Direct Pathology Detection and Characterization from MR K-Space Data Using Deep Learning

Abstract

Present MRI diagnosis comprises two steps: (i) reconstruction of multi-slice 2D or 3D images from k-space data; and (ii) pathology identification from images. In this study, we propose a strategy of direct pathology detection and characterization from MR k-space data through deep learning. This concept bypasses the traditional MR image reconstruction prior to pathology diagnosis, and presents an alternative MR diagnostic paradigm that may lead to potentially more powerful new tools for automatic and effective pathology screening, detection and characterization. Our simulation results demonstrated that this image-free strategy could detect brain tumors and their sizes/locations with high sensitivity and specificity.