Meeting Banner
Abstract #3530

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

Linfang Xiao1,2, Yilong Liu1,2, Peiheng Zeng1,2, Mengye Lyu1,2, Xiaodong Ma1,2, Alex T. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

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.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here