Real-Time Liver Tumor Localization via Combined Surface Imaging and a Single X-Ray Projection

Authors

Hua-Chieh Shao
Yunxiang Li
Jing Wang
Steve Jiang
You Zhang

Language

English

Publication Date

3-9-2023

Journal

Physics in Medicine and Biology

DOI

10.1088/1361-6560/acb889

PMID

36731143

PMCID

PMC10394117

PubMedCentral® Posted Date

3-9-2023

PubMedCentral® Full Text Version

Post-print

Abstract

Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (< 500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection.

Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm.

Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (< 250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.

Keywords

Humans, X-Rays, Radiography, Liver Neoplasms, Neural Networks, Computer, Motion, Imaging, Three-Dimensional

Published Open-Access

yes

Recommended Citation

Shao, Hua-Chieh; Li, Yunxiang; Wang, Jing; et al., "Real-Time Liver Tumor Localization via Combined Surface Imaging and a Single X-Ray Projection" (2023). Faculty, Staff and Student Publications. 6679.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6679