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May 26 – 31, 2024
Western University
America/Toronto timezone
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(G*) (POS-47) Navigating Radioguided Cancer Surgery Using Principles of Gamma Photon Interactions

May 28, 2024, 6:35 PM
2m
PAB Hallways (Western University)

PAB Hallways

Western University

Poster Competition (Graduate Student) / Compétition affiches (Étudiant(e) 2e ou 3e cycle) Physics in Medicine and Biology / Physique en médecine et en biologie (DPMB-DPMB) DPMB Poster Session & Student Poster Competition (28) | Session d'affiches DPMB et concours d'affiches étudiantes (28)

Speaker

Sydney Wilson (Western University)

Description

Breast cancer is the leading cause of cancer in women worldwide and surgery to remove the tumour and stage the cancer is a crucial component of most treatment plans. Radioguided surgery using a hand-held gamma probe that counts gamma photons is a common technique that allows surgeons to locate non-palpable, radiolabeled lesions in the operating room. While gamma probes are effective for detection of low-energy radiotracers, increased scattering at higher energies degrades the probe’s resolution and limits the use of high-energy radiotracers, such as positron emitters. Understanding the physics of photon interactions and their influence on the shape of a detected gamma-ray energy spectrum, we hypothesized that a machine learning model could analyze the energy spectrum recorded by a gamma probe and predict the location from which the gamma photons originated. As such, the goal of the study was to assess how well machine learning improves a gamma probe’s ability to localize high-energy radiotracers.

Using Monte Carlo simulations, we modeled a custom designed multifocal gamma probe featuring a 4-segmented collimator and detector. To simulate surgery, a 511 keV radioactive point source was embedded 35 mm below the probe in phantom breast tissue. The source was positioned at various known x, y locations and a 4-channel energy spectrum was recorded. Simulations were repeated 300 times, and the data was split using an 80:20 ratio for training and testing. A 1D convolutional neural network (CNN) was trained to analyze the recorded energy spectra and predict the x, y location of the radioactive source.

The CNN was able to effectively predict the location of the radioactive source from a 4-channel energy spectrum of a multifocal gamma probe. As desired, there was a strong linear relationship (R2 = 0.93) between the true and predicted coordinate locations. The CNN had a small mean prediction error of 2.9 mm and could predict the location of the radioactive source over a large 40x40 mm field of view. The CNN predictions improved the resolution of the multifocal gamma probe by at least 10-fold compared to existing gamma probes. Overall, this work presents a new, real-time localization technique that offers higher resolution and more efficient directional guidance for detecting high-energy radiotracers with a hand-held gamma probe in surgery.

Keyword-1 radioguided surgery
Keyword-2 machine learning
Keyword-3 gamma detector

Primary author

Sydney Wilson (Western University)

Co-author

David Holdsworth (Western University)

Presentation materials

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