2 research outputs found
SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning
Purpose: To propose a deep learning-based reconstruction framework for
ultrafast and robust diffusion tensor imaging and fiber tractography. Methods:
We propose SuperDTI to learn the nonlinear relationship between
diffusion-weighted images (DWIs) and the corresponding tensor-derived
quantitative maps as well as the fiber tractography. Super DTI bypasses the
tensor fitting procedure, which is well known to be highly susceptible to noise
and motion in DWIs. The network is trained and tested using datasets from Human
Connectome Project and patients with ischemic stroke. SuperDTI is compared
against the state-of-the-art methods for diffusion map reconstruction and fiber
tracking. Results: Using training and testing data both from the same protocol
and scanner, SuperDTI is shown to generate fractional anisotropy and mean
diffusivity maps, as well as fiber tractography, from as few as six raw DWIs.
The method achieves a quantification error of less than 5% in all regions of
interest in white matter and gray matter structures. We also demonstrate that
the trained neural network is robust to noise and motion in the testing data,
and the network trained using healthy volunteer data can be directly applied to
stroke patient data without compromising the lesion detectability. Conclusion:
This paper demonstrates the feasibility of superfast diffusion tensor imaging
and fiber tractography using deep learning with as few as six DWIs directly,
bypassing tensor fitting. Such a significant reduction in scan time may allow
the inclusion of DTI into the clinical routine for many potential applications.Comment: 27 pages, 7 figures, 3 tables, 3 supporting figure
Rotation-Equivariant Deep Learning for Diffusion MRI
Convolutional networks are successful, but they have recently been
outperformed by new neural networks that are equivariant under rotations and
translations. These new networks work better because they do not struggle with
learning each possible orientation of each image feature separately. So far,
they have been proposed for 2D and 3D data. Here we generalize them to 6D
diffusion MRI data, ensuring joint equivariance under 3D roto-translations in
image space and the matching 3D rotations in -space, as dictated by the
image formation. Such equivariant deep learning is appropriate for diffusion
MRI, because microstructural and macrostructural features such as neural fibers
can appear at many different orientations, and because even
non-rotation-equivariant deep learning has so far been the best method for many
diffusion MRI tasks. We validate our equivariant method on multiple-sclerosis
lesion segmentation. Our proposed neural networks yield better results and
require fewer scans for training compared to non-rotation-equivariant deep
learning. They also inherit all the advantages of deep learning over classical
diffusion MRI methods. Our implementation is available at
https://github.com/philip-mueller/equivariant-deep-dmri and can be used off the
shelf without understanding the mathematical background.Comment: 24 pages, 8 figure