Bifurcated topological optimization for IVIM

dc.contributor.authorFadnavis, Shreyas
dc.contributor.authorEndres, S.C. (Stefan)
dc.contributor.authorWen, Qiuting
dc.contributor.authorWu, Yu-Chien
dc.contributor.authorCheng, Hu
dc.contributor.authorKoudoro, Serge
dc.contributor.authorRane, Swati
dc.contributor.authorRokem, Ariel
dc.contributor.authorGaryfallidis, Eleftherios
dc.date.accessioned2022-08-10T05:56:02Z
dc.date.available2022-08-10T05:56:02Z
dc.date.issued2021-12-15
dc.description.abstractIn 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.departmentChemical Engineeringen_US
dc.description.librarianam2022en_US
dc.description.sponsorshipThe 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.urihttp://www.frontiersin.org/Neuroscienceen_US
dc.identifier.citationFadnavis, 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.issn1662-453X (online)
dc.identifier.other10.3389/fnins.2021.779025
dc.identifier.urihttps://repository.up.ac.za/handle/2263/86736
dc.language.isoenen_US
dc.publisherFrontiers Mediaen_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.subjectSimplicial homologyen_US
dc.subjectDiffusion MRIen_US
dc.subjectGlobal optimizationen_US
dc.subjectSeparable non-linear least squaresen_US
dc.subjectVariable projectionen_US
dc.subjectDiffusion microstructureen_US
dc.subjectMagnetic resonance imaging (MRI)en_US
dc.subjectIntravoxel incoherent motion (IVIM)en_US
dc.titleBifurcated topological optimization for IVIMen_US
dc.typeArticleen_US

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