Spectral µCT with an energy resolving and interpolating pixel detector.

A main challenge in x-ray µCT with laboratory radiation derives from the broad spectral content, which in contrast to monochromatic synchrotron radiation gives rise to reconstruction artifacts and impedes quantitative reconstruction. Due to the low spectral brightness of these sources, monochromatization is unfavorable and parallel recording of a broad bandpath is practically indispensable. While conventional CT sums up all spectral components into a single detector value, spectral CT discriminates the data in several spectral bins.

A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

Purpose: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities.

Methods: We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed.