# Real-time spatiotemporal optimization during imaging

**Authors:** Owen Dillon, Benjamin Lau, Shalini K. Vinod, Paul J. Keall, Tess Reynolds, Jan-Jakob Sonke, Ricky T. O’Brien

PMC · DOI: 10.1038/s44172-025-00391-9 · 2025-03-31

## TL;DR

A new real-time imaging method improves lung cancer radiation therapy by reducing scan time and radiation exposure while maintaining image quality.

## Contribution

A spatiotemporal optimization approach for real-time imaging that reduces scan time and radiation in clinical practice.

## Key findings

- Scan time was reduced by 63% compared to the clinical standard.
- Radiation exposure was reduced by 85% without compromising image quality.
- The approach was successfully tested in a clinical trial with 30 patients.

## Abstract

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.

Dillon et al. developed a new computational approach for optimizing CT-based image guidance for lung cancer radiation therapy. Monitoring the patient while controlling the imaging hardware in real time resulted in a 63% reduction in scan time and an 85% reduction in radiation, as demonstrated in a clinical trial of 30 patients.

## Linked entities

- **Diseases:** lung cancer (MONDO:0005138)

## Full-text entities

- **Diseases:** tumors (MESH:D009369), lung cancer (MESH:D008175)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11958730/full.md

---
Source: https://tomesphere.com/paper/PMC11958730