A 3D k‐space Fourier encoding and reconstruction framework for simultaneous multi‐slab acquisition

et al. 2021

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Purpose To improve distortion correction for isotropic high‐resolution whole‐brain 3D diffusion MRI when in a time‐saving acquisition scenario. Theory and methods Data were acquired using simultaneous multi‐slab (SMSlab) acquisitions, with a b = 0 image pair encoded by reversed polarity gradients (RPG) for phase encoding (PE) and diffusion weighted images encoded by a single PE direction. Eddy current‐induced distortions were corrected first. During the following susceptibility distortion correction, image deformation was first corrected by the field map estimated from the b = 0 image pair. Intensity variation was subsequently corrected by Jacobian modulation. Two Jacobian modulation methods were compared. They calculated the Jacobian modulation map from the field map, or from the deformation corrected b = 0 image pair, termed as JField and JRPG, respectively. A modified version of the JRPG method, with proper smoothing, was further proposed for improved correction performance, termed as JRPG‐smooth. Results Compared to JField modulation, less remaining distortions are observed when using the JRPG and JRPG‐smooth methods, especially in areas with large B0 field inhomogeneity. The original JRPG method causes signal‐to‐noise ratio (SNR) deficiency problem, which manifests as degraded SNR of the diffusion weighted images, while the JRPG‐smooth method maintains the original image SNR. Less estimation errors of diffusion metrics are observed when using the JRPG‐smooth method. Conclusion This study improves the distortion correction for isotropic high‐resolution whole‐brain 3D diffusion MRI by optimizing Jacobian modulation. The optimized method outperforms the conventional JField method regarding intensity variation correction and accuracy of diffusion metrics estimation, and outperforms the original JRPG method regarding SNR performance.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Acquisition and Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The dMRI data were acquired using the 3D SMSlab technique. 8,29 The efficacy of the optimized Jacobian modulation method was demonstrated in in vivo experiments.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving distortion correction for isotropic high‐resolution 3D diffusion MRI by optimizing Jacobian modulation

Liu

Xiong

et al. 2021

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Slab boundary artifact correction in multislab imaging using convolutional‐neural‐network–enabled inversion for slab profile encoding

Zhang

Liu

et al. 2021

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Purpose This study aims to propose a novel algorithm for slab boundary artifact correction in both single‐band multislab imaging and simultaneous multislab (SMSlab) imaging. Theory and Methods In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact‐free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN‐enabled inversion for slab profile encoding (CPEN). Diffusion‐weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single‐band multislab and SMSlab images with 1.3‐mm or 1‐mm isotropic resolution. CNN‐enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]). Results CNN‐enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single‐band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times. Conclusion CNN‐enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high‐resolution DWI and functional MRI.

Multi‐band multi‐shot diffusion MRI reconstruction with joint usage of structured low‐rank constraints and explicit phase mapping

Mani

McNab

2022

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To develop a joint reconstruction method for multi-band multi-shot diffusion MRI. Theory and Methods: Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values.The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]). Results: JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency. Conclusion:The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.