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PurposeTo develop a physics‐guided deep learning (PG‐DL) reconstruction strategy based on a signal intensity informed multi‐coil (SIIM) encoding operator for highly‐accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). MethodsFirst‐pass perfusion CMR acquires highly‐accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium‐based contrast agent. Thus, using PG‐DL reconstruction with a conventional multi‐coil encoding operator leads to analogous signal intensity variations across different time‐frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time‐frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG‐DL network, facilitating generalizability across time‐frames. PG‐DL reconstruction with the proposed SIIM encoding operator is compared to PG‐DL with conventional encoding operator, split slice‐GRAPPA, locally low‐rank (LLR) regularized reconstruction, low‐rank plus sparse (L + S) reconstruction, and regularized ROCK‐SPIRiT. ResultsResults on highly accelerated free‐breathing first pass myocardial perfusion CMR at three‐fold SMS and four‐fold in‐plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice‐GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG‐DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. ConclusionPG‐DL reconstruction with the proposed SIIM encoding operator improves generalization across different time‐frames /SNRs in highly accelerated perfusion CMR. 

PurposeTo develop and evaluate a cardiac phase‐resolved myocardial T₁mapping sequence. MethodsThe proposed method for temporally resolved parametric assessment of Z‐magnetization recovery (TOPAZ) is based on contiguous fast low‐angle shot imaging readout after magnetization inversion from the pulsed steady state. Thereby, segmented k‐space data are acquired over multiple heartbeats, before reaching steady state. This results in sampling of the inversion‐recovery curve for each heart phase at multiple points separated by an R‐R interval. Joint T₁andestimation is performed for reconstruction of cardiac phase‐resolved T₁andmaps. Sequence parameters are optimized using numerical simulations. Phantom and in vivo imaging are performed to compare the proposed sequence to a spin‐echo reference and saturation pulse prepared heart rate–independent inversion‐recovery (SAPPHIRE) T₁mapping sequence in terms of accuracy and precision. ResultsIn phantom, TOPAZ T₁values with integratedcorrection are in good agreement with spin‐echo T₁values (normalized root mean square error = 4.2%) and consistent across the cardiac cycle (coefficient of variation = 1.4 ± 0.78%) and different heart rates (coefficient of variation = 1.2 ± 1.9%). In vivo imaging shows no significant difference in TOPAZ T₁times between the cardiac phases (analysis of variance:P = 0.14, coefficient of variation = 3.2 ± 0.8%), but underestimation compared with SAPPHIRE (T₁time ± precision: 1431 ± 56 ms versus 1569 ± 65 ms). In vivo precision is comparable to SAPPHIRE T₁mapping until middiastole (P > 0.07), but deteriorates in the later phases. ConclusionsThe proposed sequence allows cardiac phase‐resolved T₁mapping with integratedassessment at a temporal resolution of 40 ms. Magn Reson Med 79:2087–2100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.