. Report 270 provides new definitions of display types, updated testing patterns, and revised performance standards for the modern, flat-panel displays used as part of medical image acquisition and review. The focus of the AAPM report is on consistent image quality and appearance, and how to establish a quality assurance program to achieve those two goals. This work highlights some of the key takeaways of AAPM Report 270 and makes comparisons with existing recommendations from other references. It also provides guidance for establishing a display quality assurance program for different-sized institutions. Finally, it describes future challenges for display quality assurance and what work remains.