Real-time spatiotemporal optimization during imaging.

High quality imaging is required for high quality medical care, especially in precision applications such as radiation therapy. Patient motion during image acquisition reduces image quality and is either accepted or dealt with retrospectively during image reconstruction. Here we formalize a general approach in which data acquisition is treated as a spatiotemporal optimization problem to solve in real time so that the acquired data has a specific structure that can be exploited during reconstruction. We provide results of the first-in-world clinical trial implementation of our spatiotemporal optimization approach, applied to respiratory correlated 4D cone beam computed tomography for lung cancer radiation therapy (NCT04070586, ethics approval 2019/ETH09968). Performing spatiotemporal optimization allowed us to maintain or improve image quality relative to the current clinical standard while reducing scan time by 63% and reducing scan radiation by 85%, improving clinical throughput and reducing the risk of secondary tumors. This result motivates application of the general spatiotemporal optimization approach to other types of patient motion such as cardiac signals and other modalities such as CT and MRI.