Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning—A Residual Physics Approach | IEEE Journals & Magazine | IEEE Xplore

Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning—A Residual Physics Approach


Abstract:

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal ...Show More

Abstract:

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAPs) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450–550 keV).
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 36, Issue: 1, January 2025)
Page(s): 582 - 594
Date of Publication: 20 October 2023

ISSN Information:

PubMed ID: 37862278

Funding Agency:


References

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