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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23854
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dc.contributor.authorSpinner, Georg Ralph-
dc.contributor.authorFederau, Christian-
dc.contributor.authorKozerke, Sebastian-
dc.date.accessioned2022-01-07T12:56:26Z-
dc.date.available2022-01-07T12:56:26Z-
dc.date.issued2021-10-
dc.identifier.issn1361-8415de_CH
dc.identifier.issn1361-8423de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23854-
dc.description.abstractThe intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofMedical Image Analysisde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectAcute strokede_CH
dc.subjectBayesian inferencede_CH
dc.subjectCancerde_CH
dc.subjectIntravoxel incoherent motion imagingde_CH
dc.subjectAlgorithmde_CH
dc.subjectBayes Theoremde_CH
dc.subjectBrainde_CH
dc.subjectDiffusion Magnetic Resonance Imagingde_CH
dc.subjectHumande_CH
dc.subjectMagnetic Resonance Imagingde_CH
dc.subjectMotionde_CH
dc.subjectNeoplasmsde_CH
dc.subjectStrokede_CH
dc.subject.ddc510: Mathematikde_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleBayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute strokede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1016/j.media.2021.102144de_CH
dc.identifier.doi10.21256/zhaw-23854-
dc.identifier.pmid34261009de_CH
zhaw.funding.euNode_CH
zhaw.issue102144de_CH
zhaw.originated.zhawNode_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume73de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf186588, 173952de_CH
zhaw.webfeedMedical Image Analysis & Data Modelingde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Spinner, G. R., Federau, C., & Kozerke, S. (2021). Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute stroke. Medical Image Analysis, 73(102144). https://doi.org/10.1016/j.media.2021.102144
Spinner, G.R., Federau, C. and Kozerke, S. (2021) ‘Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute stroke’, Medical Image Analysis, 73(102144). Available at: https://doi.org/10.1016/j.media.2021.102144.
G. R. Spinner, C. Federau, and S. Kozerke, “Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute stroke,” Medical Image Analysis, vol. 73, no. 102144, Oct. 2021, doi: 10.1016/j.media.2021.102144.
SPINNER, Georg Ralph, Christian FEDERAU und Sebastian KOZERKE, 2021. Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute stroke. Medical Image Analysis. Oktober 2021. Bd. 73, Nr. 102144. DOI 10.1016/j.media.2021.102144
Spinner, Georg Ralph, Christian Federau, and Sebastian Kozerke. 2021. “Bayesian Inference Using Hierarchical and Spatial Priors for Intravoxel Incoherent Motion MR Imaging in the Brain : Analysis of Cancer and Acute Stroke.” Medical Image Analysis 73 (102144). https://doi.org/10.1016/j.media.2021.102144.
Spinner, Georg Ralph, et al. “Bayesian Inference Using Hierarchical and Spatial Priors for Intravoxel Incoherent Motion MR Imaging in the Brain : Analysis of Cancer and Acute Stroke.” Medical Image Analysis, vol. 73, no. 102144, Oct. 2021, https://doi.org/10.1016/j.media.2021.102144.


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