Bayesian Estimation of CBF Measured by DSC-MRI in Patients with Moyamoya Disease: Comparison with
Adult
Algorithms
Bayes Theorem
Brain
/ diagnostic imaging
Cerebrovascular Circulation
/ physiology
Female
Humans
Image Interpretation, Computer-Assisted
/ methods
Magnetic Resonance Imaging
/ methods
Male
Middle Aged
Moyamoya Disease
/ diagnostic imaging
Neuroimaging
/ methods
Perfusion Imaging
/ methods
Positron-Emission Tomography
/ methods
Retrospective Studies
Sensitivity and Specificity
Young Adult
Journal
AJNR. American journal of neuroradiology
ISSN: 1936-959X
Titre abrégé: AJNR Am J Neuroradiol
Pays: United States
ID NLM: 8003708
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
04
04
2019
accepted:
19
08
2019
pubmed:
12
10
2019
medline:
1
7
2020
entrez:
12
10
2019
Statut:
ppublish
Résumé
CBF analysis of DSC perfusion using the singular value decomposition algorithm is not accurate in patients with Moyamoya disease. This study compared the Bayesian estimation of CBF against the criterion standard PET and singular value decomposition methods in patients with Moyamoya disease. Nineteen patients with Moyamoya disease (10 women; 22-52 years of age) were evaluated with both DSC and In qualitative assessments of DSC-CBF maps, Bayesian-CBF maps showed better visualization of decreased CBF on PET (sensitivity = 62.5%, specificity = 100%, positive predictive value = 100%, negative predictive value = 78.6%) than a block-circulant deconvolution method with a fixed noise cutoff and a block-circulant deconvolution method that adopts an oscillating noise cutoff for each voxel according to the strength of noise ( Compared with previously reported singular value decomposition algorithms, Bayesian analysis of DSC perfusion enabled better qualitative and quantitative assessments of CBF in patients with Moyamoya disease.
Sections du résumé
BACKGROUND AND PURPOSE
CBF analysis of DSC perfusion using the singular value decomposition algorithm is not accurate in patients with Moyamoya disease. This study compared the Bayesian estimation of CBF against the criterion standard PET and singular value decomposition methods in patients with Moyamoya disease.
MATERIALS AND METHODS
Nineteen patients with Moyamoya disease (10 women; 22-52 years of age) were evaluated with both DSC and
RESULTS
In qualitative assessments of DSC-CBF maps, Bayesian-CBF maps showed better visualization of decreased CBF on PET (sensitivity = 62.5%, specificity = 100%, positive predictive value = 100%, negative predictive value = 78.6%) than a block-circulant deconvolution method with a fixed noise cutoff and a block-circulant deconvolution method that adopts an oscillating noise cutoff for each voxel according to the strength of noise (
CONCLUSIONS
Compared with previously reported singular value decomposition algorithms, Bayesian analysis of DSC perfusion enabled better qualitative and quantitative assessments of CBF in patients with Moyamoya disease.
Identifiants
pubmed: 31601573
pii: ajnr.A6248
doi: 10.3174/ajnr.A6248
pmc: PMC6975120
doi:
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1894-1900Informations de copyright
© 2019 by American Journal of Neuroradiology.
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