Computational Models for Image-Guided Robot-Assisted and Simulated Medical Interventions

Delingette, Hervé; Pennec, Xavier; Soler, Luc; Marescaux, Jacques; Ayache, Nicholas

doi:10.1109/jproc.2006.880718

Cited by 33 publications

(17 citation statements)

References 87 publications

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“…[1], k and m are the so-called Lamé parameters, q is the density and F is the applied force. Under the assumption of linear motion in an isotropic medium Eq.…”

Section: Methods Governing Equationmentioning

confidence: 99%

“…Computational models in medical simulation have been identified, primarily falling into three groups: geometrical modeling, physical modeling, and physiological modeling (1). Haptic rendering forms an important role in physical modeling, because on top of visual feedback, it allows objects to be touched in virtual space using a device that transmits a response to a person through an appropriate force and torque feedback.…”

Section: Introductionmentioning

confidence: 99%

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Haptic interaction of organs based on the spherical harmonic representation

Yang

Vegh

Tieng

et al. 2010

Concepts Magn Reson

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Visual and haptic organ interactions in real time are essential in virtual reality-based medical simulations to provide training, early diagnosis, and improved treatment planning. This work presents a robust method of computing the deformation of an elastic object when external forces are applied. Point-based haptic interaction between the user of the haptic device and an object is outlined and is formulated as a deformation problem over the surface. The model is expressed in the spherical harmonic basis, from which both haptic device force feedback and object surface updates are calculated. Haptic and visual updates are performed in the same basis, in turn providing significant benefits of computing efficiency and multiresolution modeling. Partially, this is due to the fact that in the model the objects are represented as surfaces. A haptic device is used to probe different prostates that have been segmented and reconstructed from MR images to validate the findings. The calculated deformation and feedback force is further validated against the established finite element method.

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“…[1], k and m are the so-called Lamé parameters, q is the density and F is the applied force. Under the assumption of linear motion in an isotropic medium Eq.…”

Section: Methods Governing Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Haptic interaction of organs based on the spherical harmonic representation

Yang

Vegh

Tieng

et al. 2010

Concepts Magn Reson

View full text Add to dashboard Cite

show abstract

“…In this regard, a thorough biomechanical model of the anatomy has been proposed, theorized, supported and promoted by the European Union [25][26][27] in order to provide a complete dynamic registration compensating all physiological parameters and decubitus of the patient in the surgical room, which may differ from those in the radiological department.…”

Section: Introductionmentioning

confidence: 99%

Analytic description of the image to patient torso registration problem in image guided interventions

Ferrari

Moglia

Ferrari

2015

JBEI

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Objective: The accurate registration of virtual pre-operative information of the human body anatomy, obtained as images with imaging devices, with real intra-operative information is one of the key aspects on which effective Image Guided Surgery (IGS) is based. The registration of pre-operative images on the real patient, during abdominal and thoracic interventions, is influenced by many parameters, which in many cases are influenced each other, thus making it often difficult to define the problem and consequently to solve it for each specific kind of intervention. The objective of this paper is to obtain an analytic description of the 3D image to patient registration problem, which can be more intuitive than the traditional textual descriptions. Methods:The problem is formalized and various parameters affecting the registration are macro-classified in function of their nature.Results: The problem is analytically described discussing for each macro-category of parameters potential solutions to avoid or to reduce their contribution to the registration error. Conclusions:The availability of an analytic description of the image to patient torso registration problem can be beneficial for teaching IGS, to describe existing registration strategies, and to search new ones for each kind of surgery using a systematic approach.

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“…Furthermore, sophisticated patient and interventional simulations using a large amount of computational resources are becoming possible [2], [3], which have been recently demonstrated to be effectively incorporated into intra-procedural assistance in a realtime manner by combining statistical modeling and machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka

Nakamoto

Sato

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

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Computational Models for Image-Guided Robot-Assisted and Simulated Medical Interventions

Cited by 33 publications

References 87 publications

Haptic interaction of organs based on the spherical harmonic representation

Haptic interaction of organs based on the spherical harmonic representation

Analytic description of the image to patient torso registration problem in image guided interventions

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

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