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• W2340810669 abstract "Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b-values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b-values (3000 s · mm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>-2</sup> and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general." @default.
• W2340810669 created "2016-06-24" @default.
• W2340810669 creator A5027292126 @default.
• W2340810669 creator A5029228546 @default.
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• W2340810669 date "2016-10-01" @default.
• W2340810669 modified "2023-10-14" @default.
• W2340810669 title "Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI" @default.
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• W2340810669 doi "https://doi.org/10.1109/tmi.2016.2557580" @default.
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• W2340810669 hasPublicationYear "2016" @default.
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