Optimized statistical parametric mapping for partial-volume-corrected amyloid positron emission tomography in patients with Alzheimer’s disease and Lewy body dementia

• Published in: Journal of the Korean Physical Society Vol. 70; no. 5; pp. 454 - 459
• Main Authors: Oh, Jungsu S., Kim, Jae Seung, Chae, Sun Young, Oh, Minyoung, Oh, Seung Jun, Cha, Seung Nam, Chang, Ho-Jong, Lee, Chong Sik, Lee, Jae Hong
• Format: Journal Article
• Language: English
• Published: Seoul The Korean Physical Society 01.03.2017; Springer Nature B.V; 한국물리학회
• Subjects: Cerebrospinal fluid; Dementia; Image degradation; Magnetic resonance imaging; Mapping; Mathematical and Computational Physics; Medical imaging; Particle and Nuclear Physics; Physics; Physics and Astronomy; Positron emission; Signal to noise ratio; Smoothing; Theoretical; Tomography; Transfer matrices; 물리학; Partial volume correction; Alzheimer’s disease; Statistical parametric mapping; Positron emission tomography; Lewy body dementia
• ISSN: 0374-4884; 1976-8524
• DOI: 10.3938/jkps.70.454

We present an optimized voxelwise statistical parametric mapping (SPM) of partial-volume (PV)-corrected positron emission tomography (PET) of 11 C Pittsburgh Compound B (PiB), incorporating the anatomical precision of magnetic resonance image (MRI) and amyloid β (A β ) burden-specificity of PiB PET. First, we applied region-based partial-volume correction (PVC), termed the geometric transfer matrix (GTM) method, to PiB PET, creating MRI-based lobar parcels filled with mean PiB uptakes. Then, we conducted a voxelwise PVC by multiplying the original PET by the ratio of a GTM-based PV-corrected PET to a 6-mm-smoothed PV-corrected PET. Finally, we conducted spatial normalizations of the PV-corrected PETs onto the study-specific template. As such, we increased the accuracy of the SPM normalization and the tissue specificity of SPM results. Moreover, lobar smoothing (instead of whole-brain smoothing) was applied to increase the signal-to-noise ratio in the image without degrading the tissue specificity. Thereby, we could optimize a voxelwise group comparison between subjects with high and normal A β burdens (from 10 patients with Alzheimer’s disease, 30 patients with Lewy body dementia, and 9 normal controls). Our SPM framework outperformed than the conventional one in terms of the accuracy of the spatial normalization (85% of maximum likelihood tissue classification volume) and the tissue specificity (larger gray matter, and smaller cerebrospinal fluid volume fraction from the SPM results). Our SPM framework optimized the SPM of a PV-corrected A β PET in terms of anatomical precision, normalization accuracy, and tissue specificity, resulting in better detection and localization of A β burdens in patients with Alzheimer’s disease and Lewy body dementia.