Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Motivation: Quantitative image quality evaluation tools are needed for machine learning-based MR reconstruction. Goal(s): To introduce digital image quality phantoms and evaluation metrics tailored for machine learning-based MR reconstruction, scalable to form large test sets, and flexible to simulate various object size, image contrast, signal-to-noise-ratio, resolution etc. Approach: We created 2D disks, resolution arrays, and low-contrast phantoms resembling MR ACR phantom properties. The evaluation includes geometric accuracy, intensity uniformity, resolution, and low-contrast detectability. We evaluated the AUTOMAP reconstruction model trained on the M4Raw and FastMRI datasets with these phantoms. Results: The study provides a tool for evaluating machine learning-based MRI reconstruction. Impact: This research establishes digital phantoms and quantitative metrics for evaluating machine learning-based MRI reconstruction. These tools enable accurate assessment of fundamental image quality and generalizability over scan conditions, offering valuable feedback for improving machine learning-based methods development.