DeepDTI Tutorial

Diffusion MRI physics-informed DeepDTI pipeline. The input is a single b = 0 image and six diffusion-weighted image (DWI) volumes sampled along optimized diffusion-encoding directions that minimize the condition number of the diffusion tensor transformation matrix (a) (with or without anatomical, e.g., T1-weighted and T2-weighted, image volumes). The output is the high-quality b = 0 image volume and six DWI volumes sampled along optimized diffusion-encoding directions transformed from the diffusion tensor fitted using all available b = 0 images and DWIs (b). A deep 3-dimensional convolutional neural network (CNN) comprised of stacked convolutional filters paired with ReLU activation functions (n = 10, k = 190, d = 3, c = 9, p = 7) is adopted to map the input image volumes to the residuals between the input and output image volumes (c). More advanced CNNs can be used to further improve permance.

Comprison of results. DeepDTI results recover improved signal-to-noise ratio, image sharpness, and detailed anatomical information buried in the noise in the raw data and blurred out in the BM4D-noised results. Quantitative comparison can be found in the NeuroImage paper of DeepDTI.

Comprison of tractography results. DeepDTI denoised data recover more white matter fibers. Quantitative comparison of reconstructed fiber tracts and tract-specific analysis can be found in the NeuroImage paper of DeepDTI.

s_DeepDTI_prepData.m

Step-by-step MATLAB tutorial for preparing the input and ground-truth data for convolutional neural network in DeepDTI. HTML file can be automatically generaged using command: publish('s_DeepDTI_prepData.m', 'html').

Utility functions

• amatrix.m: create diffusion tensor transformation matrix for given b-vectors

• bgr_colormap.m: create blue-gray-red color map for visualizaing residual images

• decompose_tensor.m: decompose diffusion tensors and derive DTI metrics

• rot3d.m: create 3D rotation matrix to rotate b-vectors

Output

• cnn_inout.mat: input and ground-truth data prepared for CNN

s_DeepDTI_trainCNN.py

Step-by-step Python tutorial for training the DnCNN in DeepDTI using data prepared using the s_DeepDTI_prepData.m script.

Utility functions

• dncnn.py: create DnCNN model

• qtlib.py: create custom loss functions to only include loss within brain mask, and extract blocks from whole brain volume data

Output

• deepdti_nb1_ep100.h5: DnCNN model trained for 100 epoches

• deepdti_nb1_ep100.mat: L2 losses for the training and validation

HCP data

The example data are provided by the WU-Minn-Oxford Hhuman Connectome Project (HCP) (open access data use term at https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms). Please acknowledge the source to the WU-Minn-Oxford HCP. The orginal data is available at https://www.humanconnectome.org/study/hcp-young-adult.

Refereces

[1] Tian Q, Bilgic B, Fan Q, Liao C, Ngamsombat C, Hu Y, Witzel T, Setsompop K, Polimeni JR, Huang SY. DeepDTI: High-fidelity Six-direction Diffusion Tensor Imaging using Deep Learning. NeuroImage, 2020; 219: 117017. [PDF]

[2] Tian Q, Li Z, Fan Q, Ngamsombat C, Hu Y, Liao C, Wang F, Setsompop K, Polimeni JR, Bilgic B, Huang SY. SRDTI: Deep learning-based super-resolution for diffusion tensor MRI. arXiv Preprint, 2021; arXiv: 2102.09069. [PDF]

[3] Tian Q. Oral Presentation. The 2020 Workshop on Data Sampling & Image Reconstruction of ISMRM. [Video]

[4] Tian Q. Power Pitch Oral Presentation. The 2020 Annual Scientific Meeting of ISMRM. [Video]