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.