Wonil Lee1, Byungjai Kim1, Jongyeon Lee1, and HyunWook Park1
1KAIST, Daejeon, Korea, Republic of
1KAIST, Daejeon, Korea, Republic of
The trained DNN and the optimized b-values
by the proposed method quantified IVIM parameters more accurately than
combination of the conventional b-value optimization schemes with DNN fitting
method.
Figure 1.
Overall diagram of the proposed IVIM quantification method using DNN. a)
Diagram for training of DNN and for optimizing of b-values. b) Diagram for
quantification of IVIM parameters from the diffusion-weighted MRI signals.
Figure 3. Total parameter errors of the IVIM parameters
from the three estimation methods (MS-LSR fitting, Bayesian fitting, and DNN) when
the diffusion weighted images were acquired using the b-values optimized by
uniform sampling, Jalnefjord’s method, Zhang’s method, and the proposed method.
The red error bar indicates the standard deviation obtained from 50
simulations.