3D Cine Magnetic Resonance Fingerprinting for Rapid Phenotyping of Cardiomyopathy

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PROJECT SUMMARY/ABSTRACT Cardiomyopathy (CM) encompasses a diverse group of diseases of the heart muscle that occur in 1 out of 500 adults and predispose to heart failure. Accurate determination of CM subtype (phenotyping) is essential to inform downstream tests, risk stratification, and targeted treatments. Cardiac MRI has emerged as the non-invasive standard for assessment of cardiac structure, function, and tissue properties in patients with suspected CM. However, cardiac MRI only comprises 1% of all MRI exams in the US, largely due to the need for (1) long and complex protocols where multiple images are collected with different contrast weightings, (2) unreliable and uncomfortable strategies to reduce motion, and (3) a lack of reproducibility of certain tissue property measurements. This multidisciplinary project between MRI scientists and cardiologists will validate 3D cine Magnetic Resonance Fingerprinting (MRF) as a comprehensive all-in-one imaging technique for CM detection and phenotyping. A streamlined and paradigm-changing cardiac MRI exam is proposed, consisting of a 5-minute free-breathing and ungated 3D cine MRF scan that will be collected before and after administration of gadolinium contrast. This technique will yield quantitative T1, T2, and spin density (M0) maps with 3D isotropic coverage over the left (LV) and right (RV) ventricles. Additionally, measured tissue properties and MRI simulations will be used to generate contrast-weighted cine and LGE images in an automated fashion, eliminating the need for multiple acquisitions and manual scan adjustments. A multicontrast LGE approach is also proposed where bright-blood, dark-blood, and novel “optimal-contrast” images will be generated to optimally highlight myocardial scar and fibrosis. The 3D cine MRF exam is expected to have advantages over routine clinical imaging and existing rapid imaging methods in terms of (1) improved accuracy/reproducibility of quantitative tissue properties, (2) shorter exam times, (3) reduced operator dependence, and (4) high diagnostic accuracy for specific CM phenotypes. Technical validation of 3D cine MRF in healthy subjects will be performed in Aim 1, including development of cardiac/respiratory self-gating methods tailored for MRF and development of a “physics-informed” deep learning reconstruction for artifact reduction and scan acceleration. Aim 2 will compare image quality and quantitative measurements from 3D cine MRF to standard MRI methods in patients with established CM. Additionally, quantitative thresholds for objective detection of specific CM phenotypes will be determined. In Aim 3, tissue properties and synthetic images derived from 3D cine MRF for will be tested in a prospective cross-sectional study to evaluate diagnostic accuracy for differentiating (1) ischemic vs nonischemic CM and (2) nonischemic CM phenotypes, using a standard cardiac MRI protocol as reference. The overall expected outcome of this work is an ultrafast all-in-one MRI exam for CM detection and phenotyping that will streamline cardiac MRI exams and assess cardiac structure, function, and tissue properties using reproducible quantitative imaging.