  In clinical practice MR images are often first seen by radiologists long
after the scan. If image quality is inadequate either patients have to return
for an additional scan, or a suboptimal interpretation is rendered. An
automatic image quality assessment (IQA) would enable real-time remediation.
Existing IQA works for MRI give only a general quality score, agnostic to the
cause of and solution to low-quality scans. Furthermore, radiologists' image
quality requirements vary with the scan type and diagnostic task. Therefore,
the same score may have different implications for different scans. We propose
a framework with multi-task CNN model trained with calibrated labels and
inferenced with image rulers. Labels calibrated by human inputs follow a
well-defined and efficient labeling task. Image rulers address varying quality
standards and provide a concrete way of interpreting raw scores from the CNN.
The model supports assessments of two of the most common artifacts in MRI:
noise and motion. It achieves accuracies of around 90%, 6% better than the best
previous method examined, and 3% better than human experts on noise assessment.
Our experiments show that label calibration, image rulers, and multi-task
training improve the model's performance and generalizability.
