SwinDTI: swin transformer-based generalized fast estimation of diffusion tensor parameters from sparse data

Diffusion tensor imaging (DTI) is a non-invasive technique for analyzing the movement of water in the brain. However, the precision of measurements required for tracking white matter pathways can lead to long scan times, which can be challenging for some patient populations such as pediatric patient...

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Bibliographic Details
Published in:Neural computing & applications 2024-02, Vol.36 (6), p.3179-3196
Main Authors: Tiwari, Abhishek, Singh, Rajeev Kumar, Shigwan, Saurabh J.
Format: Article
Language:English
Subjects:
Anisotropy
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Deep learning
Diffusion rate
Diffusivity
Image Processing and Computer Vision
Kurtosis
Magnetic resonance imaging
Mathematical models
Neural networks
Original Article
Parameter estimation
Parameters
Probability and Statistics in Computer Science
Teaching methods
Tensors
Three dimensional models
Transformers
Young adults
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Summary:Diffusion tensor imaging (DTI) is a non-invasive technique for analyzing the movement of water in the brain. However, the precision of measurements required for tracking white matter pathways can lead to long scan times, which can be challenging for some patient populations such as pediatric patients. To address this issue, researchers have been experimenting with deep learning techniques for faster estimation of DTI parameters, which are helpful in neurological diagnosis, of diffusion-weighted images. Our proposed solution is a transformer neural network-based approach for fast estimation of diffusion tensor parameters using sparse measurements. While there have been attempts to address this problem, our proposed model handles both scalable and generalized estimation of DTI parameters using multiple sparse measurements. Through experimentation on the Human Connectome Project (HCP) Young Adult benchmark dataset, our proposed model demonstrated state-of-the-art results in terms of fractional anisotropy (FA), axial diffusivity (AD), and mean diffusivity (MD) when compared to traditional linear least square (LLS) fitting and 3D U-Net model with 16 × 16 × 16 input size (3D U-Net16).
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09206-4