Deep learning based CT thermometry for thermal tumor ablation

Tomographically measuring the temperature distribution inside a human body has important and immediate clinical applications - including thermal ablative and hyperthermic treatment of cancers, and will enable novel solutions such as opening the blood-brain barrier for therapeutics and robotic surgery in the future. A high intensity focused ultrasound (HIFU) device can heat tumor tissues to 50-90 °C locally within seconds. Thus, accurate, real-time control of the thermal dose is critical to eliminate tumors while minimizing damage to surrounding healthy tissue. This study investigates the feasibility of using deep learning to improve the accuracy of low-dose CT (LDCT) thermometry. CT thermometry relies on thermal expansion coefficients, which is prone to inter-patient variability, and is also affected by image noise and artifacts. To demonstrate that deep neural networks can compensate for these factors, 1,000 computer-generated CT phantoms with simulated heating spots were used in training both a “divide and conquer” and “end to end” approach. In the first strategy, the first encoder-decoder network corrected for beam hardening and Poisson noise in the image domain, while a second fine-tuned differences between predicted and ground truth heat maps. The second strategy is identical to the first, except only a single convolutional autoencoder was used as the CT images were not pre-cleaned. Ultimately, the two-part divide and conquer network increased thermal accuracy substantially, demonstrating exciting future potential for the use of deep learning in this field.