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Sharifzadeh Javidi, Sam, Ahadi, Reza, Saligheh Rad, Hamidreza. (1403). Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study. سامانه مدیریت نشریات علمی, 14(2), 141-150. doi: 10.31661/jbpe.v0i0.2104-1313
Sam Sharifzadeh Javidi; Reza Ahadi; Hamidreza Saligheh Rad. "Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study". سامانه مدیریت نشریات علمی, 14, 2, 1403, 141-150. doi: 10.31661/jbpe.v0i0.2104-1313
Sharifzadeh Javidi, Sam, Ahadi, Reza, Saligheh Rad, Hamidreza. (1403). 'Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study', سامانه مدیریت نشریات علمی, 14(2), pp. 141-150. doi: 10.31661/jbpe.v0i0.2104-1313
Sharifzadeh Javidi, Sam, Ahadi, Reza, Saligheh Rad, Hamidreza. Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study. سامانه مدیریت نشریات علمی, 1403; 14(2): 141-150. doi: 10.31661/jbpe.v0i0.2104-1313

Improving Accuracy of Intravoxel Incoherent Motion Reconstruction using Kalman Filter in Combination with Neural Networks: A Simulation Study

Journal of Biomedical Physics and Engineering
دوره 14، شماره 2، تیر 2024، صفحه 141-150    اصل مقاله (1.53 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2104-1313
نویسندگان
Sam Sharifzadeh Javidi1، 2؛ Reza Ahadi3؛ Hamidreza Saligheh Rad* 1، 2
1Department of Physics and Medical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
2Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
3Department of Anatomy, Medicine School, Iran University of Medical Sciences, Tehran, Iran
چکیده
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 purifies 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*.

تازه های تحقیق

Hamidreza Saligheh Rad (Google Scholar)

کلیدواژه‌ها
Intravoxel Incoherent Motion؛ IVIM؛ Perfusion Imaging؛ Diffusion Magnetic Resonance Imaging؛ Kalman Filter؛ Neural Networks, Computer
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