Photon-counting computed tomography thermometry via material decomposition and machine learning
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2023-12
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
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).
Keywords
Artificial intelligence, Computed tomography thermometry, Deep learning, Material decomposition, Neural network, Photon-counting computed tomography, Radiotherapy, Thermotherapy
Other note
Citation
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
