Gong Enhao, Huang Feng, Ying Kui, Wu Wenchuan, Wang Shi, Yuan Chun
Magnetic Resonance System Research Lab, Department of Electrical Engineering, Stanford University, Stanford, California, USA; Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
Magn Reson Med. 2015 Feb;73(2):523-35. doi: 10.1002/mrm.25142. Epub 2014 Feb 25.
A typical clinical MR examination includes multiple scans to acquire images with different contrasts for complementary diagnostic information. The multicontrast scheme requires long scanning time. The combination of partially parallel imaging and compressed sensing (CS-PPI) has been used to reconstruct accelerated scans. However, there are several unsolved problems in existing methods. The target of this work is to improve existing CS-PPI methods for multicontrast imaging, especially for two-dimensional imaging.
If the same field of view is scanned in multicontrast imaging, there is significant amount of sharable information. It is proposed in this study to use manifold sharable information among multicontrast images to enhance CS-PPI in a sequential way. Coil sensitivity information and structure based adaptive regularization, which were extracted from previously reconstructed images, were applied to enhance the following reconstructions. The proposed method is called Parallel-imaging and compressed-sensing Reconstruction Of Multicontrast Imaging using SharablE information (PROMISE).
Using L1 -SPIRiT as a CS-PPI example, results on multicontrast brain and carotid scans demonstrated that lower error level and better detail preservation can be achieved by exploiting manifold sharable information. Besides, the privilege of PROMISE still exists while there is interscan motion.
Using the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions.
典型的临床磁共振成像（MR）检查包括多次扫描，以获取具有不同对比度的图像，从而获得互补的诊断信息。多对比度成像方案需要较长的扫描时间。部分并行成像与压缩感知（CS-PPI）的结合已被用于重建加速扫描。然而，现有方法中存在几个未解决的问题。这项工作的目标是改进现有的用于多对比度成像的CS-PPI方法，特别是二维成像方法。
如果在多对比度成像中扫描相同的视野，则存在大量可共享的信息。本研究提出以顺序方式利用多对比度图像之间的流形可共享信息来增强CS-PPI。从先前重建的图像中提取的线圈灵敏度信息和基于结构的自适应正则化被应用于增强后续的重建。所提出的方法称为使用可共享信息的多对比度成像并行成像与压缩感知重建（PROMISE）。
以L1-SPIRiT作为CS-PPI的示例，多对比度脑部和颈动脉扫描的结果表明，通过利用流形可共享信息可以实现更低的误差水平和更好的细节保留。此外，在存在扫描间运动的情况下，PROMISE的优势仍然存在。
利用多对比度图像之间的可共享信息可以增强CS-PPI并对运动具有耐受性。
