Deep Learning for Predicting Gamma-Ray Interaction Positions in LYSO Detector.

Clement, C; Birindelli, G; Pizzichemi, M; Pagano, F; Kruithof-de Julio, M; Rominger, A; Auffray, E; Shi, K (2021). Deep Learning for Predicting Gamma-Ray Interaction Positions in LYSO Detector. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021, pp. 3366-3369. IEEE 10.1109/EMBC46164.2021.9630934

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Positron Emission Tomography (PET) is among the most commonly used medical imaging modalities in clinical practice, especially for oncological applications. In contrast to conventional imaging modalities like X-ray Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), PET retrieves in vivo information about biochemical processes rather than just anatomical structures. However, physical limitations and detector constraints lead to an order of magnitude lower spatial resolution in PET images. In recent years, the use of monolithic detector crystals has been investigated to overcome some of the factors limiting spatial resolution. The key to increasing PET systems' resolution is to estimate the gamma-ray interaction position in the detector as precisely as possible.In this work, we evaluate a Convolutional Neural Network (CNN) based reconstruction algorithm that predicts the gamma-ray interaction position using light patterns recorded with Silicon photomultipliers (SiPMs) on the crystal's surfaces. The algorithm is trained on data from a Monte Carlo Simulation (MCS) that models a gamma point source and a detector consisting of Lutetium-yttrium oxyorthosilicate (LYSO) crystals and SiPMs added to five surfaces. The final Mean Absolute Error (MAE) on the test dataset is 1.48 mm.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Urologie

UniBE Contributor:

Clement, Christoph Ludwig; Birindelli, Gabriele; Kruithof-de Julio, Marianna; Rominger, Axel Oliver and Shi, Kuangyu

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2694-0604

Publisher:

IEEE

Language:

English

Submitter:

Daria Vogelsang

Date Deposited:

13 Jan 2022 14:57

Last Modified:

13 Jan 2022 15:06

Publisher DOI:

10.1109/EMBC46164.2021.9630934

PubMed ID:

34891961

BORIS DOI:

10.48350/163147

URI:

https://boris.unibe.ch/id/eprint/163147

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