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dc.contributor.author | Fadnavis, Shreyas![]() |
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dc.contributor.author | Endres, S.C. (Stefan)![]() |
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dc.contributor.author | Wen, Qiuting![]() |
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dc.contributor.author | Wu, Yu-Chien![]() |
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dc.contributor.author | Cheng, Hu![]() |
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dc.contributor.author | Koudoro, Serge![]() |
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dc.contributor.author | Rane, Swati![]() |
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dc.contributor.author | Rokem, Ariel![]() |
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dc.contributor.author | Garyfallidis, Eleftherios![]() |
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dc.date.accessioned | 2022-08-10T05:56:02Z | |
dc.date.available | 2022-08-10T05:56:02Z | |
dc.date.issued | 2021-12-15 | |
dc.description.abstract | In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters. | en_US |
dc.description.department | Chemical Engineering | en_US |
dc.description.librarian | am2022 | en_US |
dc.description.sponsorship | The National Institute of Biomedical Imaging And Bioengineering (NIBIB) of the National Institutes of Health (NIH); University of Washington’s Royalty Research Fund; NIH grants; the German Research Foundation (DFG) and a grant from the Alfred P. Sloan Foundation and the Gordon & Betty Moore Foundation to the University of Washington eScience Institute Data Science Environment. | en_US |
dc.description.uri | http://www.frontiersin.org/Neuroscience | en_US |
dc.identifier.citation | Fadnavis, S., Endres, S., Wen, Q., Wu, Y.-C., Cheng, H., Koudoro, S., Rane, S., Rokem, A. & Garyfallidis, E. (2021) Bifurcated Topological Optimization for IVIM. Frontiers in Neuroscience15:779025. DOI: 10.3389/fnins.2021.779025. | en_US |
dc.identifier.issn | 1662-453X (online) | |
dc.identifier.other | 10.3389/fnins.2021.779025 | |
dc.identifier.uri | https://repository.up.ac.za/handle/2263/86736 | |
dc.language.iso | en | en_US |
dc.publisher | Frontiers Media | en_US |
dc.rights | © 2021 Fadnavis, Endres, Wen, Wu, Cheng, Koudoro, Rane, Rokem and Garyfallidis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). | en_US |
dc.subject | Simplicial homology | en_US |
dc.subject | Diffusion MRI | en_US |
dc.subject | Global optimization | en_US |
dc.subject | Separable non-linear least squares | en_US |
dc.subject | Variable projection | en_US |
dc.subject | Diffusion microstructure | en_US |
dc.subject | Magnetic resonance imaging (MRI) | en_US |
dc.subject | Intravoxel incoherent motion (IVIM) | en_US |
dc.title | Bifurcated topological optimization for IVIM | en_US |
dc.type | Article | en_US |