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To 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).

First‐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.

Results 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.

PG‐DL reconstruction with the proposed SIIM encoding operator improves generalization across different time‐frames /SNRs in highly accelerated perfusion CMR.

To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI.

ReadOutConcatenatedk‐space SPIRiT (ROCK‐SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self‐consistency as in SPIRiT‐type reconstruction in an extended k‐space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS‐accelerated cine imaging with three‐fold SMS and two‐fold in‐plane acceleration. ROCK‐SPIRiT is compared with two leakage‐blocking SMS reconstruction methods: readout‐SENSE‐GRAPPA and split slice–GRAPPA. Further evaluation and comparisons are performed using prospectively SMS‐accelerated cine imaging.

Results on retrospectively three‐fold SMS and two‐fold in‐plane accelerated cine imaging show that ROCK‐SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout‐SENSE‐GRAPPA: 33.5 ± 3.2, split slice–GRAPPA: 34.1 ± 3.8, ROCK‐SPIRiT: 35.0 ± 3.3) and SSIM (readout‐SENSE‐GRAPPA: 84.4 ± 8.9, split slice–GRAPPA: 85.0 ± 8.9, ROCK‐SPIRiT: 88.2 ± 6.6 [in percentage]). Regularized ROCK‐SPIRiT significantly outperforms all methods, as characterized by these quantitative metrics (PSNR: 37.6 ± 3.8, SSIM: 94.2 ± 4.1 [in percentage]). The prospectively five‐fold SMS and two‐fold in‐plane accelerated data show that ROCK‐SPIRiT and regularized ROCK‐SPIRiT have visually improved image quality compared with existing methods.

The proposed ROCK‐SPIRiT technique reduces noise and interslice leakage in accelerated SMS cardiac cine MRI, improving on existing methods both quantitatively and qualitatively.

To develop a strategy for training a physics‐guided MRI reconstruction neural network without a database of fully sampled data sets.

Self‐supervised learning via data undersampling (SSDU) for physics‐guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground‐truth data, as well as conventional compressed‐sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics‐guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively two‐fold accelerated high‐resolution brain data sets at different acceleration rates, and compared with parallel imaging.

Results on five different knee sequences at an acceleration rate of 4 shows that the proposed self‐supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed‐sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively subsampled brain data sets, in which supervised learning cannot be used due to lack of ground‐truth reference, show that the proposed self‐supervised approach successfully performs reconstruction at high acceleration rates (4, 6, and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared with parallel imaging at acquisition acceleration.

The proposed SSDU approach allows training of physics‐guided deep learning MRI reconstruction without fully sampled data, while achieving comparable results with supervised deep learning MRI trained on fully sampled data.

To develop a non‐Cartesian k‐space reconstruction method using self‐calibrated region‐specific interpolation kernels for highly accelerated acquisitions.

In conventional non‐Cartesian GRAPPA with through‐time GRAPPA (TT‐GRAPPA), the use of region‐specific interpolation kernels has demonstrated improved reconstruction quality in dynamic imaging for highly accelerated acquisitions. However, TT‐GRAPPA requires the acquisition of a large number of separate calibration scans. To reduce the overall imaging time, we propose Self‐calibrated Interpolation of Non‐Cartesian data with GRAPPA (SING) to self‐calibrate region‐specific interpolation kernels from dynamic undersampled measurements. The SING method synthesizes calibration data to adapt to the distinct shape of each region‐specific interpolation kernel geometry, and uses a novel local k‐space regularization through an extension of TT‐GRAPPA. This calibration approach is used to reconstruct non‐Cartesian images at high acceleration rates while mitigating noise amplification. The reconstruction quality of SING is compared with conjugate‐gradient SENSE and TT‐GRAPPA in numerical phantoms and in vivo cine data sets.

In both numerical phantom and in vivo cine data sets, SING offers visually and quantitatively similar reconstruction quality to TT‐GRAPPA, and provides improved reconstruction quality over conjugate‐gradient SENSE. Furthermore, temporal fidelity in SING and TT‐GRAPPA is similar for the same acceleration rates. G‐factor evaluation over the heart shows that SING and TT‐GRAPPA provide similar noise amplification at moderate and high rates.

The proposed SING reconstruction enables significant improvement of acquisition efficiency for calibration data, while matching the reconstruction performance of TT‐GRAPPA.

To develop an improved k‐space reconstruction method using scan‐specific deep learning that is trained on autocalibration signal (ACS) data.

Robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k‐space lines from acquired k‐space data with improved noise resilience, as opposed to conventional linear k‐space interpolation‐based methods, such as GRAPPA, which are based on linear convolutional kernels.

The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm. The neural network contains 3 layers of convolutional operators, with 2 of these including nonlinear activation functions. The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo data sets.

Phantom imaging shows that the proposed RAKI method outperforms GRAPPA at high (≥4) acceleration rates, both visually and quantitatively. Quantitative cardiac imaging shows improved noise resilience at high acceleration rates (rate 4:23% and rate 5:48%) over GRAPPA. The same trend of improved noise resilience is also observed in high‐resolution brain imaging at high acceleration rates.

The RAKI method offers a training database‐free deep learning approach for MRI reconstruction, with the potential to improve many existing reconstruction approaches, and is compatible with conventional data acquisition protocols.

To develop and evaluate a cardiac phase‐resolved myocardial T₁mapping sequence.

The 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.

In 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.

The 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.