# Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography

arXiv: https://arxiv.org/abs/2407.00665 · HTML: https://mlsanity.com/paper/2407.00665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 30 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Online Learning Rate Adaptation with Hypergradient Descent](https://mlsanity.com/paper/1703.04782/) (2017)
- [Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts](https://mlsanity.com/paper/2106.06853/) (2021)

## Built on by (most cited first, 2 of 2)

- [DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model](https://mlsanity.com/paper/2506.22280/) (2025)
- [Biophysics-Enhanced Neural Representations for Patient-Specific Respiratory Motion Modeling](https://mlsanity.com/paper/2603.22123/) (2026)

## Abstract (arXiv)

4D Computed Tomography (4DCT) is widely used for many clinical applications such as radiotherapy treatment planning, PET and ventilation imaging. However, common 4DCT methods reconstruct multiple breath cycles into a single, arbitrary breath cycle which can lead to various artefacts, impacting the downstream clinical applications. Surrogate driven motion models can estimate continuous variable motion across multiple cycles based on CT segments `unsorted' from 4DCT, but it requires respiration surrogate signals with strong correlation to the internal motion, which are not always available. The method proposed in this study eliminates such dependency by adapting the hyper-gradient method to the optimization of surrogate signals as hyper-parameters, while achieving better or comparable performance, as demonstrated on digital phantom simulations and real patient data. Our method produces a high-quality motion-compensated image together with estimates of the motion, including breath-to-breath variability, throughout the image acquisition. Our method has the potential to improve downstream clinical applications, and also enables retrospective analysis of open access 4DCT dataset where no respiration signals are stored. Code is avaibale at https://github.com/Yuliang-Huang/4DCT-irregular-motion.