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PurposeA 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.Theory and methodsIf 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).ResultsUsing 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.ConclusionUsing the sharable information among multicontrast images can enhance CS-PPI with tolerance to motions.

Original publication




Journal article


Magnetic resonance in medicine

Publication Date





523 - 535


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.


Carotid Arteries, Brain, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Image Enhancement, Sensitivity and Specificity, Reproducibility of Results, Algorithms, Data Compression