IEEE Trans Med Imaging. 2019 Oct;38(10):2303-2313. doi: 10.1109/TMI.2019.2908140. Epub 2019 Mar 28.
We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SmooThness Regularization on Manifolds (SToRM) scheme that exploits the global manifold structure of images in the dataset. Since the manifold structure of the patches varies depending on the spatial location and is often considerably simpler than the global one, this approach significantly reduces the data demand, facilitating the recovery from shorter scans. Since the navigator-based estimation of manifold structure pursued in SToRM is not feasible in this setting, a reformulation of SToRM is introduced. Specifically, the regularization term of the cost function involves the sum of robust distances between images sub-patches in the dataset. The optimization algorithm alternates between updating the images and estimating the manifold structure of the image patches. The utility of the proposed scheme is demonstrated in the context of in-vivo prospective free-breathing cardiac CINE MRI imaging with multichannel acquisitions and simulated phantoms. The new framework facilitates a reduction in scan time, as compared to the SToRM strategy.
我们引入了一种局部流形正则化方法，从高度欠采样的测量中恢复动态 MRI 数据。所提出的方案依赖于在自由呼吸心脏 MRI 数据集的同一空间位置的局部图像块的流形结构; 这种方法是利用数据集图像的全局流形结构的 SmooThness Regularization on Manifolds (SToRM) 方案的推广。由于补丁的流形结构取决于空间位置，并且通常比全局结构简单得多，因此这种方法大大减少了数据需求，方便了从更短的扫描中恢复。由于 SToRM 中基于导航仪的流形结构估计在这种情况下不可行，因此引入了 SToRM 的重新表述。具体来说，代价函数的正则化项涉及数据集中小图像块之间鲁棒距离的总和。优化算法在更新图像和估计图像块的流形结构之间交替进行。所提出的方案在具有多通道采集和模拟体模的体内前瞻性自由呼吸心脏 CINE MRI 成像的背景下证明了其有效性。与 SToRM 策略相比，新框架有助于减少扫描时间。
