Analysis of Respiratory Induced Liver Motion using Fiducial Marker in Stereotactic Radiosurgery for Liver Tumor

Je, Hyoung-Uk; Choi, Eun Kyung; Ahn, Seung Do; Lee, S.; Park, S.; Kim, J.

doi:10.1016/j.ijrobp.2008.06.090

Cited by 2 publications

(1 citation statement)

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“…Guidance for applications in the liver began with studies focused on modeling and accounting for respiratory motion [15]–[18]. The need for accounting for this motion within the context of radiation therapy for abdominal organs is quite compelling [19].…”

Section: Introductionmentioning

confidence: 99%

A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data

Rucker

Clements

et al. 2014

IEEE Trans. Med. Imaging

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In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.

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Section: Introductionmentioning

confidence: 99%