Purpose Parallel imaging allows the reconstruction of images from undersampled multi-coil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm Theory and Methods A theoretical analysis shows: 1. The correlations in k-space are encoded in the null space of a calibration matrix. 2. Both approaches restrict the solution to a subspace spanned by the sensitivities. 3. The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods Results The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA. Conclusion In this paper the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.

Purpose A new acquisition and reconstruction method called T2 Shuffling is presented for volumetric fast spin-echo (3D FSE) imaging. T2 Shuffling reduces blurring and recovers many images at multiple T2 contrasts from a single acquisition at clinically feasible scan times (6 to 7 minutes). Theory and Methods The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in SNR. By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and RF field inhomogeneity. Results Retrospective under-sampling and in vivo scans confirm the increase in sharpness afforded by T2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow. Conclusion The proposed T2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan.

For the last two years, we have been experimenting with applying compressed sensing parallel imaging for body imaging of pediatric patients. It is a joint-effort by teams from UC Berkeley, Stanford University and GE Healthcare. This paper aims to summarize our experience so far. We describe our acquisition approach: 3D spoiled-gradient-echo with poisson-disc random undersampling of the phase encodes. Our re-construction approach: ℓ1-SPIRiT, an iterative autocalibrating parallel imaging reconstruction that enforces both data consistency and joint-sparsity in the wavelet domain. Our implementation: an on-line parallelized implementation of ℓ1-SPIRiT on multi-core CPU and General Purpose Graphics Processors (GPGPU) that achieves sub-minute 3D reconstructions with 8-channels. Clinical results showing higher quality reconstruction and better diagnostic confidence than parallel imaging alone at accelerations on the order of number of coils.

To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data. Methods: We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l 1-ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice. Results: The (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l 1-ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l 1-ESPIRiT images. Conclusions: DL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.

Purpose To develop and evaluate motion-compensation and compressed-sensing techniques in 4D flow MRI for anatomical assessment in a comprehensive ferumoxytol-enhanced congenital heart disease (CHD) exam. Materials and Methods A Cartesian 4D flow sequence was developed to enable intrinsic navigation and two sampling schemes: VDPoisson and VDRad. Four compressed-sensing methods were developed: A) VDPoisson scan reconstructed using spatial wavelets, B) added temporal total variation to A, C) VDRad scan using the same reconstruction as in B, and D) added motion compensation to C. With IRB approval and HIPAA compliance, 23 consecutive patients (8 females, mean 6.3 years) referred for ferumoxytol-enhanced CHD 3T MRI were recruited. Images were acquired and reconstructed using methods A–D. Two cardiovascular radiologists independently scored the images on a 5-point scale. These readers performed a paired wall motion and functional assessment between method D and 2D bSSFP CINE for 16 cases. Results Method D had higher diagnostic image quality for most anatomical features (mean 3.8–4.8) compared to A (2.0–3.6), B (2.2–3.7), and C (2.9–3.9) with P < 0.05 with good inter-observer agreement (κ ≥ 0.49). Method D had similar or better assessment of myocardial borders and cardiac motion compared to 2D bSSFP (P < 0.05, κ ≥ 0.77). All methods had good internal agreement in comparing aortic with pulmonic flow (BA mean < 0.02%, r > 0.85) and compared to method A (BA mean < 0.13%, r > 0.84) with P < 0.01. Conclusion Flow, functional, and anatomical assessment in CHD with ferumoxytol-enhanced 4D flow is feasible and can be significantly improved using motion compensation and compressed sensing.