Abstract #3101

B0 Inhomogeneity Distortion Corrected Image Reconstruction with Deep Learning on An Open Bore MRI-Linac

Shanshan Shan^1,2, Yang Gao^3,4, Meng Ma⁵, Hongping Gan⁵, David Waddington², Brendan Whelan², Paul Liu², Chunyi Liu¹, Mingyuan Gao¹, and Feng Liu⁴

¹Center for Molecular Imaging and Nuclear Medicine, State Key Laboratory of Radiation Medicine and Protection,School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou, China, ²ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia, ³School of Computer Science and Engineering, Central South University, Changsha, China, ⁴School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia, ⁵School of Software, Northwestern Polytechnical University, Suzhou, China

Synopsis

Keywords: Image Reconstruction, BrainMRI-Linac systems require real-time anatomical images with high geometric fidelity to localize and track tumours during radiotherapy treatments. Image distortions caused by B0 field inhomogeneity and slow MR acquisition hinder the application of real-time MRI-guided radiotherapy. Here, we develop and investigate a deep learning-based reconstruction pipeline to reconstruct B0 inhomogeneity distortion-corrected images (B0ReconNet) directly from k-space. MR acceleration techniques such as compressed sensing (CS) were integrated into B0ReconNet to further reduce acquisition time. Simulated and experimental data with fully sampled and retrospectively subsampled acquisitions on a 1T open bore MRI-Linac were used to validate the proposed method.

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