Photon-counting computed tomography thermometry via material decomposition and machine learning

Visual Computing for Industry, Biomedicine, and Art

Original Article

Petteri Haverinen¹  Nathan Wang²  Mengzhou Li³ 

[1] Aalto Design Factory, Aalto University, 02150, Espoo, Finland;Department of Biomedical Engineering, Johns Hopkins University, 21218, Baltimore, MD, USA;Department of Biomedical Engineering, Rensselaer Polytechnic Institute, 12180, Troy, NY, USA;

关键词: Photon-counting computed tomography;    Material decomposition;    Computed tomography thermometry;    Artificial intelligence;    Deep learning;    Neural network;    Thermotherapy;    Radiotherapy;   

DOI  :  10.1186/s42492-022-00129-w

 received in 2022-10-20, accepted in 2022-12-22,  发布年份 2022

来源: Springer

【 摘 要 】

Thermal ablation procedures, such as high intensity focused ultrasound and radiofrequency ablation, 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 computed tomography (CT) thermometry is based on energy-integrated CT, tissue-specific experimental data, and linear relationships between attenuation and temperature. In this paper, 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 mmol/L CaCl2, and 600 mmol/L 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 3.97 °C and 1.80 °C on 300 mmol/L CaCl2 and a milk-based protein shake respectively. These experimental results indicate that our approach is promising for handling non-linear thermal properties for materials that are similar or dissimilar to our base materials.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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