Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas.

Abstract

PURPOSE:

A unique advantage of the brain positron emission tomography (PET) imaging is the ability to image different biological processes with different radiotracers. However, the diversity of the brain PET image patterns also makes their spatial normalization challenging. Since structural MR images are not always available in the clinical practice, this study proposed a PET-only spatial normalization method based on adaptive probabilistic brain atlas.

METHODS:

The proposed method (atlas-based method) consists of two parts, an adaptive probabilistic brain atlas generation algorithm, and a probabilistic framework for registering PET image to the generated atlas. To validate this method, the results of MRI-based method and template-based method (a widely used PET-only method) were treated as the gold standard and control, respectively. A total of 286 brain PET images, including seven radiotracers (FDG, PIB, FBB, AV-45, AV-1451, AV-133, [18F]altanserin) and four groups of subjects (Alzheimer disease, Parkinson disease, frontotemporal dementia, and healthy control), were spatially normalized using the three methods. The results were then quantitatively compared by using correlation analysis, meta region of interest (meta-ROI) standardized uptake value ratio (SUVR) analysis, and statistical parametric mapping (SPM) analysis.

RESULTS:

The Pearson correlation coefficient between the images computed by atlas-based method and the gold standard was 0.908 ± 0.005. The relative error of meta-ROI SUVR computed by atlas-based method was 2.12 ± 0.18%. Compared with template-based method, atlas-based method was also more consistent with the gold standard in SPM analysis.

CONCLUSION:

The proposed method provides a unified approach to spatially normalize brain PET images of different radiotracers accurately without MR images. A free MATLAB toolbox for this method has been provided.

• Authors: Tianhao Zhang, Binbin Nie, Hua Liu, Baoci Shan, Alzheimer’s Disease Neuroimaging Initiative
• Journal: European journal of nuclear medicine and molecular imaging (Eur J Nucl Med Mol Imaging) Vol. 49 Issue 9 Pg. 3073-3085 (07 2022) ISSN: 1619-7089 [Electronic] Germany
• PMID: 35258689 (Publication Type: Journal Article)
• Copyright: © 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Topics

• Algorithms
• Alzheimer Disease (diagnostic imaging)
• Brain (diagnostic imaging)
• Humans
• Image Processing, Computer-Assisted (methods)
• Magnetic Resonance Imaging (methods)
• Positron-Emission Tomography (methods)