Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra.

Purpose: To design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging.

Materials: We realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer.

A deep learning classifier for digital breast tomosynthesis.

Purpose: To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN).

Materials And Methods: Three DCNN architectures working at image-level (DBT slice) were compared: two state-of-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments.

[High resolution computerized tomography: dose evaluation with an anthropomorphic phantom].

High resolution Computed Tomography (HRCT), a technique for pulmonary parenchyma studies, has been for a long time performed with only few scans with a large interval in between: the reasons were the supposed increase in the dose to the patient after kVp and mAs increase, which is necessary to reduce background noise, and the characteristic distribution of most diffuse lung diseases. Of late, HRCT skin dose has been demonstrated to be markedly lower than that of conventional CT (8/10 mm contiguous slices). To evaluate organ doses to patients during HRCT, we compared conventional CT (10 mm slices), HRCT (1.