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Abstract #2610

PROMISE: Parallel Reconstruction with Optimized Acquisition for Multi-Contrast Imaging in the Context of Compressed Sensing

Enhao Gong1, Feng Huang2, Kui Ying3, Wenchuan Wu4, Shi Wang3, Chun Yuan4, 5, George Randy Duensing6

1Electrical Engineering, Stanford University, Stanford, CA, United States; 2Philips Healthcare, Shanghai, China; 3Department of Engineering Physics, Tsinghua University, Beijing, China; 4Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China; 5Department of Radiology, University of Washington, Seattle, WA, United States; 6Philips Healthcare, Gainesville, FL, United States

In this work, PROMISE (Parallel Reconstruction with Optimized acquisition for Multi-contrast Imaging in the context of compressed Sensing) is proposed to use manifold sharable information between multi-contrast scans for fast imaging. With the assumption that the same FOV is scanned for multi-contrast MRI, coil sensitivity maps, image structural information and optimal acquisition trajectory were extracted in seconds from previously acquired/reconstructed data to enhance the reconstruction of the following scans. Compared to previous work, PROMISE used more sharable information and resulted in lower artifact/noise level at higher reduction factors. Moreover, PROMISE is able to tolerate inter-scan motions better and is more clinically applicable.