Introduction: Current practice to obtain left ventricular (LV) native and post-contrast T1 and T2 comprises single-slice readouts with multiple breath-holds (BHs). We propose a multi-slice parallel... Show moreIntroduction: Current practice to obtain left ventricular (LV) native and post-contrast T1 and T2 comprises single-slice readouts with multiple breath-holds (BHs). We propose a multi-slice parallel-imaging approach with a 72-channel receive-array to reduce BHs and demonstrate this in healthy subjects and hypertrophic cardiomyopathy (HCM) patients.Methods: A T1/T2 phantom was scanned at 3 T using a 16-channel and a novel 72-channel coil to assess the impact of different coils and acceleration factors on relaxation times. 16-18 healthy participants (8 female, age 28.4 +/- 5.1 years) and 3 HCM patients (3 male, age 55.3 +/- 4.2 years) underwent cardiac-MRI with the 72-channel coil, using a Modified Look-Locker scan with a shared inversion pulse across 3 slices and a Gradient-Spin-Echo scan. Acceleration was done by sensitivity encoding (SENSE) with accelerations 2, 4, and 6. LV T1 and T2 values were analyzed globally, per slice, and in 16 segments, with SENSE = 2 as the reference.Results: The phantom scans revealed no bias between coils and acceleration factors for T1 or T2, except for T2 with SENSE = 2, which resulted in a bias of 8.0 +/- 6.7 ms (p < 0.001) between coils. SENSE = 4 and 6 enabled T1 mapping of three slices in a single BH, and T2 mapping of three slices within two BHs. In healthy subjects, T1 and T2 values varied. We found an average overestimation of T1 in 3 slices of 25 +/- 87 ms for SENSE = 4 and 30 +/- 103 ms using SENSE = 6, as compared to SENSE = 2. Acceleration resulted in decreased signal-to-noise; however, visually insignificant and without increased incidence of SENSE-artifacts. T2 was overestimated by 2.1 +/- 5.0 ms for SENSE = 4 and 6.4 +/- 9.7 ms using SENSE = 6, as compared to SENSE = 2. Native and post-contrast T1 measurements with SENSE = 4 and ECV quantification in HCM patients was successful.Conclusion: The 72-channel receiver-array coil with SENSE = 4 and 6, enabled LV-tissue characterization in three slices. Pre- and post-contrast T1 maps were obtained in a single BH, while T2 required two BHs. Show less
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated... Show moreQuantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table) as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution by approximating the signal between grid points through B-spline interpolation. The interpolant and its gradient are evaluated efficiently which enables a least-squares fitting method for parameter mapping. The resolution of each parameter was minimized while obtaining a user-specified interpolation accuracy. The method was evaluated by phantom and in-vivo experiments using fully-sampled and undersampled unbalanced (FISP) MR fingerprinting acquisitions. Bloch simulations incorporated relaxation effects (T-1, T-2), proton density (PD), receiver phase ( phi(0)), transmit field inhomogeneity (B-1(+)), and slice profile. Parametermapswere comparedwith those obtained from dictionary matching, where the parameter resolution was chosen to obtain similar signal (interpolation) accuracy. For both the phantom and the in-vivo acquisition, the proposed method approximated the parameter maps obtained through dictionary matching while reducing the parameter resolution in each dimension (T-1, T-2, B-1(+)) by - on average - an order of magnitude. In effect, the applied dictionary was reduced from 1.47GB to 464KB. Furthermore, the proposed method was equally robust against undersampling artifacts as dictionarymatching. Dictionary fittingwith B-spline interpolation reduces the computational and memory costs of dictionary-based methods and is therefore a promising method for multi- parametric mapping. Show less
