论文标题
通过材料分解和机器学习的光子计数CT调节法
Photon-counting CT thermometry via material decomposition and machine learning
论文作者
论文摘要
热消融程序，例如高强度聚焦超声（HIFU）和射频消融（RFA），通常用于通过微创加热焦点区域来消除肿瘤。对于此任务，实时3D温度可视化是针对患病组织的关键，同时最大程度地减少对周围环境的损害。当前的CT热量计基于能量集成的CT，组织特异性实验数据以及衰减与温度之间的线性关系。在这封信中，我们使用光子计数CT进行材料分解和神经网络开发了一种新颖的方法，以基于基础材料的热特性和频谱层析成绩测量的温度进行预测。在我们的可行性研究中，选择了蒸馏水，50 mM CaCl2和600 mM CaCl2作为基础材料。它们的衰减在各种温度下以四个离散的能量箱进行测量。经过实验数据训练的神经网络分别在300 mM CaCl2和基于牛奶的蛋白质奶昔上达到了1.80°C和3.97°C的平均绝对误差。这些实验结果表明，我们的方法有望用于处理与基础材料相似或不同的材料的非线性热性能。
Thermal ablation procedures, such as high intensity focused ultrasound (HIFU) and Radiofrequency Ablation (RFA), are often used to eliminate tumors by minimally invasively heating a focal region. For this task, real-time 3D temperature visualization is key to target the diseased tissues while minimizing damage to the surroundings. Current CT thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this letter, we develop a novel approach using photon-counting CT for material decomposition and a neural network to predict temperature based on thermal characteristics of base materials and spectral tomographic measurements of a volume of interest. In our feasibility study, distilled water, 50 mM CaCl2, and 600 mM CaCl2 are chosen as the base materials. Their attenuations are measured in four discrete energy bins at various temperatures. The neural network trained on the experimental data achieves a mean absolute error of 1.80 °C and 3.97 °C on 300 mM CaCl2 and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling nonlinear thermal properties for materials that are similar or dissimilar to our base materials.
