RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation
<|reference_start|>RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation: Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications.<|reference_end|>
@article{xiao2024robnoddi:,
    title={RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under
  Continuous Representation},
    author={Taohui Xiao, Jian Cheng, Wenxin Fan, Jing Yang, Cheng Li, Enqing Dong,
  and Shanshan Wang},
    journal={arXiv preprint arXiv:2408.01944},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01944},
    primaryClass={cs.CV eess.IV}
}