Accelerated High Spatial Resolution Diffusion-Weighted Imaging.
Accelerated High Spatial Resolution Diffusion-Weighted Imaging.
复制标题
加速高空间分辨率扩散加权成像。
DOI：
10.1007/978-3-319-19992-4_6
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Warfield,SimonK
中科院分区：
文献类型：
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作者：
Scherrer,Benoit;Afacan,Onur;Taquet,Maxime;Prabhu,SanjayP;Gholipour,Ali;Warfield,SimonK
最近提出了在图像空间中获取一系列各向异性过采样（所谓的各向异性“快照”）和重建，以提高扩散加权成像（DWI）的空间分辨率，与传统的密集k空间采样相比，在等信噪比（SNR）下提供理论上8倍的加速度。然而，在大多数作品中，每张DW图像都是单独重建的，而忽略了DW图像构成同一解剖结构的不同视图。此外，目前的方法受到无法从具有不同扩散梯度子集的快照中重建高分辨率（HR）采集的限制：如果各向异性快照中的一个缺失，例如由于扫描内运动，即使成功获取了该梯度的其他快照，各向同性HR梯度图像也无法重建。在这项工作中，我们提出了一种新的多快照DWI重建技术，该技术可以同时实现HR重建和局部组织模型估计，同时可以从包含不同扩散梯度子集的快照中进行重建，从而提高对患者运动和加速潜力的鲁棒性。我们的方法被形式化为具有缺失观测值的联合概率模型，从中自然出现了缺失快照，HR重建和通用组织模型之间的相互作用。我们通过综合模拟、模拟多快照场景和实时多快照成像来评估我们的方法。我们表明：(1)我们的联合方法最终提供了更好的HR重建和更好的组织模型估计；(2)缺失快照情况下的误差可以量化。我们新颖的多快照技术将提高大脑连接和微结构在体内的高空间表征。
Acquisition of a series of anisotropically oversampled acquisitions (so-called anisotropic “snapshots”) and reconstruction in the image space has recently been proposed to increase the spatial resolution in diffusion weighted imaging (DWI), providing a theoretical 8x acceleration at equal signal-to-noise ratio (SNR) compared to conventional dense k-space sampling. However, in most works, each DW image is reconstructed separately and the fact that the DW images constitute different views of the same anatomy is ignored. In addition, current approaches are limited by their inability to reconstruct a high resolution (HR) acquisition from snapshots with different subsets of diffusion gradients: an isotropic HR gradient image cannot be reconstructed if one of its anisotropic snapshots is missing, for example due to intra-scan motion, even if other snapshots for this gradient were successfully acquired. In this work, we propose a novel multi-snapshot DWI reconstruction technique that simultaneously achieves HR reconstruction and local tissue model estimation while enabling reconstruction from snapshots containing different subsets of diffusion gradients, providing increased robustness to patient motion and potential for acceleration. Our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We evaluate our approach with synthetic simulations, simulated multi-snapshot scenario andin vivomulti-snapshot imaging. We show that (1) our combined approach ultimately provides both better HR reconstruction and better tissue model estimation and (2) the error in the case of missing snapshots can be quantified. Our novel multi-snapshot technique will enable improved high spatial characterization of the brain connectivity and microstructurein vivo.