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Abstract #0949

IMPULSED model-based brain tumor microstructural parameter estimation with deep neural network

Jian Wu1, Taishan Kang2, Xinran Chen1, Lina Xu1, Jianzhong Lin2, Zhigang Wu3, Tianhe Yang2, Congbo Cai1, and Shuhui Cai1
1Xiamen University, Xiamen, China, 2Zhongshan Hospital Afflicated to Xiamen University, Xiamen, China, 3MSC Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China


This study assesses the feasibility of training a convolutional neural network (CNN) for IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model fitting to diffusion-weighted (DW) data and evaluates its performance on a brain tumor (poorly differentiated adenocarcinoma) patient data directly acquired from clinical MR scanner. Comparisons were made with the results calculated from the non-linear least squares (NLLS) algorithm. More accurate and robust results were obtained by our CNN method, with processing speed several orders of magnitude faster than the reference method (from 5 min to 1 s).

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