  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.
