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|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|
|Published in:||Medical Image Analysis|
|Publisher / Ed. Institution:||Elsevier|
|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.|
|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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|2021_Spinner-etal_Bayesian-inference-MR-imaging-brain.pdf||5.18 MB||Adobe PDF|
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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, 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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