Speakers
Description
We present a new approach for positron emission tomography (PET) event classification that integrates machine learning with quantum-aware signal processing. Our system utilizes a cross-strip Cadmium Zinc Telluride (CZT) detector architecture optimized for high-resolution spatial and energy discrimination. By exploiting quantum correlations of annihilation photons, we aim to enhance the differentiation between true coincidence events and scatter-induced gamma interactions. A machine learning model is trained to identify signatures of entangled annihilation events in real time, using features derived from timing, energy, and spatial coincidence patterns across orthogonal strips. We envision this work contributing to scalable, AI-enhanced PET systems and welcome collaborations on model co-design, firmware integration, and front-end ML acceleration strategies.
| Preferred session (remote speakers) | Afternoon session |
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