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A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

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Abstract

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.

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  1. Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN, 37203, USA

    Ke Xu & Hakmook Kang

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  1. Ke Xu
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The review was designed by Hakmook Kang. The first draft of the manuscript was written by Ke Xu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Hakmook Kang.

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Xu, K., Kang, H. A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis. Nucl Med Mol Imaging 58, 203–212 (2024). https://doi.org/10.1007/s13139-024-00845-6

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