Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 0 0 2025 02 16 1127 1127 Somaye Jabari Department of Algorithms and Computation, Faculty of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran Amin Ghodousian Tehran University Reza Lashgari Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran Babak A. Ardekani Center for Advanced Brain Imaging and Neuromodulation, The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA 2024 10 14 2025 01 07 Purpose: This topic focuses on a comprehensive evaluation of various diffusion tensor imaging (DTI) estimation methods, such as linear least squares (LLS), weighted linear least squares (WLLS), iterative re-weighted linear least squares (IRLLS) and non-linear least squares (NLS). The article will explore how each method performs in terms of accuracy, efficiency in estimating the diffusion tensor and robustness against noise. Materials and Methods:  The study compares the methods using simulated diffusion-weighted MRI data. Time complexity and performance were evaluated across key metrics such as TRMSE, RMSE, MSD and ΔSNR. Results: The results of the study demonstrate that LLS and IRLLS consistently outperform other methods in terms of TRMSE, MSD and SNR, particularly in high-noise scenarios. NLS performs best in reducing RMSE but high noise causes it to fit to noise, so it is not robust. WLLS showed the weakest performance across all metrics. Conclusion: LLS and IRLLS provide a balance between accuracy and computational efficiency, making them practical for use in DTI analysis. https://fbt.tums.ac.ir/index.php/fbt/article/view/1127 https://fbt.tums.ac.ir/index.php/fbt/article/download/1127/493