S Hara1,2, Y Tanaka3, S Hayashi3,4, M Inaji3,4, T Maehara3, M Hori2, S Aoki2, K Ishii4, T Nariai3,4. 1. From the Department of Neurosurgery (S. Hara, Y.T., S. Hayashi, M.I., T.M., T.N.), Tokyo Medical and Dental University, Tokyo, Japan shara.nsrg@tmd.ac.jp. 2. Department of Radiology (S. Hara. M.H., S.A.), Juntendo University, Tokyo, Japan. 3. From the Department of Neurosurgery (S. Hara, Y.T., S. Hayashi, M.I., T.M., T.N.), Tokyo Medical and Dental University, Tokyo, Japan. 4. Research Team for Neuroimaging (S. Hayashi, M.I., K.I., T.N.), Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan.
Abstract
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 15O-gas PET within 60 days. DSC-CBF maps were created using Bayesian analysis and 3 singular value decomposition analyses (standard singular value decomposition, a block-circulant deconvolution method with a fixed noise cutoff, and a block-circulant deconvolution method that adopts an occillating noise cutoff for each voxel according to the strength of noise). Qualitative and quantitative analyses of the Bayesian-CBF and singular value decomposition-CBF methods were performed against 15O-gas PET and compared with each other. 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 (P < .03 for all except for specificity). Quantitative analysis of CBF showed that the correlation between Bayesian-CBF and PET-CBF values (ρ = 0.46, P < .001) was similar among the 3 singular value decomposition methods, and Bayesian analysis overestimated true CBF (mean difference, 47.28 mL/min/100 g). However, the correlation between CBF values normalized to the cerebellum was better in Bayesian analysis (ρ = 0.56, P < .001) than in the 3 singular value decomposition methods (P < .02). 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.
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 15O-gas PET within 60 days. DSC-CBF maps were created using Bayesian analysis and 3 singular value decomposition analyses (standard singular value decomposition, a block-circulant deconvolution method with a fixed noise cutoff, and a block-circulant deconvolution method that adopts an occillating noise cutoff for each voxel according to the strength of noise). Qualitative and quantitative analyses of the Bayesian-CBF and singular value decomposition-CBF methods were performed against 15O-gas PET and compared with each other. 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 (P < .03 for all except for specificity). Quantitative analysis of CBF showed that the correlation between Bayesian-CBF and PET-CBF values (ρ = 0.46, P < .001) was similar among the 3 singular value decomposition methods, and Bayesian analysis overestimated true CBF (mean difference, 47.28 mL/min/100 g). However, the correlation between CBF values normalized to the cerebellum was better in Bayesian analysis (ρ = 0.56, P < .001) than in the 3 singular value decomposition methods (P < .02). 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.
Authors: Mikkel Bo Hansen; Anna Tietze; Jayashree Kalpathy-Cramer; Elizabeth R Gerstner; Tracy T Batchelor; Leif Østergaard; Kim Mouridsen Journal: J Magn Reson Imaging Date: 2016-11-30 Impact factor: 4.813
Authors: S Hara; Y Tanaka; Y Ueda; S Hayashi; M Inaji; K Ishiwata; K Ishii; T Maehara; T Nariai Journal: AJNR Am J Neuroradiol Date: 2017-02-16 Impact factor: 3.825
Authors: Masashi Takasawa; P Simon Jones; Joseph V Guadagno; Soren Christensen; Tim D Fryer; Sally Harding; Jonathan H Gillard; Guy B Williams; Franklin I Aigbirhio; Elizabeth A Warburton; Leif Østergaard; Jean-Claude Baron Journal: Stroke Date: 2008-02-07 Impact factor: 7.914