  Purpose: To evaluate the quality of deep learning reconstruction for
prospectively accelerated intraoperative magnetic resonance imaging (iMRI)
during resective brain tumor surgery.
  Materials and Methods: Accelerated iMRI was performed during brain surgery
using dual surface coils positioned around the area of resection. A deep
learning (DL) model was trained on the fastMRI neuro dataset to mimic the data
from the iMRI protocol. Evaluation was performed on imaging material from 40
patients imaged between 01.11.2021 - 01.06.2023 that underwent iMRI during
tumor resection surgery. A comparative analysis was conducted between the
conventional compressed sense (CS) method and the trained DL reconstruction
method. Blinded evaluation of multiple image quality metrics was performed by
two working neuro-radiologists and a working neurosurgeon on a 1 to 5 Likert
scale (1=non diagnostic, 2=poor, 3=acceptable, 4=good, 5=excellent), and the
favored reconstruction variant.
  Results: The DL reconstruction was strongly favored or favored over the CS
reconstruction for 33/40, 39/40, and 8/40 of cases for reader 1, 2, and 3,
respectively. Two of three readers consistently assigned higher ratings for the
DL reconstructions, and the DL reconstructions had a higher score than their
respective CS counterparts for 72%, 72%, and 14% of the cases for reader 1, 2,
and 3, respectively. Still, the DL reconstructions exhibited shortcomings such
as a striping artifact and reduced signal.
  Conclusion: DL shows promise to allow for high-quality reconstructions of
intraoperative MRI with equal to or improved perceived spatial resolution,
signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and
spatial resolution compared to compressed sense.
