Vessel-targeted compensation of deformable motion in interventional cone-beam CT

• Published in: Medical image analysis Vol. 97; p. 103254
• Main Authors: Lu, Alexander, Huang, Heyuan, Hu, Yicheng, Zbijewski, Wojciech, Unberath, Mathias, Siewerdsen, Jeffrey H., Weiss, Clifford R., Sisniega, Alejandro
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2024
• Subjects: Cone-beam CT; Deep learning; Image-guided procedures; Motion compensation; TACE; Deep learning; TACE; Cone-beam CT; Motion compensation; Image-guided procedures
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103254

•Projection-domain segmentation of vascular anatomy using multi-frame neural network.•Novel differentiable anatomy-aware vessel-enhancing motion compensation framework.•Validation on simulated interventional CBCT and clinical CBCT.•Substantial improvements to vascular sharpness, DICE score, SSIM.•Recovers previously ambiguous vascular connectivity and diameter. 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−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures. [Display omitted]