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Elastic Registration Based on Compliance Analysis and Biomechanical Graph Matching
Jaime Garcia Guevara1,2, Igor Peterlik3, Marie-Odile Berger3,4
1Inria Nancy Grand Est, Villers-les-Nancy, France. jaime.garcia-guevara@inria.fr.
Annals of Biomedical Engineering
|September 25, 2019
Summary
This study introduces an adaptive graph matching method for elastic registration of vascularized organs, improving surgical navigation. The new method achieves higher accuracy and speed than previous techniques for fusing pre-operative data with intra-operative images.
Area of Science:
- Medical Imaging
- Computer-Aided Surgery
- Biomedical Engineering
Background:
- Accurate registration of pre-operative and intra-operative images is crucial for image-guided surgery, especially for vascularized organs.
- Intra-operative imaging modalities often have limited detail and organs undergo deformation, complicating registration.
- Existing methods may require manual initialization and struggle with significant non-rigid deformations.
Purpose of the Study:
- To propose an automatic elastic registration method for vascularized organs using graph representation of vasculature.
- To enhance the fusion of pre-operative information onto organs during surgery.
- To develop a method that compensates for limited intra-operative imaging details and organ shape changes.
Main Methods:
- Vascular structures in pre-operative and intra-operative images are represented as graphs.
- An Adaptive Compliance Graph Matching (ACGM) method is presented, utilizing a biomechanical liver model.
- The method employs a novel compliance-based adaptive search for efficient graph matching without manual initialization.
Main Results:
- ACGM handles intra-operative non-rigid deformations up to 65 mm.
- It computes a complete displacement field using only matched vasculature.
- ACGM achieved a lower average Target Registration Error (TRE) of 4.2 mm compared to BGM's 6.5 mm on porcine datasets.
- The method is up to one order of magnitude faster, more parameter-independent, and robust to noise than the BGM method.
Conclusions:
- The proposed ACGM method offers a significant improvement over previous biomechanical graph matching techniques for elastic registration of vascularized organs.
- ACGM provides accurate and efficient registration, enabling better fusion of pre-operative data for surgical guidance.
- The method's robustness and speed make it a promising tool for real-time surgical applications.
