TY - GEN
T1 - Low-Cost Ultrasound Thermometry for HIFU Therapy Using CNN
AU - Kim, Younsu
AU - Audigier, Chloe
AU - Ellens, Nicholas
AU - Boctor, Emad M.
N1 - Funding Information:
The research reported in this paper was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R01EB021396 and National Science Foundation under Proposal Number 1653322.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/17
Y1 - 2018/12/17
N2 - High intensity focused ultrasound (HIFU)is a noninvasive thermal therapy used for hyperthermia and ablation treatments. Temperature monitoring is important for those procedures to induce a necessary amount of thermal dose to the target area without damaging the surrounding healthy tissues. To this end, various medical imaging techniques have been proposed. Magnetic resonance imaging provides a high accuracy temperature monitoring feature. Ultrasound is a favorable medical imaging modality for thermal monitoring due to its cost-effectiveness, accessibility and non-ionizing radiation. The speed of sound and attenuation of ultrasound waves varies with the temperature, so temperature can be measured using those ultrasound physical properties. In the previous work, we developed an ultrasound thermal monitoring method for HIFU using an external ultrasound element. The system only requires simple hardware additions such as the external ultrasound sensor and computation units, providing a temperature monitoring method at a reduced cost. However, since we use only few external ultrasound sensors, the collected time of flight information is sparse. Moreover, the thermal image reconstruction highly depends on the ultrasound element locations and its accuracy could be highly deteriorated with certain sensor locations. In this paper, we propose to reconstruct thermal images using a neural network. As this method can learn the heat evolution from a large amount of data set, it could be less sensitive to the ultrasound element location. We validated the temperature image reconstruction method on a phantom study. Promising results show the feasibility of a thermal monitoring method using an external ultrasound element and deep learning reconstruction.
AB - High intensity focused ultrasound (HIFU)is a noninvasive thermal therapy used for hyperthermia and ablation treatments. Temperature monitoring is important for those procedures to induce a necessary amount of thermal dose to the target area without damaging the surrounding healthy tissues. To this end, various medical imaging techniques have been proposed. Magnetic resonance imaging provides a high accuracy temperature monitoring feature. Ultrasound is a favorable medical imaging modality for thermal monitoring due to its cost-effectiveness, accessibility and non-ionizing radiation. The speed of sound and attenuation of ultrasound waves varies with the temperature, so temperature can be measured using those ultrasound physical properties. In the previous work, we developed an ultrasound thermal monitoring method for HIFU using an external ultrasound element. The system only requires simple hardware additions such as the external ultrasound sensor and computation units, providing a temperature monitoring method at a reduced cost. However, since we use only few external ultrasound sensors, the collected time of flight information is sparse. Moreover, the thermal image reconstruction highly depends on the ultrasound element locations and its accuracy could be highly deteriorated with certain sensor locations. In this paper, we propose to reconstruct thermal images using a neural network. As this method can learn the heat evolution from a large amount of data set, it could be less sensitive to the ultrasound element location. We validated the temperature image reconstruction method on a phantom study. Promising results show the feasibility of a thermal monitoring method using an external ultrasound element and deep learning reconstruction.
KW - CNN
KW - HIFU
KW - Ultrasound
KW - deep learning
KW - temperature monitoring
KW - thermometry
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U2 - 10.1109/ULTSYM.2018.8579982
DO - 10.1109/ULTSYM.2018.8579982
M3 - Conference contribution
AN - SCOPUS:85060599715
T3 - IEEE International Ultrasonics Symposium, IUS
BT - 2018 IEEE International Ultrasonics Symposium, IUS 2018
PB - IEEE Computer Society
T2 - 2018 IEEE International Ultrasonics Symposium, IUS 2018
Y2 - 22 October 2018 through 25 October 2018
ER -