Preliminary assessment of a convolutional neural network for localization of a radioactive source with a hand-held gamma detector

Sydney Wilson; David W. Holdsworth

doi:10.1117/12.3006304

29 March 2024 Preliminary assessment of a convolutional neural network for localization of a radioactive source with a hand-held gamma detector

Sydney Wilson, David W. Holdsworth

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129280F (2024) https://doi.org/10.1117/12.3006304
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Intraoperative localization of non-palpable lesions is an essential task in cancer surgery, and radioguided surgery using a gamma probe is a commonly used technique for this purpose. While gamma probes have proven to be effective for precise detection of lower energy radionuclides, scattering and septal penetration of the collimator at higher energies rapidly degrades the probe’s resolution. To combat these challenges, we aim to investigate the accuracy of a neural network in predicting the location of a high-energy radioactive source. In this study, we use Monte Carlo simulations to model a gamma probe containing a multifocal collimator and 4-segmented scintillation crystal. A 511 keV radioactive point source was positioned at various x, y locations 35 mm below the probe and a 4-channel energy spectrum was recorded for 300 simulations. A convolutional neural network (CNN) featuring three blocks of 1D-convolutions was used to predict the x, y source location from the energy spectra. The CNN hyperparameters were tuned using cross validation, and 20% of the data was reserved for testing. The results demonstrated a strong linear relationship (R² = 0.92) between the true and predicted location. Achieving a mean radial error of 2.9 mm (±1.8 mm standard deviation) enabled the location of the radioactive source to be predicted to within 6.5 mm 95% of the time, a significant improvement compared to existing gamma probes where the resolution can be tens of millimeters. Overall, this work presents a new technique to improve the localization of positron-emitting radiolabels, thereby enhancing surgical accuracy.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sydney Wilson and David W. Holdsworth "Preliminary assessment of a convolutional neural network for localization of a radioactive source with a hand-held gamma detector", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129280F (29 March 2024); https://doi.org/10.1117/12.3006304

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available