Abstract
Purpose
Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions.
Methods
A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student’s t-distributions to model the former and Watson distributions for the latter.
Results
The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than \(85\%\) of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student’s t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores.
Conclusion
The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.
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Acknowledgements
AM and NR were supported by the Emerging Fields Initiative project BIG-THERA, ZZ is supported by China Scholarship Council scholarship No. 201406120046.
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The study was done retrospectively. It does not contain any studies with animals performed by any of the authors. Brain image data was acquired for clinical purposes. The SPREAD study was approved by local institute broad.
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Bayer, S., Zhai, Z., Strumia, M. et al. Registration of vascular structures using a hybrid mixture model. Int J CARS 14, 1507–1516 (2019). https://doi.org/10.1007/s11548-019-02007-y
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DOI: https://doi.org/10.1007/s11548-019-02007-y