  A dataset of 3D-GRE and 3D-TSE brain 3T post contrast T1-weighted images as
part of a quality improvement project were collected and shown to five
neuro-radiologists who evaluated each sequence for both image quality and
imaging artifacts. The same scans were processed using the MRQy tool for
objective, quantitative image quality metrics. Using the combined radiologist
and quantitative metrics dataset, a decision tree classifier with a bagging
ensemble approach was trained to predict radiologist assessment using the
quantitative metrics. A machine learning model was developed for the following
three tasks: (1) determine the best model / performance for each MRI sequence
and evaluation metric, (2) determine the best model / performance across all
MRI sequences for each evaluation metric, and (3) determine the best general
model / performance across all MRI sequences and evaluations. Model performance
for imaging artifact was slightly higher than image quality, for example, the
final generalized model AUROC for image quality was 0.77 (0.41 - 0.84, 95% CI)
while imaging artifact was 0.78 (0.60 - 0.93, 95% CI). Further, it was noted
that the generalized model performed slightly better than the individual models
(AUROC 0.69 for 3D-GRE image quality, for example), indicating the value in
comprehensive training data for these applications. These models could be
deployed in the clinic as automatic checks for real-time image acquisition to
prevent patient re-scanning requiring another appointment after retrospective
radiologist analysis or improve reader confidence in the study. Further work
needs to be done to validate the model described here on an external dataset.
The results presented here suggest that MRQy could be used as a foundation for
quantitative metrics as a surrogate for radiologist assessment.
