Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging.

Background And Purpose: Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images.

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis.

(1) Background: Segmentation of the bladder inner's wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function. This paper proposes an optimized system for the segmentation and the classification of the bladder wall. (2) Methods: For each image of our data set, the region of interest corresponding to the bladder wall was extracted using LevelSet contour-based segmentation.

Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis.

Purpose: To describe the creation process of a new breast phantom specifically designed to monitor quality control (QC) metrics consistency over several months in digital breast tomosynthesis (DBT).

Methods: The semi-anthropomorphic Tomomam phantom was designed and evaluated twice monthly on a single Hologic Selenia Dimensions unit over 5 months. The phantom is manufactured in a one-piece epoxy resin homogeneous material as the basis for manufacturing, simulating breast tissue as 50% equivalent glandular (GL)/50% equivalent adipose (AD) and compressed thickness of 60 mm.