Lu Wang1, Zhen Xing2, Jian Wu1, Qinqin Yang1, Congbo Cai1, Shuhui Cai 1, Zhong Chen1, and Dairong Cao2
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
1Department of Electronic Science, Xiamen University, Xiamen, Fujian, China, 2Department of Radiology, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
We proposed a deep neural network-based reconstruction method with
synthetic training data for IVIM imaging and extend it to hybrid IVIM-DKI
(diffusion kurtosis imaging) model fitting. Experimental
results show that our method owns prominent performance with a remarkably short time.
Figure 2. Reconstructed parametric maps (D, f
and D*) of IVIM model
using proposed method, least square and Bayesian algorithm. (a) A 59-year-old man with pathologically confirmed oligodendroglioma. The lesion hardly enhanced
on postcontrast T1 weighted image. (b) A 57-year-old man with
pathologically confirmed IDH wild-type astrocytoma. The lesion shows remarkable
enhancement on postcontrast T1 weighted image. (c) A 49-year-old man with pathologically confirmed IDH-mutated
astrocytoma. The lesion hardly enhanced on postcontrast T1
weighted image.
Figure 4. RMSE of the estimated IVIM parameters
(D, f and D*) for
proposed method, least square (LS), and Bayesian algorithm (BP) under SNR from
10 to 80 dB.
