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The Complexity and Fractal Geometry of Nuclear Medicine Images

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Abstract

Irregularity in shape and behavior is the main feature of every anatomical system, including human organs, tissues, cells, and sub-cellular entities. It has been shown that this property cannot be quantified by means of the classical Euclidean geometry, which is only able to describe regular geometrical objects. In contrast, fractal geometry has been widely applied in several scientific fields. This rapid growth has also produced substantial insights in the biomedical imaging. Consequently, particular attention has been given to the identification of pathognomonic patterns of “shape” in anatomical entities and their changes from natural to pathological states. Despite the advantages of fractal mathematics and several studies demonstrating its applicability to oncological research, many researchers and clinicians remain unaware of its potential. Therefore, this review aims to summarize the complexity and fractal geometry of nuclear medicine images.

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Acknowledgments

The “Michele Rodriguez” Foundation is acknowledged for the scientific support.

Funding

The Italian Association for Research on Cancer (AIRC—Associazione Italiana per la Ricerca sul Cancro) provided financial support for the research with the grant no. 18923.

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Authors and Affiliations

  1. Department of Immunology and Inflammation, Humanitas Clinical and Research Hospital, Via Manzoni 56 - Rozzano, 20089, Milan, Italy

    Fabio Grizzi & Dorina Qehajaj

  2. Humanitas University, Via Rita Levi Montalcini, Pieve Emanuele, 20090, Milan, Italy

    Fabio Grizzi

  3. Department of Nuclear Medicine, Humanitas Clinical and Research Hospital, Via Manzoni 56 - Rozzano, 20089, Milan, Italy

    Angelo Castello & Egesta Lopci

  4. “Michele Rodriguez” Foundation, Via Ludovico di Breme, 79, 20156, Milan, Italy

    Carlo Russo

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  1. Fabio Grizzi
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Correspondence to Egesta Lopci.

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Grizzi, F., Castello, A., Qehajaj, D. et al. The Complexity and Fractal Geometry of Nuclear Medicine Images. Mol Imaging Biol 21, 401–409 (2019). https://doi.org/10.1007/s11307-018-1236-5

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