Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors

TL;DR

This paper introduces DeepMRIRec, a deep learning method that accelerates MRI scans for pediatric brain tumor radiotherapy planning by reducing scan time fourfold while maintaining high image quality.

Contribution

The study presents a novel deep learning-based MRI reconstruction technique tailored for radiotherapy planning, addressing challenges specific to RT imaging that previous methods overlooked.

Findings

DeepMRIRec reduces MRI scan time by a factor of four.

It achieves a higher structural similarity score (0.960) compared to existing methods.

The approach is effective for pediatric brain tumor MRI data.

Abstract

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic and radiotherapy (RT) planning tool, offering detailed insights into the anatomy of the human body. The extensive scan time is stressful for patients, who must remain motionless in a prolonged imaging procedure that prioritizes reduction of imaging artifacts. This is challenging for pediatric patients who may require measures for managing voluntary motions such as anesthesia. Several computational approaches reduce scan time (fast MRI), by recording fewer measurements and digitally recovering full information via post-acquisition reconstruction. However, most fast MRI approaches were developed for diagnostic imaging, without addressing reconstruction challenges specific to RT planning. In this work, we developed a deep learning-based method (DeepMRIRec) for MRI reconstruction from undersampled data acquired with RT-specific receiver coil arrangements. We evaluated our method against fully sampled data of T1-weighted MR images acquired from 73 children with brain tumors/surgical beds using loop and posterior coils (12 channels), with and without applying virtual compression of coil elements. DeepMRIRec reduced scanning time by a factor of four producing a structural similarity score surpassing the evaluated state-of-the-art method (0.960 vs 0.896), thereby demonstrating its potential for accelerating MRI scanning for RT planning.