Learning-based target localization in robotic radioguided surgery in noisy environments

In radioguided surgery, G-probes are used intraoperatively to localize targets marked by radionuclides. However, the interpretation of G-probe measurements is challenging due to background activity from surrounding organs. This work investigated whether deep neural networks can localize radiation sources in such environments and how different background activities impact this task. A physics-guided forward model simulated G-probe measurements for different intra-abdominal distributions, including an anatomically inspired bladder activity. A convolutional neural network was trained on simulated measurements to predict 3D target source positions. Results indicated that prediction accuracy improved with more Gprobe measurements and degraded with increased background activity. In particular, proximity to high-activity regions like the bladder significantly reduced accuracy. This study demonstrates the need to consider background activity distributions for target localization and that a convolutional neural network could solve this task.

DDC Class

610: Medicine, Health

616: Diseases

617: Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology

519: Applied Mathematics, Probabilities

539: Matter; Molecular Physics; Atomic and Nuclear physics; Radiation; Quantum Physics

Funding(s)

Centre of Excellence of Al for Sustainable Living and Working

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

10.1515_cdbme-2025-0323-1.pdf

Type

Main Article

Size

2.75 MB

Format

Adobe PDF

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Learning-based target localization in robotic radioguided surgery in noisy environments