# SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning

arXiv: https://arxiv.org/abs/2002.01031 · HTML: https://mlsanity.com/paper/2002.01031/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Diffusion MRI with Machine Learning](https://mlsanity.com/paper/2402.00019/) (2024)
- [Diffusion Tensor Estimation with Transformer Neural Networks](https://mlsanity.com/paper/2201.05701/) (2022)
- [Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI](https://mlsanity.com/paper/2205.03210/) (2022)
- [Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study](https://mlsanity.com/paper/2304.00996/) (2023)
- [Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model](https://mlsanity.com/paper/2405.15830/) (2024)
- [TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds](https://mlsanity.com/paper/2410.22099/) (2024)
- [DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging](https://mlsanity.com/paper/2405.03159/) (2024)
- [TractoTransformer: Diffusion MRI Streamline Tractography using CNN and Transformer Networks](https://mlsanity.com/paper/2509.16429/) (2025)
- [Diffusion Tensor Estimation with Uncertainty Calibration](https://mlsanity.com/paper/2111.10847/) (2021)
- [Reliable Deep Diffusion Tensor Estimation: Rethinking the Power of Data-Driven Optimization Routine](https://mlsanity.com/paper/2409.02492/) (2024)
- [Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction](https://mlsanity.com/paper/2503.03475/) (2025)
- [A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography](https://mlsanity.com/paper/2504.18400/) (2025)
- [TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs](https://mlsanity.com/paper/2606.25545/) (2026)
- [A foundation for systematic analysis of transformers and RNNs for tractography](https://mlsanity.com/paper/2610.01894/) (2026)

## Abstract (arXiv)

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.