# Surface-guided breathing signal integration in breathing-adapted intelligent 4D computed tomography: prototype implementation and comparison with an infrared marker-based system

## TL;DR

This paper introduces a new method for 4D CT scans using surface tracking instead of infrared markers, improving accuracy and image quality during irregular breathing.

## Contribution

First use of surface-guided signals for intelligent 4D CT, with motion correction and prediction enhancing accuracy.

## Key findings

- Surface-guided tracking achieved high correlation with ideal breathing patterns (r = 1).
- Tumor motion error was reduced to below 0.5 mm with prediction during irregular breathing.
- Phase-based reconstructions showed better accuracy than amplitude-based ones when prediction was applied.

## Abstract

•Surface guided signal used for the first time to control intelligent 4D computed tomography.•Motion correction and prediction algorithm improved signal accuracy.•Breathing curves from surface tracking closely matched ideal motion pattern.•Tumor motion error reduced from 1.5 mm to below 0.5 mm with prediction.•Contactless tracking preserved image quality even under irregular breathing.

Surface guided signal used for the first time to control intelligent 4D computed tomography.

Motion correction and prediction algorithm improved signal accuracy.

Breathing curves from surface tracking closely matched ideal motion pattern.

Tumor motion error reduced from 1.5 mm to below 0.5 mm with prediction.

Contactless tracking preserved image quality even under irregular breathing.

Breathing-adapted intelligent four-dimensional computed tomography (i4DCT) reduces motion artifacts during irregular breathing using real-time surrogate signal analysis to control X-ray timing. Current implementations rely on infrared (IR) markers or pressure belts. Surface-guided radiation therapy (SGRT), a widely used markerless technique in radiotherapy, has not been evaluated for direct i4DCT integration. This study tested SGRT for i4DCT acquisition in a phantom setup and benchmarked it against an infrared marker-based system.

Phantom measurements with clinically relevant regular and irregular breathing signals were performed on a prototype CT scanner with direct SGRT control. To improve SGRT-based surrogate accuracy, a table motion correction profile was empirically modeled, and a vendor-specific prediction algorithm was implemented. Surrogate signal accuracy, latency, and motion reconstruction accuracy were compared between SGRT and an established IR system using amplitude- and phase-based reconstructions.

SGRT exhibited an absolute latency of ∼ 63  ms, compared to ∼ 23  ms for the IR system. Across regular and irregular breathing signals, SGRT breathing signals showed Root Mean Square Error (RMSE) values up to ∼ 1.5  mm, while correlation with the ideal signal remained high (r = 1). Tumor center-of-mass deviations in amplitude-based reconstructions reached 1.5  mm without prediction at 20 breaths-per-minute and 15  mm amplitude, but reduced to < 0.5  mm with 50  ms prediction. Both amplitude- and phase-based reconstructions showed improved agreement with ideal motion when prediction was applied, with phase-based reconstructions yielding better accuracy.

These findings support SGRT as a clinically viable non-contact alternative to IR tracking in i4DCT, especially when combined with motion correction and predictive modeling.