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

Collins, Jerry C.; Weis, Jared A.; Heiselman, Jon S.; Clements, Logan W.; Simpson, Amber L.; Jarnagin, William R.; Miga, Michael I.

doi:10.1109/tmi.2017.2668842

Cited by 42 publications

(22 citation statements)

References 26 publications

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“…This approach was reported in previous work with a detailed analysis of how it improves fidelity of both rigid and nonrigid registration. 35…”

Section: Rigid Registrationmentioning

confidence: 99%

“…2.3.1. After rigid registration, the salient feature and anterior surface digitizations are resampled using the surface reconstruction method described by Collins et al 35 This resampling method standardizes the density and topology of the sparse surfaces to diminish the influences of trajectory, dwell, and surface noise from intraoperative data collection. An augmented vector of model parameters α 0 is considered, where E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 6 ; 3 2 6 ; 4 7 0 α 0 ¼ ½α T ; t x ; t y ; t z ; θ x ; θ y ; θ z T (16) includes the rigid body translation and rotation parameters t x , t y , t z , θ x , θ y , and θ z in addition to the vector of linear coefficients α that apply to the preoperatively determined responses to control point deformations.…”

Section: Reconstruction Of Intraoperative Deformationmentioning

confidence: 99%

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Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

et al. 2017

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Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. We first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a biomechanical correction approach that leverages anatomically load-bearing support surfaces from ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of [Formula: see text] and surface error of [Formula: see text] using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Finally, we demonstrate viability of the correction method with clinical data.

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“…This approach was reported in previous work with a detailed analysis of how it improves fidelity of both rigid and nonrigid registration. 35…”

Section: Rigid Registrationmentioning

confidence: 99%

Section: Reconstruction Of Intraoperative Deformationmentioning

confidence: 99%

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

et al. 2017

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“…Nonetheless, the results presented here indicate that accuracies better than 10 mm can only be achieved by deformable registration. Deformable registration and breathing motion compensation [ 16 ] of the liver has been shown to be technically possible by several groups [ 9 , 17 ]. This raises the question of how the surgeon interprets alignment errors when the model has been computationally deformed.…”

Section: Discussionmentioning

confidence: 99%

“…Collins et al [ 9 ] investigate the effect of variation in surface reconstruction protocol on rigid and non-rigid surface-based registration. They show that a system using rigid registration can be expected to have registration errors around 10 mm, while deformable registration can get down to approximately 6 mm.…”

Section: Introductionmentioning

confidence: 99%

In vivo estimation of target registration errors during augmented reality laparoscopic surgery

et al. 2018

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PurposeSuccessful use of augmented reality for laparoscopic surgery requires that the surgeon has a thorough understanding of the likely accuracy of any overlay. Whilst the accuracy of such systems can be estimated in the laboratory, it is difficult to extend such methods to the in vivo clinical setting. Herein we describe a novel method that enables the surgeon to estimate in vivo errors during use. We show that the method enables quantitative evaluation of in vivo data gathered with the SmartLiver image guidance system.MethodsThe SmartLiver system utilises an intuitive display to enable the surgeon to compare the positions of landmarks visible in both a projected model and in the live video stream. From this the surgeon can estimate the system accuracy when using the system to locate subsurface targets not visible in the live video. Visible landmarks may be either point or line features. We test the validity of the algorithm using an anatomically representative liver phantom, applying simulated perturbations to achieve clinically realistic overlay errors. We then apply the algorithm to in vivo data.ResultsThe phantom results show that using projected errors of surface features provides a reliable predictor of subsurface target registration error for a representative human liver shape. Applying the algorithm to in vivo data gathered with the SmartLiver image-guided surgery system shows that the system is capable of accuracies around 12 mm; however, achieving this reliably remains a significant challenge.ConclusionWe present an in vivo quantitative evaluation of the SmartLiver image-guided surgery system, together with a validation of the evaluation algorithm. This is the first quantitative in vivo analysis of an augmented reality system for laparoscopic surgery.

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“…Both surface-based methods have demonstrated effective correction of soft-tissue deformation in phantom and clinical applications for hepatic resection. [26][27][28][29][30] With respect to thermal dose guidance, predictive, biophysical modeling of MWA presents a strong alternative to the manufacturer-provided estimates of ablation outcome by utilizing numerical approaches to solve the physical governing equations defining energy deposition and heat transfer. Other direct thermographic measurement strategies such as MR 31 and US 32 thermography are on the horizon, but these also have high technical and economic hurdles for practical use in the operating room or interventional suite.…”

Section: Localization and Therapy Guidancementioning

confidence: 99%

Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework

2019

J. Med. Imag.

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We compare a surface-driven, model-based deformation correction method to a clinically relevant rigid registration approach within the application of image-guided microwave ablation for the purpose of demonstrating improved localization and antenna placement in a deformable hepatic phantom. Furthermore, we present preliminary computational modeling of microwave ablation integrated within the navigational environment to lay the groundwork for a more comprehensive procedural planning and guidance framework. To achieve this, we employ a simple, retrospective model of microwave ablation after registration, which allows a preliminary evaluation of the combined therapeutic and navigational framework. When driving registrations with full organ surface data (i.e., as could be available in a percutaneous procedure suite), the deformation correction method improved average ablation antenna registration error by 58.9% compared to rigid registration (i.e., 2.5 AE 1.1 mm, 5.6 AE 2.3 mm of average target error for corrected and rigid registration, respectively) and on average improved volumetric overlap between the modeled and ground-truth ablation zones from 67.0 AE 11.8% to 85.6 AE 5.0% for rigid and corrected, respectively. Furthermore, when using sparse-surface data (i.e., as is available in an open surgical procedure), the deformation correction improved registration error by 38.3% and volumetric overlap from 64.8 AE 12.4% to 77.1 AE 8.0% for rigid and corrected, respectively. We demonstrate, in an initial phantom experiment, enhanced navigation in image-guided hepatic ablation procedures and identify a clear multiphysics pathway toward a more comprehensive thermal dose planning and deformation-corrected guidance framework.

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

Cited by 42 publications

References 26 publications

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

In vivo estimation of target registration errors during augmented reality laparoscopic surgery

Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework

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