ABSTRACT Background0.55T systems offer unique advantages and may support expanded access to cardiac MRI. PurposeTo assess the feasibility of 0.55T cardiac MR Fingerprinting (MRF), leveraging a deep image prior reconstruction to mitigate noise. Study TypePhantom and prospective in vivo assessment. PopulationISMRM/NIST MRI system phantom and 18 healthy subjects (11 female; ages 28 ± 8 years). Field Strength and SequencesMRF, modified Look‐Locker inversion recovery (MOLLI), and T2‐prepared balanced steady state free precession (T2‐bSSFP) at 0.55T. AssessmentMRF T1and T2maps were reconstructed using (1) a low‐rank technique with sparse and locally low‐rank regularization (SLLR‐MRF) and (2) a deep image prior (DIP‐MRF). Accuracy and precision of MRF and conventional sequences were evaluated in a phantom. In vivo performance of MRF was evaluated in the 18 healthy subjects, with 7 subjects also undergoing conventional mapping. Myocardial T1and T2values were compared among methods and image quality scored by three readers (2, 3, and 4 years of experience) on a 5‐point scale. Statistical TestsLinear regression, Bland–Altman, intraclass correlation coefficient, and one‐way ANOVA withp < 0.05 considered significant. ResultsMean measurements in the left ventricular septum were 671 ± 31 ms (MOLLI), 761 ± 147 ms (SLLR‐MRF), and 686 ± 39 ms (DIP‐MRF) for T1, and 63.5 ± 5.7 ms (T2‐bSSFP), 47.5 ± 12.7 ms (SLLR‐MRF), and 45.2 ± 4.5 ms (DIP‐MRF) for T2. Compared to conventional mapping, DIP‐MRF exhibited significantly lower T2but no differences in T1(p > 0.99). Standard deviations within the myocardium were significantly lower with DIP‐MRF compared to SLLR‐MRF (39 vs. 147 ms for T1and 4.5 vs. 12.7 ms for T2). Overall image quality ratings were significantly lower for SLLR‐MRF (T1: 2.3, T2: 2.9), which were significantly lower compared to conventional mapping methods (T1: 3.4, T2: 3.9), and DIP‐MRF (T1: 3.8, T2: 4.1) received higher scores. Data ConclusionThis study demonstrated the feasibility of cardiac MRF on a commercial 0.55T system, enabled by a deep image prior reconstruction for denoising. Evidence Level2. Stage of Technical Efficacy1.
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Phase stabilization with motion compensated diffusion weighted imaging
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.
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- Award ID(s):
- 2205103
- PAR ID:
- 10536157
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Magnetic Resonance in Medicine
- ISSN:
- 0740-3194
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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