Distortion-corrected image reconstruction with deep learning on an MRI-Linac.

ABSTRACT:

Purpose

MRI is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for MR-guided radiotherapy applications.

Methods

We leverage recent advances in interpretable unrolling networks to develop a Distortion-Corrected Reconstruction Network (DCReconNet) that applies convolutional neural networks (CNNs) to learn effective regul

SUBMITTER: Shan S 

PROVIDER: S-EPMC10860740 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature