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He S, Qian Y, Zhu X, Liao X, Heng PA, Feng Z, Wang Q. Versatile cutting fracture evolution modeling for deformable object cutting simulation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106749. [PMID: 35334344 DOI: 10.1016/j.cmpb.2022.106749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 02/09/2022] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Soft body cutting simulation is the core module of virtual surgical training systems. By making full use of the powerful computing resources of modern computers, the existing methods have already met the needs of real-time interaction. However, there is still a lack of high realism. The main reason is that most current methods follows the "Intersection-IS-Fracture" mode, namely cutting fracture occurs as long as the cutting blade intersects with the object. To model real-life cutting phenomenon considering deformable objects' fracture resistance, this paper presents a highly realistic virtual cutting simulation algorithm by introducing an energy-based cutting fracture evolution model. METHODS We design the framework based on the co-rotational linear FEM model to support large deformations of soft objects and also adopt the composite finite element method (CFEM) to balance between simulation accuracy and efficiency. Then, a cutting plane constrained Griffth's energy minimization scheme is proposed to determine when and how to generate a new cut. Moreover, to provide the contact effect before the fracture occurs, we design a material-aware adaptation scheme that can guarantee indentation consistent with the cutting tool blade and visually plausible indentation-induced deformation to avoiding large computational effort. RESULTS AND CONCLUSION The experimental results demonstrate that the proposed algorithm is feasible for generating highly realistic cutting simulation results of different objects with various materials and geometrical characteristics while introducing a negligible computational cost. Besides, for different blade shapes, the proposed algorithm can produce highly consistent indentation and fracture. Qualitative evaluation and performance analysis indicate the versatility of the proposed algorithm.
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Affiliation(s)
- Sirui He
- College of Computer Science, Sichuan University, China; Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Yinling Qian
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Xin Zhu
- College of Computer Science, Sichuan University, China; Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Xiangyun Liao
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong; Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Ziliang Feng
- College of Computer Science, Sichuan University, China
| | - Qiong Wang
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China.
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Drakopoulos F, Tsolakis C, Angelopoulos A, Liu Y, Yao C, Kavazidi KR, Foroglou N, Fedorov A, Frisken S, Kikinis R, Golby A, Chrisochoides N. Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems. Front Digit Health 2021; 2:613608. [PMID: 34713074 PMCID: PMC8521897 DOI: 10.3389/fdgth.2020.613608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/23/2020] [Indexed: 12/21/2022] Open
Abstract
Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.
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Affiliation(s)
- Fotis Drakopoulos
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States
| | - Christos Tsolakis
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
| | - Angelos Angelopoulos
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
| | - Yixun Liu
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States
| | - Chengjun Yao
- Department of Neurosurgery, Huashan Hospital, Shanghai, China
| | | | - Nikolaos Foroglou
- Department of Neurosurgery, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Sarah Frisken
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Alexandra Golby
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.,Department of Neurosurgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Nikos Chrisochoides
- Center for Real-Time Computing, Old Dominion University, Norfolk, VA, United States.,Department of Computer Science, Old Dominion University, Norfolk, VA, United States
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Terzano M, Dini D, Rodriguez Y Baena F, Spagnoli A, Oldfield M. An adaptive finite element model for steerable needles. Biomech Model Mechanobiol 2020; 19:1809-1825. [PMID: 32152795 PMCID: PMC7502456 DOI: 10.1007/s10237-020-01310-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 02/17/2020] [Indexed: 11/28/2022]
Abstract
Penetration of a flexible and steerable needle into a soft target material is a complex problem to be modelled, involving several mechanical challenges. In the present paper, an adaptive finite element algorithm is developed to simulate the penetration of a steerable needle in brain-like gelatine material, where the penetration path is not predetermined. The geometry of the needle tip induces asymmetric tractions along the tool–substrate frictional interfaces, generating a bending action on the needle in addition to combined normal and shear loading in the region where fracture takes place during penetration. The fracture process is described by a cohesive zone model, and the direction of crack propagation is determined by the distribution of strain energy density in the tissue surrounding the tip. Simulation results of deep needle penetration for a programmable bevel-tip needle design, where steering can be controlled by changing the offset between interlocked needle segments, are mainly discussed in terms of penetration force versus displacement along with a detailed description of the needle tip trajectories. It is shown that such results are strongly dependent on the relative stiffness of needle and tissue and on the tip offset. The simulated relationship between programmable bevel offset and needle curvature is found to be approximately linear, confirming empirical results derived experimentally in a previous work. The proposed model enables a detailed analysis of the tool–tissue interactions during needle penetration, providing a reliable means to optimise the design of surgical catheters and aid pre-operative planning.
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Affiliation(s)
- Michele Terzano
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy
| | - Daniele Dini
- Department of Mechanical Engineering, Imperial College London, Exhibition Road, London, SW7 2AZ, UK.
| | | | - Andrea Spagnoli
- Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124, Parma, Italy
| | - Matthew Oldfield
- Department of Mechanical Engineering Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, UK
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A Novel Haptic Interactive Approach to Simulation of Surgery Cutting Based on Mesh and Meshless Models. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:9204949. [PMID: 29850006 PMCID: PMC5925175 DOI: 10.1155/2018/9204949] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/20/2017] [Accepted: 12/03/2017] [Indexed: 12/03/2022]
Abstract
In the present work, the majority of implemented virtual surgery simulation systems have been based on either a mesh or meshless strategy with regard to soft tissue modelling. To take full advantage of the mesh and meshless models, a novel coupled soft tissue cutting model is proposed. Specifically, the reconstructed virtual soft tissue consists of two essential components. One is associated with surface mesh that is convenient for surface rendering and the other with internal meshless point elements that is used to calculate the force feedback during cutting. To combine two components in a seamless way, virtual points are introduced. During the simulation of cutting, the Bezier curve is used to characterize smooth and vivid incision on the surface mesh. At the same time, the deformation of internal soft tissue caused by cutting operation can be treated as displacements of the internal point elements. Furthermore, we discussed and proved the stability and convergence of the proposed approach theoretically. The real biomechanical tests verified the validity of the introduced model. And the simulation experiments show that the proposed approach offers high computational efficiency and good visual effect, enabling cutting of soft tissue with high stability.
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In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction. Sci Rep 2016; 6:28654. [PMID: 27387301 PMCID: PMC4937391 DOI: 10.1038/srep28654] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/07/2016] [Indexed: 11/08/2022] Open
Abstract
Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).
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Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery. Ann Biomed Eng 2015; 44:128-38. [PMID: 26354118 DOI: 10.1007/s10439-015-1433-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 08/18/2015] [Indexed: 01/14/2023]
Abstract
With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.
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Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery. Med Image Anal 2013; 17:974-96. [DOI: 10.1016/j.media.2013.04.003] [Citation(s) in RCA: 182] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Revised: 04/05/2013] [Accepted: 04/12/2013] [Indexed: 12/16/2022]
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A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner. Int J Comput Assist Radiol Surg 2013; 9:669-81. [PMID: 24293030 PMCID: PMC4082653 DOI: 10.1007/s11548-013-0958-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2013] [Accepted: 10/23/2013] [Indexed: 12/31/2022]
Abstract
BACKGROUND Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application. METHODS We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance. RESULTS The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %). CONCLUSIONS The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.
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Simpson AL, Dumpuri P, Ondrake JE, Weis JA, Jarnagin WR, Miga MI. Preliminary study of a novel method for conveying corrected image volumes in surgical navigation. Int J Med Robot 2012; 9:109-18. [PMID: 22991306 DOI: 10.1002/rcs.1459] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2012] [Indexed: 11/11/2022]
Abstract
BACKGROUND Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable? METHODS We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume. RESULTS A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target. CONCLUSIONS These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.
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Affiliation(s)
- Amber L Simpson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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Sadeghi-Naini A, Shirzadi Z, Samani A. Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures. ACTA ACUST UNITED AC 2012; 17:211-20. [DOI: 10.3109/10929088.2012.708788] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Simpson AL, Dumpuri P, Jarnagin WR, Miga MI. Model-Assisted Image-Guided Liver Surgery Using Sparse Intraoperative Data. STUDIES IN MECHANOBIOLOGY, TISSUE ENGINEERING AND BIOMATERIALS 2012. [DOI: 10.1007/8415_2012_117] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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