Abstract
BackgroundBiomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise. However, this technique has been limited by the accuracy and the computational efficiency of patient-specific modeling.MethodsThis study presents a tissue–tissue coupling strategy based on penalty method to model the heterogeneous behavior of deformable body, and estimate the personalized tissue–tissue coupling parameters in a data-driven way. Moreover, considering that the computational efficiency of biomechanical model is highly dependent on the mechanical resolution, a practical coarse-to-fine scheme is proposed to increase runtime efficiency. Particularly, a detail enrichment database is established in an offline fashion to represent the mapping relationship between the deformation results of high-resolution hexahedral mesh extracted from the raw medical data and a newly constructed low-resolution hexahedral mesh. At runtime, the mechanical behavior of human organ under interactions is simulated with this low-resolution hexahedral mesh, then the microstructures are synthesized in virtue of the detail enrichment database.ResultsThe proposed method is validated by volumetric registration in an abdominal phantom compression experiments. Our personalized heterogeneous deformable model can well describe the coupling effects between different tissues of the phantom. Compared with high-resolution heterogeneous deformable model, the low-resolution deformable model with our detail enrichment database can achieve 9.4× faster, and the average target registration error is 3.42 mm, which demonstrates that the proposed method shows better volumetric registration performance than state-of-the-art.ConclusionsOur framework can well balance the precision and efficiency, and has great potential to be adopted in the practical augmented reality image-guided robotic systems.
Highlights
Biomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise
Surgical procedures are traditionally supported with pre-operative images, such as the computed tomography (CT) images and magnetic resonance (MR) images
We propose a coarse-to-fine scheme to reduce the computational complexity of heterogeneous deformable model for fast volumetric registration
Summary
Biomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise This technique has been limited by the accuracy and the computational efficiency of patient-specific modeling. Purely rigid transformation is not sufficient to describe the mechanical behaviors of human organ for most of the surgeries To this end, this technique cannot produce an optimal alignment when human organ undergoes deformations caused by external forces (such as surgical tools) or natural motions (such as respiration). This technique cannot produce an optimal alignment when human organ undergoes deformations caused by external forces (such as surgical tools) or natural motions (such as respiration) In such cases, non-rigid registration is required when the imaged anatomy non-rigidly deforms between acquisitions, which can provide a relatively accurate alignment for cases of non-rigid deformations. Readers can refer to [3] for a thorough and comprehensive introduction of non-rigid registration
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