Real-time motion analysis with 4D deep learning for ultrasound-guided radiotherapy

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of <inline-formula><tex-math notation="LaTeX">$0.35\pm 0.2$</tex-math></inline-formula> mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.

Schlagworte

4D deep learning

Deep learning

motion analysis

Motion analysis

Motion estimation

radiation therapy

Radiation therapy

Real-time systems

sequence processing

Training

Ultrasonic imaging

ultrasound

MLE@TUHH

DDC Class

620: Engineering

004: Computer Sciences

Options

Real-time motion analysis with 4D deep learning for ultrasound-guided radiotherapy