Invited Talk: Yuliang Huang (University College London) – Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography, March 7th 2025, 10 AM CET

Title: Resolving Variable Respiratory Motion From Unsorted 4D Computed Tomography
Date: March 7th 2025, 10 AM CET
Location: https://fau.zoom-x.de/j/64254558508?pwd=Z3BWP1bIYCpvI02Av3bO8SfGY4bAly.1

Abstract:
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. In this talk, I will introduce our recent work that 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.

Short Bio:
Yuliang Huang is a final year PhD student from the Radiotherapy Image Computing (RTIC) group, UCL Hawkes Institute. He obtained his bachelor’s and master’s degree from Health Science Center, Peking University, China. He is currently working with Dr. Jamie McClelland on developing models of respiratory motion which include breath-to-breath variability, methods of fitting these models to a variety of imaging data, and tools for using these models to improve future radiotherapy treatments.

Paper Link:
https://link.springer.com/chapter/10.1007/978-3-031-72378-0_55