Photon-counting computed tomography thermometry via material decomposition and machine learning
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors
Wang, Nathan
Li, Mengzhou
Haverinen, Petteri
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en
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6
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Visual Computing for Industry, Biomedicine, and Art, Volume 6, issue 1
Abstract
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.Description
Funding Information: This work is supported by the Johns Hopkins University Leong Research Award for Undergraduates. Publisher Copyright: © 2023, The Author(s).
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Wang, N, Li, M & Haverinen, P 2023, 'Photon-counting computed tomography thermometry via material decomposition and machine learning', Visual Computing for Industry, Biomedicine, and Art, vol. 6, no. 1, 2. https://doi.org/10.1186/s42492-022-00129-w