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Arterial spin labeling-based Z-maps have high specificity and positive predictive value for neurodegenerative dementia compared to FDG-PET.
- Publication Year :
- 2017
-
Abstract
- OBJECTIVE: Cerebral perfusion analysis based on arterial spin labeling (ASL) MRI has been proposed as an alternative to FDG-PET in patients with neurodegenerative disease. Z-maps show normal distribution values relating an image to a database of controls. They are routinely used for FDG-PET to demonstrate disease-specific patterns of hypometabolism at the individual level. This study aimed to compare the performance of Z-maps based on ASL to FDG-PET. METHODS: Data were combined from two separate sites, each cohort consisting of patients with Alzheimer's disease (n = 18 + 7), frontotemporal dementia (n = 12 + 8) and controls (n = 9 + 29). Subjects underwent pseudocontinuous ASL and FDG-PET. Z-maps were created for each subject and modality. Four experienced physicians visually assessed the 166 Z-maps in random order, blinded to modality and diagnosis. RESULTS: Discrimination of patients versus controls using ASL-based Z-maps yielded high specificity (84%) and positive predictive value (80%), but significantly lower sensitivity compared to FDG-PET-based Z-maps (53% vs. 96%, p < 0.001). Among true-positive cases, correct diagnoses were made in 76% (ASL) and 84% (FDG-PET) (p = 0.168). CONCLUSION: ASL-based Z-maps can be used for visual assessment of neurodegenerative dementia with high specificity and positive predictive value, but with inferior sensitivity compared to FDG-PET. KEY POINTS: • ASL-based Z-maps yielded high specificity and positive predictive value in neurodegenerative dementia. • ASL-based Z-maps had significantly lower sensitivity compared to FDG-PET-based Z-maps. • FDG-PET might be reserved for ASL-negative cases where clinical suspicion persists. • Findings were similar at two study sites.
Details
- Database :
- OAIster
- Notes :
- application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1233861787
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1007.s00330-017-4784-1