GATE data for simulating Y-90 Microsphere Clusters in Human Phantom in Vereos Ring Scanner.Copy

Liver cancer treatment, especially for metastatic cases, poses significant challenges in accurately targeting tumours while sparing healthy tissue. Radioembolisation with yttrium-90 (Y-90) microspheres is a promising technique, but precise imaging of microsphere distribution is crucial. This study utilises T-PEPT, a novel Positron Emission Particle Tracking (PEPT) algorithm that combines topological data analysis with machine learning to identify Y-90 microsphere clusters in a digital twin of a patient's liver. GATE (Geant4 Application for Tomographic Emission) Monte Carlo simulations were utilised to model the radioembolisation procedure, including a detailed human-like phantom and the Philips Vereos PET/CT scanner. T-PEPT demonstrates effective identification of microsphere clusters amidst significant noise, achieving high spatial and temporal resolution. The results suggest that T-PEPT could enable near real-time imaging, enhance dosimetry accuracy, and optimise clinical resource utilisation. Future work incorporating Time-of-Flight (ToF) information and improving detector sensitivity is anticipated to further improve this technique's viability for medical applications.

肝脏癌的治疗，尤其是在治疗转移性病例时，准确靶向肿瘤同时保护健康组织面临着重大挑战。利用钇-90（Y-90）微球进行放射栓塞是一种有潜力的技术，但精确成像微球分布至关重要。本研究采用T-PEPT，这是一种创新的正电子发射粒子追踪（PEPT）算法，它结合拓扑数据分析与机器学习，以在患者肝脏的数字孪生中识别Y-90微球簇。本研究利用GATE（Geant4用于断层发射的应用）蒙特卡洛模拟来建模放射栓塞过程，包括一个详细的人体类似模型和Philips Vereos PET/CT扫描仪。T-PEPT在显著噪声中有效识别微球簇，实现了高空间和时间分辨率。结果表明，T-PEPT能够实现近乎实时的成像，提高剂量学准确性，并优化临床资源配置。未来工作将融合飞行时间（ToF）信息并提高检测器灵敏度，以进一步提高该技术在医疗应用中的可行性。