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

The creation of a large set of realistic synthetic microcalcification clusters for simulation in (contrastenhanced) mammography images.

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Persistent median artery (PMA) thrombosis is a rare cause of carpal tunnel syndrome (CTS) with only a few cases reported in the literature. The bifid median nerve (BMN) is often associated with PMA and may be a factor in the development of CTS. There is a paucity of information in the literature on the management options for CTS secondary to PMA thrombosis. This paper presents two cases of CTS with associated PMA thrombosis and BMN and offers a discussion on diagnostic and management options.

. Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.

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This work proposes an objective and automated procedure to obtain realistic digital breast tomosynthesis (DBT) images for virtual clinical trials (VCT) using the hybrid approach (simulating lesions in acquired patient images). Based on extensive feedback from a radiologist, we have implemented an automatic selection of an appropriate insertion position that (1) is located in the interior region of the breast, (2) contains sufficient glandular tissue, and (3) has the lowest variance to cope with the presence of prominent background structure and contains no Sobel edge detected blood vessels. Next, the lesion is rotated to align with the breast structures using the histogram of oriented gradient feature descriptor. The spicules of the lesion are extended to improve the fine details of the manually segmented mass models. To reduce the pronounced shadow artefact surrounding the mass, the lesion template is modified with a fitted 2D gaussian to create a softer transition between background and lesion. The realism of 20 simulated lesions using the established automated procedure was scored; 70% of the simulated cases received at least a realism score of 4 out of 5. This means an improvement in realism compared to when lesions without processing are inserted at random locations in the patient background images. Additionally, the automated method eliminates the dependency on the researcher performing the VCT.

No abstract