Reliable Estimation of the Intra-Voxel Incoherent Motion Parameters of Brain Diffusion Imaging Using θ-Teaching-Learning-Based Optimization
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Date
2020-04
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Referee
Mark
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Volume Title
Publisher
Společnost pro radioelektronické inženýrství
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Abstract
Intra-voxel incoherent motion (IVIM) imaging can characterize diffusion and perfusion of tissues. Traditionally, the least-square method has been used to determine IVIM parameters consisting of pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and the micro-vascular volume fraction (f). This paper proposes an accurate estimation method for IVIM parameters in human brain tissues using θ-teaching-learning-based-optimization (θ-TLBO). θ-TLBO as an evolutionary algorithm provides high quality solutions for parameter estimations in curve fitting problems. Evaluation of the proposed method was performed on simulated data with different levels of noise and experimental data. The estimated parameters were compared with the results of TLBO and three conventional algorithms: Segmented-Unconstrained (“SU”), Segmented-Constrained (“SC”) and “Full”. The results show that the proposed θ-TLBO has higher accuracy, precision and robustness than other methods in estimating parameters of simulated and experimental data in human brain images especially in low SNR images according to analysis of variance (ANOVA), coefficient of variation (CV), relative bias and relative root mean square errors.
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Citation
Radioengineering. 2020 vol. 29, č. 1, s. 251-258. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2019/20_01_0251_0258.pdf
https://www.radioeng.cz/fulltexts/2019/20_01_0251_0258.pdf
Document type
Peer-reviewed
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en