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Compton imaging for medical applications

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Abstract

Compton imaging exploits inelastic scattering, known as Compton scattering, using a Compton camera consisting of a scatterer detector in the front layer and an absorber detector in the back layer. This method was developed for astronomy, and in recent years, research and development for environmental and medical applications has been actively conducted. Compton imaging can discriminate gamma rays over a wide energy range from several hundred keV to several MeV. Therefore, it is expected to be applied to the simultaneous imaging of multiple nuclides in nuclear medicine and prompt gamma ray imaging for range verification in particle therapy. In addition, multiple gamma coincidence imaging is expected to be realized, which allows the source position to be determined from a single coincidence event using nuclides that emit multiple gamma rays simultaneously, such as nuclides that emit a single gamma ray simultaneously with positron decay. This review introduces various efforts toward the practical application of Compton imaging in the medical field, including in vivo studies, and discusses its prospects.

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Notes

  1. The relationship among the incidence and outgoing photon energies and the scattering angle is determined by conservation of relativistic energy and momentum.

  2. A scintillator is excited by gamma-ray irradiation and then emits luminescent lights.

  3. The Doppler effect by a distribution of the velocities of electrons bounded to atoms results in uncertainty of Doppler shifts in energy information when interacting with the gamma rays.

  4. A gamma ray immediately emitted from an atomic nucleus by a nuclear reaction is called a prompt gamma ray, and its energy depends on the nuclide.

  5. The spatial distribution of detector response for elongated elements becomes wider when gamma rays are coming at an oblique angle compared to when they are coming from the front.

  6. Radiation detectors that can detect interaction position not only in 2D but also depth direction are called DOI detectors.

  7. GAN is a machine learning framework consisting of two neural networks that contest each other, and pix2pix is a powerful tool for image conversion. In training for image conversion, one network generates a converted image, and the other discriminates whether it is an image from the training set or a generated one.

  8. https://www.cancerimagingarchive.net/

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Acknowledgements

The authors would like to thank Dr. Takashi Nakano for the financial support provided under the “Directorate’s Fund Project.” This work was supported by the QST President's Strategic Grant (QST Advanced Study Laboratory), Nakatani Foundation, Konica Minolta Science and Technology Foundation, and JSPS KAKENHI Grant (21K19936, 20H05667, and 20K12683).

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  1. National Institutes for Quantum Science and Technology, Anagawa 4-9-1, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan

    Hideaki Tashima & Taiga Yamaya

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Correspondence to Hideaki Tashima.

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Tashima, H., Yamaya, T. Compton imaging for medical applications. Radiol Phys Technol 15, 187–205 (2022). https://doi.org/10.1007/s12194-022-00666-2

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