Weizman Lior, Eldar Yonina C, Ben Bashat Dafna
Department of Electrical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel.
Functional Brain Center, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 64239, Israel.
Med Phys. 2015 Sep;42(9):5195-208. doi: 10.1118/1.4928148.
Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans.
The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies. Since similarity is not guaranteed, sampling and reconstruction are adjusted during acquisition to match the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted reconstruction. In adaptive sampling, k-space sampling locations are optimized during acquisition. Weighted reconstruction uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. The approach was tested on 2D and 3D MRI scans of patients with brain tumors.
The longitudinal adaptive compressed sensing MRI (LACS-MRI) scheme provides reconstruction quality which outperforms other CS-based approaches for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution. Compared with data sampled at the Nyquist rate, LACS-MRI exhibits signal-to-error ratio (SER) of 24.8 dB with undersampling factor of 16.6 in 3D MRI.
The authors presented an adaptive method for image reconstruction utilizing similarity of scans in longitudinal MRI studies, where possible. The proposed approach can significantly reduce scanning time in many applications that consist of disease follow-up and monitoring of longitudinal changes in brain MRI.
在许多临床情况下都会进行重复的脑部磁共振成像（MRI）扫描，例如对肿瘤患者的随访以及治疗反应评估。在本文中，作者展示了一种利用患者先前扫描结果来加速重复MRI扫描的方法。
所提出的方法利用了纵向MRI研究中重复扫描可能存在的相似性。由于相似性无法保证，因此在采集过程中会调整采样和重建，以匹配扫描之间的实际相似性。基线MR扫描在采样阶段通过自适应采样加以利用，在重建阶段则采用加权重建。在自适应采样中，k空间采样位置在采集过程中进行优化。加权重建在恢复过程中将稀疏域中非零系数的位置用作先验信息。该方法在脑肿瘤患者的二维和三维MRI扫描上进行了测试。
纵向自适应压缩感知MRI（LACS-MRI）方案提供的重建质量优于其他基于压缩感知的快速MRI方法。展示了脑肿瘤患者的实例，显示出空间分辨率得到改善。与以奈奎斯特速率采样的数据相比，LACS-MRI在三维MRI中欠采样因子为16.6时的信噪误比（SER）为24.8 dB。
作者提出了一种在可能的情况下利用纵向MRI研究中扫描相似性进行图像重建的自适应方法。所提出的方法在许多包括疾病随访和脑MRI纵向变化监测的应用中可显著减少扫描时间。
