# SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI

arXiv: https://arxiv.org/abs/2411.06444 · HTML: https://mlsanity.com/paper/2411.06444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data](https://mlsanity.com/paper/2012.08931/) (2020)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues](https://mlsanity.com/paper/2102.02463/) (2021)
- [Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning](https://mlsanity.com/paper/2402.02704/) (2024)
- [Spherical Harmonic Residual Network for Diffusion Signal Harmonization](https://mlsanity.com/paper/1808.01595/) (2018)
- [Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes](https://mlsanity.com/paper/1706.06682/) (2017)
- [PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging](https://mlsanity.com/paper/2202.01494/) (2022)
- [Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework](https://mlsanity.com/paper/1704.01246/) (2017)
- [RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation](https://mlsanity.com/paper/2408.01944/) (2024)

## Built on by (most cited first, 1 of 1)

- [Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)](https://mlsanity.com/paper/2603.25977/) (2026)

## Abstract (arXiv)

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. In this paper, we propose a q-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on q-space sampling augmentation is introduced to fully explore the information between different gradient directions in q-space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying q-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks. To validate the effectiveness of the proposed method, we compared it with 7 state-of-the-art methods across 18 different q-space sampling schemes, demonstrating that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility.