Multi-Stage Deep Learning Enables Accurate Detection of Ischemia in Myocardial Perfusion MRI with Order-of-magnitude Lower Contrast Dose

Motivation: Reducing gadolinium doses in stress/rest perfusion cardiac-MRI (CMR) is highly desirable for patient safety, but lower doses diminish image quality, risking inaccurate myocardial ischemia detection. Goal(s): Determine if a multi-stage deep learning (MST) myocardial blood flow quantification method can accurately detect myocardial ischemia in stress-perfusion CMR using significantly reduced gadolinium doses. Approach: Trained an MST model on full-dose perfusion images with data augmentation to simulate low contrast-to-noise acquisition, then assessed its ischemia detection accuracy at reduced contrast doses compared to traditional methods. Results: The MST method accurately detected myocardial ischemia with up to tenfold lower gadolinium doses, outperforming traditional Fermi-deconvolution in diagnostic accuracy. Impact: The MST deep learning method enables accurate ischemia detection in stress CMR with significantly reduced gadolinium doses, enhancing patient safety and reducing costs. This advancement could facilitate safer, more accessible stress CMR protocols in clinical practice.