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

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

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.

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Data availability

The data used in the current study come from five public datasets: ADNI (Alzheimer’s Disease Neuroimaging Initiative; http://adni.loni.usc.edu), AIBL (The Australian Imaging, Biomarker and Lifestyle Study of Aging; http://www.AIBL.csiro.au), ICBM (International Consortium for Brain Mapping), NIFD (Neuroimaging in Frontotemporal Dementia; https://memory.ucsf.edu/research-trials/research/allftd), and PPMI (Parkinson’s Progression Markers Initiative; https://www.ppmi-info.org). The codes and templates for this study are available at https://github.com/IHEP-Brain-Imaging/Spatial-Normalization-of-Brain-PET-Images.

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Acknowledgements

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Funding

This work was financially supported by the China Postdoctoral Science Foundation (2021T140668) and National Natural Science Foundation of China (11975249, 81771923, 12175268).

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Authors and Affiliations

  1. Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Tianhao Zhang, Binbin Nie, Hua Liu & Baoci Shan

  2. School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Tianhao Zhang, Binbin Nie, Hua Liu & Baoci Shan

Authors
  1. Tianhao Zhang
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  2. Binbin Nie
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  3. Hua Liu
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  4. Baoci Shan
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Alzheimer’s Disease Neuroimaging Initiative

Contributions

Data processing and analysis were performed by Tianhao Zhang and Hua Liu. The design of the algorithm was performed by Tianhao Zhang, Baoci Shan, and Binbin Nie. The first draft of the manuscript was written by Tianhao Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Baoci Shan.

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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

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Zhang, T., Nie, B., Liu, H. et al. Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas. Eur J Nucl Med Mol Imaging 49, 3073–3085 (2022). https://doi.org/10.1007/s00259-022-05752-6

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  • DOI: https://doi.org/10.1007/s00259-022-05752-6

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