Abstract #2861

Bayesian Reconstruction of DW-PROPELLER Images Using Joint Entropy

Jinhua Sheng¹, Dong Liang¹, Jing Tang², Donglai Huo³, Leslie Ying¹

¹Dept. of Electrical Engineering and Computer Science, Univ. of Wisconsin-Milwaukee, Milwaukee, WI, USA; ²Dept. of Radiology, the Johns Hopkins University, Baltimore, MD, USA; ³Keller Center for Imaging Inno,Barrow Neurological Institute, St. Joseph Hospital and Medical Center, Phoenix, AZ, USA

Turboprop has shown to reduce the artifacts of single-shot EPI for Diffusion-weighted imaging (DWI) but at the cost of longer acquisition. In this abstract, we propose a novel Bayesian reconstruction method to reduce the number of acquisitions without compromising the SNR. The method incorporates the information from high quality non-weighted images into the DWI reconstruction, with the joint entropy between the weighted and non-weighted image features as the prior. The method is shown to improve the DW image quality with a set of DW-PROPELLER brain data.