Elastic registration based on compliance analysis and biomechanical graph matching

TL;DR

This paper introduces an automatic elastic registration method for vascularized organs that uses biomechanical graph matching to improve accuracy and speed in aligning preoperative and intra-operative images, handling significant deformations.

Contribution

The proposed Adaptive Compliance Graph Matching (ACGM) method advances prior work by eliminating manual initialization, improving robustness, and increasing efficiency in nonrigid organ registration.

Findings

ACGM achieves lower target registration error (TRE) than previous methods.

ACGM is up to ten times faster and more noise-robust than BGM.

It effectively handles deformations up to 65 mm in size.

Abstract

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.g. CBCT) and to cope with changes in the shape of the organ. Due to image modalities differences and organ deformation, each graph has a different topology and shape. The Adaptive Compliance Graph Matching (ACGM) method presented does not require any manual initialization, handles intra-operative nonrigid deformations of up to 65 mm and computes a complete displacement field over the organ from only the matched vasculature. ACGM is better than the previous Biomechanical Graph Matching method 3 (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance. This allows to efficiently find the best graph matches with a novel compliance-based adaptive search. These contributions are evaluated on ten realistic synthetic and two real porcine automatically segmented datasets. ACGM obtains better target registration error (TRE) than BGM, with an average TRE in the real datasets of 4.2 mm compared to 6.5 mm, respectively. It also is up to one order of magnitude faster, less dependent on the parameters used and more robust to noise.