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

被引：0
作者：
Javidi S.S. [1 ,2 ]
Ahadi R. [3 ]
Rad H.S. [1 ,2 ]
机构：
[1] Department of Physics and Medical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran
[2] Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran
[3] Department of Anatomy, Medicine School, Iran University of Medical Sciences, Tehran
来源：
Journal of Biomedical Physics and Engineering | / 14卷 / 02期
关键词：
Diffusion Magnetic Resonance Imaging; Intravoxel Incoherent Motion; IVIM; Kalman Filter; Neural Networks; Computer; Perfusion Imaging;
D O I：
10.31661/jbpe.v0i0.2104-1313
中图分类号：
学科分类号：
摘要：
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*. © Journal of Biomedical Physics and Engineering.
引用
收藏
页码：141 / 150
页数：9
相关论文
共 28 条
  • [1] Basser PJ., Inferring microstructural features and the physiological state of tissues from diffusion-weighted images, NMR Biomed, 8, 7-8, pp. 333-344, (1995)
  • [2] Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M., MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders, Radiology, 161, 2, pp. 401-407, (1986)
  • [3] Le Bihan D., What can we see with IVIM MRI?, Neuroimage, 187, pp. 56-67, (2019)
  • [4] Bley TA, Wieben O, Uhl M., Diffusion-weighted MR imaging in musculoskeletal radiology: applications in trauma, tumors, and inflammation, Magn Reson Imaging Clin N Am, 17, 2, pp. 263-275, (2009)
  • [5] Ni X, Wang W, Li X, Li Y, Chen J, Shi D, Et al., Utility of Diffusion-Weighted Imaging for Guiding Clinical Management of Patients With Kidney Transplant: A Prospective Study, J Magn Reson Imaging, 52, 2, pp. 565-574, (2020)
  • [6] Huang HM., Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion-weighted MRI, NMR Biomed, 33, 4, (2020)
  • [7] Cheng ZY, Feng YZ, Hu JJ, Lin QT, Li W, Qian L, Et al., Intravoxel incoherent motion imaging of the kidney: The application in patients with hyperuricemia, J Magn Reson Imaging, 51, 3, pp. 833-840, (2020)
  • [8] Beyhan M, Sade R, Koc E, Adanur S, Kantarci M., The evaluation of prostate lesions with IVIM DWI and MR perfusion parameters at 3T MRI, Radiol Med, 124, 2, pp. 87-93, (2019)
  • [9] Surer E, Rossi C, Becker AS, Finkenstaedt T, Wurnig MC, Valavanis A, Et al., Cardiac-gated intravoxel incoherent motion diffusion-weighted magnetic resonance imaging for the investigation of intracranial cerebrospinal fluid dynamics in the lateral ventricle: a feasibility study, Neuroradiology, 60, 4, pp. 413-419, (2018)
  • [10] Stejskal EO, Tanner JE., Spin Diffusion Measurements: Spin Echoes in the Presence of a Time Dependent Field Gradient, The Journal of Chemical Physics, 42, 1, pp. 288-292, (1965)
123