Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy

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 $0.35pm 0.2$ 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.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research