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- Title
Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study.
- Authors
Javidi, Sam Sharifzadeh; Ahadi, Reza; Rad, Hamidreza Saligheh
- Abstract
Background: The intravoxel Incoherent Motion (IVIM) model extracts perfusion map and diffusion coefficient map using diffusion-weighted imaging. The main limitation of this model is inaccuracy in the presence of noise. Objective: This study aims to improve the accuracy of IVIM output parameters. Material and Methods: In this simulated and analytical study, the Kalman filter is applied to reject artifact and measurement noise. The proposed method puri- fies the diffusion coefficient from blood motion and noise, and then an artificial neural network is deployed in estimating perfusion parameters. Results: Based on the T-test results, however, the estimated parameters of the conventional method were significantly different from actual values, those of the proposed method were not substantially different from actual. The accuracy of f and D* also was improved by using Artificial Neural Network (ANN) and their bias was minimized to 4% and 12%, respectively. Conclusion: The proposed method outperforms the conventional method and is a promising technique, leading to reproducible and valid maps of D, f, and D*.
- Subjects
KALMAN filtering; ARTIFICIAL neural networks; DIFFUSION magnetic resonance imaging; DIFFUSION coefficients
- Publication
Journal of Biomedical Physics & Engineering, 2024, Vol 14, Issue 2, p141
- ISSN
2251-7200
- Publication type
Article
- DOI
10.31661/jbpe.v0i0.2104-1313