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基于正则化图像域分裂片的 GRAPPA 的扩散 MRI 同步多片重建。

Simultaneous multi-slice image reconstruction using regularized image domain split slice-GRAPPA for diffusion MRI.

机构信息

Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT, USA; Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA.

Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT, USA.

出版信息

Med Image Anal. 2021 May;70:102000. doi: 10.1016/j.media.2021.102000. Epub 2021 Feb 16.

Abstract

The main goal of this work is to improve the quality of simultaneous multi-slice (SMS) reconstruction for diffusion MRI. We accomplish this by developing an image domain method that reaps the benefits of both SENSE and GRAPPA-type approaches and enables image regularization in an optimization framework. We propose a new approach termed regularized image domain split slice-GRAPPA (RI-SSG), which establishes an optimization framework for SMS reconstruction. Within this framework, we use a robust forward model to take advantage of both the SENSE model with explicit sensitivity estimations and the SSG model with implicit kernel relationship among coil images. The proposed approach also allows combining of coil images to increase the SNR and enables image domain regularization on estimated coil-combined single slices. We compare the performance of RI-SSG with that of SENSE and SSG using in-vivo diffusion EPI datasets with simulated and actual SMS acquisitions collected on a 3T MR scanner. Reconstructed diffusion-weighted images (DWIs) and the resulting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) maps are analyzed to evaluate the quantitative and qualitative performance of the three methods. The DWIs reconstructed by RI-SSG are closer to the single-band ground truth images than SENSE and SSG. Specifically, the proposed RI-SSG reduces the normalized root-mean-square-error (nRMSE) against ground truth images by ∼5% and increases the structural similarity index (SSIM) by ∼4% compared to SSG. All three methods produce similar fractional anisotropy (FA) maps using DTI representation, but mean diffusivity (MD) and fiber orientation estimates using RI-SSG are closer to the reference than SENSE and SSG. RI-SSG results in NODDI maps with noticeably smaller errors than those of SENSE and SSG and improves the accuracy of the mean value of orientation dispersion index (ODI) by ∼5% and the mean value of intracellular volume fraction by ∼7% in regions of interest in brain white matter compared to SSG.

摘要

这项工作的主要目标是提高扩散磁共振成像的多片同时重建质量。我们通过开发一种图像域方法来实现这一目标,该方法利用了 SENSE 和 GRAPPA 型方法的优势,并在优化框架中实现了图像正则化。我们提出了一种新的方法,称为正则化图像域分裂片-GRAPPA(RI-SSG),它为 SMS 重建建立了一个优化框架。在这个框架内,我们使用一个稳健的正向模型,利用具有显式灵敏度估计的 SENSE 模型和具有线圈图像之间隐式核关系的 SSG 模型。所提出的方法还允许结合线圈图像以增加 SNR,并能够对估计的线圈组合单幅图像进行图像域正则化。我们使用在 3T 磁共振扫描仪上采集的体内扩散 EPI 数据集和模拟及实际 SMS 采集,比较了 RI-SSG 与 SENSE 和 SSG 的性能。重建的扩散加权图像(DWIs)以及由此产生的扩散张量成像(DTI)和神经纤维取向分散度和密度成像(NODDI)图用于评估这三种方法的定量和定性性能。与 SENSE 和 SSG 相比,RI-SSG 重建的 DWIs 更接近单带地面真实图像。具体来说,与 SSG 相比,所提出的 RI-SSG 使归一化均方根误差(nRMSE)相对于地面真实图像降低了约 5%,并使结构相似性指数(SSIM)提高了约 4%。使用 DTI 表示,这三种方法都产生了相似的各向异性分数(FA)图,但使用 RI-SSG 的平均扩散系数(MD)和纤维取向估计值比 SENSE 和 SSG 更接近参考值。RI-SSG 产生的 NODDI 图误差明显小于 SENSE 和 SSG,与 SSG 相比,在脑白质感兴趣区域,方位离散度指数(ODI)的平均值提高了约 5%,细胞内体积分数的平均值提高了约 7%。

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