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

Bone health assessment via digital wrist tomosynthesis in the mammography setting

Bone fractures attributable to osteoporosis are a significant problem. Though preventative treatment options are available for individuals who are at risk of a fracture, a substantial number of these individuals are not identified due to lack of adherence to bone screening recommendations. The issue is further complicated as standard diagnosis of osteoporosis is based on bone mineral density (BMD) derived from dual energy x-ray absorptiometry (DXA), which, while helpful in identifying many at risk, is limited in fully predicting risk of fracture. It is reasonable to expect that bone screening would become more prevalent and efficacious if offered in coordination with digital breast tomosynthesis (DBT) exams, provided that osteoporosis can be assessed using a DBT modality. Therefore, the objective of the current study was to explore the feasibility of using digital tomosynthesis imaging in a mammography setting. To this end, we measured density, cortical thickness and microstructural properties of the wrist bone, correlated these to reference measurements from microcomputed tomography and DXA, demonstrated the application in vivo in a small group of participants, and determined the repeatability of the measurements. We found that measurements from digital wrist tomosynthesis (DWT) imaging with a DBT scanner were highly repeatable ex vivo (error = 0.05%-9.62%) and in vivo (error = 0.06%-10.2%). In ex vivo trials, DWT derived BMDs were strongly correlated with reference measurements (R = 0.841-0.980), as were cortical thickness measured at lateral and medial cortices (R = 0.991 and R = 0.959, respectively) and the majority of microstructural measures (R = 0.736-0.991). The measurements were quick and tolerated by human patients with no discomfort, and appeared to be different between young and old participants in a preliminary comparison. In conclusion, DWT is feasible in a mammography setting, and informative on bone mass, cortical thickness, and microstructural qualities that are known to deteriorate in osteoporosis. To our knowledge, this study represents the first application of DBT for imaging bone. Future clinical studies are needed to further establish the efficacy for diagnosing osteoporosis and predicting risk of fragility fracture using DWT

Genetic dissection of EphA receptor signaling dynamics during retinotopic mapping.

Retinal ganglion cells (RGCs) project axons from their cell bodies in the eye to targets in the superior colliculus of the midbrain. The wiring of these axons to their synaptic targets creates an ordered representation, or "map," of retinal space within the brain. Many lines of experiments have demonstrated that the development of this map requires complementary gradients of EphA receptor tyrosine kinases and their ephrin-A ligands, yet basic features of EphA signaling during mapping remain to be resolved. These include the individual roles played by the multiple EphA receptors that make up the retinal EphA gradient. We have developed a set of ratiometric "relative signaling" (RS) rules that quantitatively predict how the composite low-nasal-to-high-temporal EphA gradient is translated into topographic order among RGCs. A key feature of these rules is that the component receptors of the gradient--in the mouse, EphA4, EphA5, and EphA6--must be functionally equivalent and interchangeable. To test this aspect of the model, we generated compound mutant mice in which the periodicity, slope, and receptor composition of the gradient are systematically altered with respect to the levels of EphA4, EphA5, and a closely related receptor, EphA3, that we ectopically express. Analysis of the retinotopic maps of these new mouse mutants establishes the general utility of the RS rules for predicting retinocollicular topography, and demonstrates that individual EphA gene products are approximately equivalent with respect to axon guidance and target selection.journal articleresearch support, n.i.h., extramuralresearch support, non-u.s. gov't2011 Jul 13importe

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\u27s 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

Anomalous scaling law for noise variance and spatial resolution in differential phase contrast computed tomography

In conventional absorption based x-ray computed tomography (CT), the noise variance in reconstructed CT images scales with spatial resolution following an inverse cubic relationship. Without reconstruction, in x-ray absorption radiography, the noise variance scales as an inverse square with spatial resolution. In this letter we report that while the inverse square relationship holds for differential phase contrast projection imaging, there exists an anomalous scaling law in differential phase contrast CT, where the noise variance scales with spatial resolution following an inverse linear relationship. The anomalous scaling law is theoretically derived and subsequently validated with phantom results from an experimental Talbot-Lau interferometer system

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

Calibrating from Within: Multipoint Internal Calibration of a Quantitative Mass Spectrometric Assay of Serum Methotrexate.

BACKGROUND:Clinical LC-MS/MS assays traditionally require that samples be run in batches with calibration curves in each batch. This approach is inefficient and presents a barrier to random access analysis. We developed an alternative approach called multipoint internal calibration (MPIC) that eliminated the need for batch-mode analysis. METHODS:The new approach used 4 variants of 13C-labeled methotrexate (0.026-10.3 µM) as an internal calibration curve within each sample. One site carried out a comprehensive validation, which included an evaluation of interferences and matrix effects, lower limit of quantification (LLOQ), and 20-day precision. Three sites evaluated assay precision and linearity. MPIC was also compared with traditional LC-MS/MS and an immunoassay. RESULTS:Recovery of spiked analyte was 93%-102%. The LLOQ was validated to be 0.017 µM. Total variability, determined in a 20-day experiment, was 11.5%CV. In a 5-day variability study performed at each site, total imprecision was 3.4 to 16.8%CV. Linearity was validated throughout the calibrator range (r2 > 0.995, slopes = 0.996-1.01). In comparing 40 samples run in each laboratory, the median interlaboratory imprecision was 6.55%CV. MPIC quantification was comparable to both traditional LC-MS/MS and immunoassay (r2 = 0.96-0.98, slopes = 1.04-1.06). Bland-Altman analysis of all comparisons showed biases rarely exceeding 20% when MTX concentrations were >0.4 µM. CONCLUSION:The MPIC method for serum methotrexate quantification was validated in a multisite proof-of-concept study and represents a big step toward random-access LC-MS/MS analysis, which could change the paradigm of mass spectrometry in the clinical laboratory

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis