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DIMOND: DIffusion Model OptimizatioN with Deep Learning
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DOI：10.1002/advs.202307965.png)
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Abstract
En 中文
Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled DIMOND is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications. This study proposes a self-supervised and physics-informed deep learning framework entitled DIMOND to accelerate and improve the diffusion MRI microstructural model fitting. This work systematically and quantitatively evaluates DIMOND on DTI, DKI and NODDI. DIMOND's superior performance, self-supervised manner, and easy implementation and deployment increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications. image
Keywords:
diffusion MRI
non-linear optimization
microstructure imaging
self-supervised learning
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