Deformable CT-US Registration via Anatomy-Aware Implicit Neural Representations
In this work, we introduce a deformable CT-ultrasound registration framework that incorporates anatomical priors derived from CT to improve registration under deformation.
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Key points
- Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structures intra-operatively that are discernible in CT.
- While optical tracking enables initial rigid registration, contact from the probe induces soft tissue deformations that inhibit accurate alignment.
- Rigid registration is first established using a robot-assisted optical tracking system, after which a deformable transformation is estimated using a sinusoidal implicit neural representation (SIREN) optimized per frame.
- Tissue stiffness is approximated from CT-based HU values and used as spatially varying regularization, suppressing deformation in rigid structures such as bone while allowing more flexibility in soft tissue.
Sources (1)
- [1]Deformable CT-US Registration via Anatomy-Aware Implicit Neural RepresentationsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 02:22 PM
In this work, we introduce a deformable CT-ultrasound registration framework that incorporates anatomical priors derived from CT to improve registration under deformation.
Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structures intra-operatively that are discernible in CT.
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