Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23854
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain : analysis of cancer and acute stroke
Authors: Spinner, Georg Ralph
Federau, Christian
Kozerke, Sebastian
et. al: No
DOI: 10.1016/j.media.2021.102144
10.21256/zhaw-23854
Published in: Medical Image Analysis
Volume(Issue): 73
Issue: 102144
Issue Date: Oct-2021
Publisher / Ed. Institution: Elsevier
ISSN: 1361-8415
1361-8423
Language: English
Subjects: Acute stroke; Bayesian inference; Cancer; Intravoxel incoherent motion imaging; Algorithm; Bayes Theorem; Brain; Diffusion Magnetic Resonance Imaging; Human; Magnetic Resonance Imaging; Motion; Neoplasms; Stroke
Subject (DDC): 510: Mathematics
616: Internal medicine and diseases
Abstract: The 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/23854
Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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