CUST: CNN for ultrasound thermal image reconstruction using sparse time-of-flight information

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Abstract

Thermotherapy is a clinical procedure to induce a desired biological tissue response through temperature changes. To precisely operate the procedure, temperature monitoring during the treatment is essential. Ultrasound propagation velocity in biological tissue changes as temperature increases. An external ultrasound element was integrated with a bipolar radiofrequency (RF) ablation probe to collect time-of-flight information carried by ultrasound waves going through the ablated tissues. Recovering temperature at the pixel level from the limited information acquired from this minimal setup is an ill-posed problem. Therefore, we propose a learning approach using a designed convolutional neural network. Training and testing were performed with temperature images generated with a computational bioheat model simulating a RF ablation. The reconstructed thermal images were compared with results from another sound velocity reconstruction method. The proposed method showed better stability and accuracy for different ultrasound element locations. Ex-vivo experiments were also performed on porcine liver to evaluate the proposed temperature reconstruction method.

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Kim, Y., Audigier, C., Anas, E. M. A., Ziegle, J., Friebe, M., & Boctor, E. M. (2018). CUST: CNN for ultrasound thermal image reconstruction using sparse time-of-flight information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11042 LNCS, pp. 29–37). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_4

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