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A dataset of 3D-GRE and 3D-TSE brain 3T post contrast T1-weighted images aspart of a quality improvement project were collected and shown to fiveneuro-radiologists who evaluated each sequence for both image quality andimaging artifacts. The same scans were processed using the MRQy tool forobjective, quantitative image quality metrics. Using the combined radiologistand quantitative metrics dataset, a decision tree classifier with a baggingensemble approach was trained to predict radiologist assessment using thequantitative metrics. A machine learning model was developed for the followingthree tasks: (1) determine the best model / performance for each MRI sequenceand evaluation metric, (2) determine the best model / performance across allMRI sequences for each evaluation metric, and (3) determine the best generalmodel / performance across all MRI sequences and evaluations. Model performancefor imaging artifact was slightly higher than image quality, for example, thefinal 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 notedthat the generalized model performed slightly better than the individual models(AUROC 0.69 for 3D-GRE image quality, for example), indicating the value incomprehensive training data for these applications. These models could bedeployed in the clinic as automatic checks for real-time image acquisition toprevent patient re-scanning requiring another appointment after retrospectiveradiologist analysis or improve reader confidence in the study. Further workneeds 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 forquantitative metrics as a surrogate for radiologist assessment.

The Use of Quantitative Metrics and Machine Learning to Predict Radiologist Interpretations of MRI Image Quality and Artifacts