Registration of liver images to minimally invasive intraoperative surface and subsurface data

Wu, Yifei; Rucker, D. Caleb; Conley, Rebekah H.; Pheiffer, Thomas S.; Simpson, Amber L.; Geevarghese, Sunil K.; Miga, Michael I.

doi:10.1117/12.2044250

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…In a separate extensive study looking at patterns of intraoperative model-to-OR-data fit, [11] also observed this behavior. Additionally, these phantom results are in accordance with our previous phantom studies which entailed multiple novel deformations as reported in [13] and a different liver phantom as in [28]. We acknowledge that the human-to-phantom validation framework could be further strengthened with additional phantom work (derived from varying clinically acquired anatomy), more deformations, and more clinical sparse surface data patterns; and while demonstrated in past work [13], [28], all of these areas are an important continued direction for the future.…”

Section: Discussionsupporting

confidence: 91%

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Collins

Weis

Heiselman

et al. 2017

IEEE Trans. Med. Imaging

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In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.

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Section: Discussionsupporting

confidence: 91%

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Collins

Weis

Heiselman

et al. 2017

IEEE Trans. Med. Imaging

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“…Also, the influence of the sparsity and partiality of the target data is studied w. r. t. the accuracy of the registration method. Finally, different acquisition modalities are compared in Wu et al (2014).…”

Section: Geometry-based Registrationmentioning

confidence: 99%

Fast elastic registration of soft tissues under large deformations

Peterlík

Courtecuisse

Rohling

et al. 2018

Medical Image Analysis

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A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance.

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“…FEM-based techniques that register the liver between pre-procedural and intra-procedural images have been investigated for image-guidance applications in radiation therapy [18][19][20] and minimally invasive liver surgery. 16,[21][22][23] A natural approach to FEM-based deformable liver registration is to use liver surface displacements as boundary conditions (BCs) from which the internal displacements are assumed based on the physical properties of the tissue. 14,24,25 However, if BCs are only applied on the volume's surface, FEM-based registration models may yield increased uncertainty at internal points of interest for cases of highly variable, heterogeneous, and localized deformations.…”

Section: Introductionmentioning

confidence: 99%

FEM deformable liver registration to facilitate CBCT guided histotripsy

Minesinger,

Laeseke,

Kutlu

et al. 2024

Medical Imaging 2024: Physics of Medical Imaging

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Histotripsy is an emerging focal tumor therapy that utilizes focused ultrasound (US) to mechanically destroy tissue. Cone beam CT (CBCT) guidance has been developed to overcome limitations of diagnostic US for visualizing and targeting tumors. Existing workflow for CBCT-guided histotripsy utilizes targeting based on CBCT images acquired with the patient in position for treatment; therefore, treatment planning is currently performed intraprocedurally right before delivering therapy. To provide a framework for planning liver tumor treatments in advance, a biomechanical, nonrigid model is proposed to predict volumetric liver deformations between a pre-procedural diagnostic scan and the intraprocedural CBCT. In the proposed registration approach, the liver, gallbladder (GB), and hepatic blood vessels were segmented from CBCT images acquired at two different points of motion. Segmented structural surfaces were registered using a rigid iterative closest point algorithm followed by deformable registration (demons algorithm). These internal and external hepatic structural surface registrations were used as boundary conditions for a finite element model (FEM) to determine internal liver deformations. Four FEM models were constructed: the liver alone (L-FEM), liver and GB (LG-FEM), liver and vessels (LV-FEM), and liver, GB, and vessels (LGV-FEM). Registration accuracy was measured as the Euclidean distance between manually annotated vessel bifurcations. Bifurcation error was 3.5±3.5mm for LGV-FEM, a 52% improvement from L-FEM (7.4±6.1mm). Including vessels and GB in the model both individually reduced bifurcation error compared to corresponding models excluding those structures. FEM-based liver registration guided by internal and external hepatic structures is a feasible method to facilitate CBCT-guided histotripsy treatment planning.

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Registration of liver images to minimally invasive intraoperative surface and subsurface data

Cited by 4 publications

References 22 publications

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Fast elastic registration of soft tissues under large deformations

FEM deformable liver registration to facilitate CBCT guided histotripsy

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