DIFFnet

• The code is for reconstructing diffusion model parameters from various diffusion gradient schemes and b-values using deep learning (DIFFnet).
• last update : 2020. 02. 04
• The source data for training can be shared to academic institutions. Request should be sent to snu.list.software@gmail.com. For each request, individual approval from our institutional review board is required (i.e. takes time)

Reference

• DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues. J. Park, W. Jung, E-J. Choi, S-H. Oh, D. Shin, H. An, and J. Lee.
  https://arxiv.org/abs/2102.02463

Overview

Requirements

• Python 3.7

• TensorFlow-gpu 1.15

• NVIDIA GPU (CUDA 10.0)

• MATLAB 2019a

Data acquisition

• 3T MRI system (Tim Trio, SIEMENS, Erlangen, Germany) using a 32-channel phased-array head coil.

• DatasetDTI-A and DatasetNODDI-A were form below reference.

  • W. Jung et al., "Whole brain g-ratio mapping using myelin water imaging (MWI) and neurite orientation dispersion and density imaging (NODDI)," NeuroImage, vol. 182, pp. 379-388, Nov. 2018. https://www.sciencedirect.com/science/article/pii/S1053811917308017

• DatasetDTI-A (b = 700 s/mm^2 with 32 directinos)

• DatasetDTI-B (b = 1000 s/mm^2 with 30 directions)

• DatasetNODDI-A (b = 300 s/mm^2 with 8 directions; b = 700 s/mm^2 with 32 directions; b = 2000 s/mm^2 with 64 directions)

• DatasetNODDI-B (b = 300 s/mm^2 with 8 directions; b = 700 s/mm^2 with 30 directions; b = 2000 s/mm^2 with 60 directions)

Usage

Simulation

• Monte-Carlo diffusion simulation code to generate diffusion-weighted signals for training.

Training

• The source code for training DIFFnet. Simulated data from Monte-Carlo diffusion simulation has to be required.

Evaluation

• The source code for evaluation of the trained networks.
• In-vivo data and simulated data can be evaluated both.
• Networks generate diffusion model parameters.