Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
Magn Reson Med. 2021 Jun;85(6):3256-3271. doi: 10.1002/mrm.28674. Epub 2021 Feb 3.
To jointly reconstruct highly undersampled multicontrast two-dimensional (2D) datasets through a low-rank Hankel tensor completion framework.
A multicontrast Hankel tensor completion (MC-HTC) framework is proposed to exploit the shareable information in multicontrast datasets with respect to their highly correlated image structure, common spatial support, and shared coil sensitivity for joint reconstruction. This is achieved by first organizing multicontrast k-space datasets into a single block-wise Hankel tensor. Subsequent low-rank tensor approximation via higher-order singular value decomposition (HOSVD) uses the image structural correlation by considering different contrasts as virtual channels. Meanwhile, the HOSVD imposes common spatial support and shared coil sensitivity by treating data from different contrasts as from additional k-space kernels. The missing k-space data are then recovered by iteratively performing such low-rank approximation and enforcing data consistency. This joint reconstruction framework was evaluated using multicontrast multichannel 2D human brain datasets (T -weighted, T -weighted, fluid-attenuated inversion recovery, and T -weighted-inversion recovery) of identical image geometry with random and uniform undersampling schemes.
The proposed method offered high acceleration, exhibiting significantly less residual errors when compared with both single-contrast SAKE (simultaneous autocalibrating and k-space estimation) and multicontrast J-LORAKS (joint parallel-imaging-low-rank matrix modeling of local k-space neighborhoods) low-rank reconstruction. Furthermore, the MC-HTC framework was applied uniquely to Cartesian uniform undersampling by incorporating a novel complementary k-space sampling strategy where the phase-encoding direction among different contrasts is orthogonally alternated.
The proposed MC-HTC approach presents an effective tensor completion framework to jointly reconstruct highly undersampled multicontrast 2D datasets without coil-sensitivity calibration.
通过低秩 Hankel 张量补全框架联合重建高度欠采样的多对比度二维（2D）数据集。
提出了一种多对比度汉克尔张量补全（MC-HTC）框架，以利用多对比度数据集在高度相关的图像结构、共同的空间支持和共享线圈灵敏度方面的可共享信息进行联合重建。这是通过首先将多对比度 k 空间数据集组织成单个块式汉克尔张量来实现的。随后通过高阶奇异值分解（HOSVD）进行低秩张量逼近，通过考虑不同对比度作为虚拟通道来利用图像结构相关性。同时，HOSVD 通过将来自不同对比度的数据视为额外的 k 空间核来施加共同的空间支持和共享线圈灵敏度。然后通过迭代执行这种低秩逼近并强制数据一致性来恢复缺失的 k 空间数据。使用具有相同图像几何形状的多对比度多通道 2D 人脑数据集（T 加权、T 加权、液体衰减反转恢复和 T 加权反转恢复）评估了这种联合重建框架，采用了随机和均匀欠采样方案。
与单对比度 SAKE（同时自动校准和 k 空间估计）和多对比度 J-LORAKS（局部 k 空间邻域的并行成像低秩矩阵建模的联合）低秩重建相比，所提出的方法提供了更高的加速比，表现出明显更低的残余误差。此外，通过引入一种新颖的互补 k 空间采样策略，MC-HTC 框架独特地应用于笛卡尔均匀欠采样，其中不同对比度的相位编码方向是正交交替的。
所提出的 MC-HTC 方法提供了一种有效的张量补全框架，可在无需线圈灵敏度校准的情况下联合重建高度欠采样的多对比度 2D 数据集。
