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Abstract #3405

DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient directions and b-values

Juhyung Park1, Woojin Jung1, Eun-jung Choi1, Se-Hong Oh2, Dongmyung Shin1, Hongjun An1, and Jongho Lee1
1Seoul National University, Seoul, Korea, Republic of, 2Hankuk University of Foreign Studies, Gyeonggi-do, Korea, Republic of

A deep neural network, referred to as DIFFnet, was developed to reconstruct the diffusion parameters from data with reasonable b-value and gradient scheme (gradient direction and the number of gradients). For the generalization, Qmatrix was proposed via the projection and quantization of q-space. DIFFnet was trained by simulated datasets with various b-values and gradient schemes. Two DIFFnets, one for DTI and the other for NODDI were developed. DIFFnet successfully reconstructs the diffusion parameter maps of two in-vivo datasets with different b-values and gradient schemes.

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