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Fundamentals of Radiomics in Nuclear Medicine and Hybrid Imaging

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Basic Sciences of Nuclear Medicine

Abstract

Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are nuclear diagnostic imaging modalities for different diseases including cardiac failures and cancer. They hold the advantage of detecting disease-related biochemical and physiologic abnormalities in advance of anatomical changes, thus widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, nuclear imaging has benefited from early adoption of quantitative image analysis starting from simple standard uptake value (SUV) normalization to more advanced extraction of complex imaging uptake patterns; thanks chiefly to application of sophisticated image processing and machine learning algorithms. In this chapter, we discuss the application of image processing and machine/deep learning techniques to PET/SPECT imaging with special focus on the oncological radiotherapy domain as a case study. We will start from basic feature extraction to application in image-based outcome modeling in the radiomics and the deep learning fields.

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  1. Physics Division, Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA

    Lise Wei & Issam El Naqa

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    Magdy M. Khalil

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Wei, L., El Naqa, I. (2021). Fundamentals of Radiomics in Nuclear Medicine and Hybrid Imaging. In: Khalil, M.M. (eds) Basic Sciences of Nuclear Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-65245-6_17

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