3D-MB-MUSE: A robust 3D multi-slab, multi-band and multi-shot reconstruction approach for ultrahigh resolution diffusion MRI.
3D-MB-MUSE: A robust 3D multi-slab, multi-band and multi-shot reconstruction approach for ultrahigh resolution diffusion MRI.
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DOI：
10.1016/j.neuroimage.2017.07.035
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发表时间：
2017-10-01
期刊：
影响因子：
5.7
通讯作者：
Song AW
中科院分区：
文献类型：
--
作者：
Bruce IP;Chang HC;Petty C;Chen NK;Song AW
事实证明，在实现超高空间分辨率（例如亚毫米）扩散 MRI (dMRI) 数据方面取得的最新进展对于表征大脑等器官的组织微观结构非常有益。然而，由于采集时间长、运动引起的伪影和低信噪比等因素，在如此高的空间分辨率下常规采集体内 dMRI 数据在很大程度上受到阻碍。为了克服这些限制，我们在此提出了一个用于获取和重建 3D 多片、多波段和交错多镜头 EPI 数据的框架，称为 3D-MB-MUSE。通过多波段激励，同时采集多个 3D 板，可以在相当短的时间内在 3 特斯拉临床 MRI 扫描仪上以高空间分辨率在体内采集全脑 dMRI 体积。 3D-MB-MUSE 代表真实的 3D 模型，可立即重建整个 3D 多波段、多镜头 dMRI 板，同时考虑整个板的线圈灵敏度变化以及通常与 3D 和多镜头扩散成像相关的运动引起的伪影。这种重建通过消除多个维度上的运动和混叠伪影，充分保留了高分辨率 dMRI 图像中 3D 和多镜头采集的 SNR 优势。通过实现超高分辨率 dMRI 的日常使用，此处介绍的 3D-MB-MUSE 框架可能在临床和研究应用中具有极高的价值。
Recent advances in achieving ultrahigh spatial resolution (e.g. sub-millimeter) diffusion MRI (dMRI) data have proven highly beneficial in characterizing tissue microstructures in organs such as the brain. However, the routine acquisition of in-vivo dMRI data at such high spatial resolutions has been largely prohibited by factors that include prolonged acquisition times, motion induced artifacts, and low SNR. To overcome these limitations, we present here a framework for acquiring and reconstructing 3D multi-slab, multi-band and interleaved multi-shot EPI data, termed 3D-MB-MUSE. Through multi-band excitations, the simultaneous acquisition of multiple 3D slabs enables whole brain dMRI volumes to be acquired in-vivo on a 3 Tesla clinical MRI scanner at high spatial resolution within a reasonably short amount of time. Representing a true 3D model, 3D-MB-MUSE reconstructs an entire 3D multi-band, multi-shot dMRI slab at once while simultaneously accounting for coil sensitivity variations across the slab as well as motion induced artifacts commonly associated with both 3D and multi-shot diffusion imaging. Such a reconstruction fully preserves the SNR advantages of both 3D and multi-shot acquisitions in high resolution dMRI images by removing both motion and aliasing artifacts across multiple dimensions. By enabling ultrahigh resolution dMRI for routine use, the 3D-MB-MUSE framework presented here may prove highly valuable in both clinical and research applications.
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