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Abstract #0556

Registration and quantification net (RQnet) for IVIM-DKI analysis

Wonil Lee1, Giyong Choi1, Jongyeon Lee1, and HyunWook Park1
1Electrical Engineering, KAIST, Daejeon, Korea, Republic of


Accurate alignment of multiple diffusion-weighted images must be preceded to predict accurate diffusion parameters. A number of registration approaches have been studied (1,2). However, most of them minimize the dissimilarity between diffusion weighted image and a reference, which can cause errors because the characteristics of the images are different. In order to accurately investigate diffusion, perfusion, and kurtosis parameters using hybrid IVIM-DKI model, a deep learning network is proposed as an end-to-end fashion. This method is entirely unsupervised learning, which does not require reference image for registration and the labeled IVIM-DKI parameters for registration and quantification.

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