Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Purpose To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl‐MRI) reconstruction that is fast, memory efficient, and scales to high‐dimensional imaging. Theory and Methods Cl‐MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh‐dimensional fast convolutional framework (HICU), provides fast, memory‐efficient recovery of unsampled k‐space points. For demonstration, HICU is applied to 6 2D T2‐weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi‐shot diffusion weighted imaging (MSDWI) datasets. Results The 2D imaging results show that HICU can offer 1‐2 orders of magnitude computation speedup compared to other Cl‐MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE‐based compressed sensing methods with up to 3 dB improvement in signal‐to‐error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi‐shot echo‐planar imaging application. Conclusions The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

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“…The prospective sampling pattern for 4 shots is S 5 with acceleration rate

R = 6.74

, as shown in Figure 1. Detailed description of the dataset is provided by Mani et al 13 …”

Section: Methodsmentioning

confidence: 99%

High‐dimensional fast convolutional framework (HICU) for calibrationless MRI

Zhao

Potter

Ahmad

2021

Magnetic Resonance in Med

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“…In its current implementation, the image reconstruction for the multi-shot approach and dictionary matching steps have not been optimized for speed and prohibit the images from being used in real time. The image reconstruction time depends on several factors, including the number of central processing unit (CPU) cores used for parallel imaging, image size, number of shots, number of iterations, and stopping criteria, but should be able to be accelerated by several orders of magnitude using more recently proposed approaches 48,49 for multi-shot reconstruction. Additionally, several approaches for improving dictionary matching speed [50][51][52] have been proposed using fast group matching, neural networks, and graphics processing units (GPUs) that allow for this step to be completed in less than 1 s per slice.…”

Section: Discussionmentioning

confidence: 99%

A multi‐inversion multi‐echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts

Manhard

Stockmann

Liao

et al. 2021

Magnetic Resonance in Med

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Purpose: Brain imaging exams typically take 10-20 min and involve multiple sequential acquisitions. A low-distortion whole-brain echo planar imaging (EPI)-based approach was developed to efficiently encode multiple contrasts in one acquisition, allowing for calculation of quantitative parameter maps and synthetic contrastweighted images. Methods: Inversion prepared spin-and gradient-echo EPI was developed with sliceorder shuffling across measurements for efficient acquisition with T 1 , T 2 , and T * 2 weighting. A dictionary-matching approach was used to fit the images to quantitative parameter maps, which in turn were used to create synthetic weighted images with typical clinical contrasts. Dynamic slice-optimized multi-coil shimming with a B 0 shim array was used to reduce B 0 inhomogeneity and, therefore, image distortion by >50%. Multi-shot EPI was also implemented to minimize distortion and blurring while enabling high in-plane resolution. A low-rank reconstruction approach was used to mitigate errors from shot-to-shot phase variation. Results: The slice-optimized shimming approach was combined with in-plane parallel-imaging acceleration of 4× to enable single-shot EPI with more than eightfold distortion reduction. The proposed sequence efficiently obtained 40 contrasts across the whole-brain in just over 1 min at 1.2 × 1.2 × 3 mm resolution. The multishot variant of the sequence achieved higher in-plane resolution of 1 × 1 × 4 mm with good image quality in 4 min. Derived quantitative maps showed comparable values to conventional mapping methods. Conclusion:The approach allows fast whole-brain imaging with quantitative parameter maps and synthetic weighted contrasts. The slice-optimized multi-coil shimming | 867 MANHARD et Al.

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“…We compare the proposed scheme against IRLS-MUSSELS [7],P-MUSE [22], and a solution based on U-NET [27]. The IRLS-MUSSELS is a modification of the MUSSELS algorithm [6].…”

Section: Algorithms Used For Comparisonmentioning

confidence: 99%

“…The MUSSELS algorithm [6], which is based on iterative singular value shrinkage, alternates between a data-consistency block and a low-rank matrix recovery block. By contrast, the IRLS-MUSSELS algorithm [7] alternates between a data-consistency block and a residual multichannel 1 convolution block. The multichannel convolution block can be viewed as the projection of the data to the nullspace of the multichannel signals; the subtraction of the result from the original ones, induced by the residual structure, projects the data to the signal subspace, thus removing the artefacts in the signal.…”

Section: Introductionmentioning

confidence: 99%

MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

Aggarwal

Mani

Jacob

2020

IEEE Trans. Med. Imaging

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We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace, thus exploiting the annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the annihilation relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state-of-the-art methods while offering several orders of magnitude reduction in run-time. ) are with the department of electrical and computer engineering,

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Improved MUSSELS reconstruction for high‐resolution multi‐shot diffusion weighted imaging

Cited by 21 publications

References 51 publications

High‐dimensional fast convolutional framework (HICU) for calibrationless MRI

High‐dimensional fast convolutional framework (HICU) for calibrationless MRI

A multi‐inversion multi‐echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts

MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

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