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12 March 2010 Improving RESTORE for robust diffusion tensor estimation: a simulation study
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Diffusion tensor magnetic resonance imaging (DT-MRI) is increasingly used in clinical research and applications for its ability to depict white matter tracts and for its sensitivity to microstructural and architectural features of brain tissue. However, artifacts are common in clinical DT-MRI acquisitions. Signal perturbations produced by such artifacts can be severe and neglecting to account for their contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier Rejection (RESTORE) has been demonstrated to be an effective method for improving tensor estimation on a voxel-by-voxel basis in the presence of artifactual data points in diffusion weighted images. Despite the very good performance of the RESTORE algorithm, there are some limitations and opportunities for improvement. Instabilities in tensor estimation using RESTORE have been observed in clinical human brain data. Those instabilities can come from the intrinsic high frequency spin inflow effects in non-DWIs or from excluding too many data points from the fitting. This paper proposes several practical constraints to the original RESTORE method. Results from Monte Carlo simulation indicate that the improved RESTORE method reduces the instabilities in tensor estimation observed from the original RESTORE method.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lin-Ching Chang "Improving RESTORE for robust diffusion tensor estimation: a simulation study", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762328 (12 March 2010);

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