Faculty, Staff and Student Publications
Language
English
Publication Date
10-1-2024
Journal
Medical Image Analysis
DOI
10.1016/j.media.2024.103254
PMID
38968908
PMCID
PMC11365791
PubMedCentral® Posted Date
10-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm
Keywords
Cone-Beam Computed Tomography, Humans, Liver Neoplasms, Imaging, Three-Dimensional, Motion, Chemoembolization, Therapeutic, Radiography, Interventional, Algorithms, Movement, Neural Networks, Computer, Deep learning, Motion compensation, TACE, Cone-beam CT, Image-guided procedures
Published Open-Access
yes
Recommended Citation
Lu, Alexander; Huang, Heyuan; Hu, Yicheng; et al., "Vessel-Targeted Compensation of Deformable Motion in Interventional Cone-Beam CT" (2024). Faculty, Staff and Student Publications. 6535.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6535
Graphical Abstract
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