In prostate MRI, tissue alignment between T2w and DWI can be challenging due to elastic distortions in DWI induced by fast switching of gradients. In this work, we propose a joint approach to perform registration between DWI and T2w on the prostate data, by considering both image intensity and prostate gland shape information. After rigid alignment between segmented prostate masks, surface points on the prostate masks were extracted and Gaussian mixture model was used to build shape correspondence. The shape information was integrated into mutual-information based deformable registration to constrain undesired distortions. The proposed framework was compared with other strategies and demonstrated the best registration accuracy (DICE = 0.91 between T2 and DWI based prostate masks).
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