IEEE Trans Biomed Eng. 2018 Oct;65(10):2219-2230. doi: 10.1109/TBME.2017.2787111. Epub 2017 Dec 25.
The purpose of this paper is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone.
Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient.
Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone.
Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features, which may allow more spatial coverage, higher spatial resolution, and shorter temporal footprint in the future.
本文旨在通过整合低秩性和压缩感知来加速心脏扩散张量成像（CDTI）。
扩散加权图像表现出变换稀疏性和低秩性。这些特性可以联合利用来加速 CDTI，特别是当相位图用于校正扩散方向之间的相位不一致时，从而增强低秩性。该方法在离体和体内进行了评估，并与仅使用低秩或稀疏约束的方法进行了比较。
与单独使用低秩或稀疏约束相比，该方法在更高的加速率下保留了更准确的螺旋角特征、心肌壁的贯穿连续体和平均扩散率，同时显著降低了偏差和提高了组内相关系数。
低秩性和压缩感知的结合促进了离体和体内 CDTI 的加速，与单独使用任何一种约束相比，提高了重建准确性。
与以前用于加速 CDTI 的方法相比，该方法有可能在保留肌纤维结构特征的同时达到更高的加速率，这可能允许在未来具有更大的空间覆盖范围、更高的空间分辨率和更短的时间足迹。
