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Abstract PurposeDiffusion encoding gradient waveforms can impartintra‐voxelandinter‐voxeldephasing owing to bulk motion, limiting achievable signal‐to‐noise and complicating multishot acquisitions. In this study, we characterize improvements in phase consistency via gradient moment nulling of diffusion encoding waveforms. MethodsHealthy volunteers received neuro () and cardiac () MRI. Three gradient moment nulling levels were evaluated: compensation for position (), position + velocity (), and position + velocity + acceleration (). Three experiments were completed: (Exp‐1) Fixed Trigger Delay Neuro DWI; (Exp‐2) Mixed Trigger Delay Neuro DWI; and (Exp‐3) Fixed Trigger Delay Cardiac DWI. Significant differences () of the temporal phase SD between repeated acquisitions and the spatial phase gradient across a given image were assessed. Resultsmoment nulling was a reference for all measures. In Exp‐1, temporal phase SD for diffusion encoding was significantly reduced with (35% oft‐tests) and (68% oft‐tests). The spatial phase gradient was reduced in 23% oft‐tests for and 2% of cases for . In Exp‐2, temporal phase SD significantly decreased with gradient moment nulling only for (83% oft‐tests), but spatial phase gradient significantly decreased with only (50% oft‐tests). In Exp‐3, gradient moment nulling significantly reduced temporal phase SD and spatial phase gradients (100% oft‐tests), resulting in less signal attenuation and more accurate ADCs. ConclusionWe characterized gradient moment nulling phase consistency for DWI. UsingM₁for neuroimaging andM₁ + M₂for cardiac imaging minimized temporal phase SDs and spatial phase gradients. 

Increased passive myocardial stiffness is implicated in the pathophysiology of many cardiac diseases, and its in vivo estimation can improve management of heart disease. MRI-driven computational constitutive modeling has been used extensively to evaluate passive myocardial stiffness. This approach requires subject-specific data that is best acquired with different MRI sequences: conventional cine (e.g. bSSFP), tagged MRI (or DENSE), and cardiac diffusion tensor imaging. However, due to the lack of comprehensive datasets and the challenge of incorporating multi-phase and single-phase disparate MRI data, no studies have combined in vivo cine bSSFP, tagged MRI, and cardiac diffusion tensor imaging to estimate passive myocardial stiffness. The objective of this work was to develop a personalized in silico left ventricular model to evaluate passive myocardial stiffness by integrating subject-specific geometric data derived from cine bSSFP, regional kinematics extracted from tagged MRI, and myocardial microstructure measured using in vivo cardiac diffusion tensor imaging. To demonstrate the feasibility of using a complete subject-specific imaging dataset for passive myocardial stiffness estimation, we calibrated a bulk stiffness parameter of a transversely isotropic exponential constitutive relation to match the local kinematic field extracted from tagged MRI. This work establishes a pipeline for developing subject-specific biomechanical ventricular models to probe passive myocardial mechanical behavior, using comprehensive cardiac imaging data from multiple in vivo MRI sequences.