Inter-subject Modelling of Liver Deformation During Radiation Therapy

Siebenthal, Martin von; Székely, Gábor; Lomax, Anthony; Cattin, Philippe C.

doi:10.1007/978-3-540-75757-3_80

Cited by 32 publications

(22 citation statements)

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“…Comparing the displacements of the tracked regions with the regions on the actually acquired slice provides the needed information used for the correction of the previously acquired static atlas. In order to compensate these drifts, we introduce a population-based statistical drift model describing the inter-subject variations of exhalation positions in a shared shape-free coordinate system [8]. Shape-free means, that only the relative differences to the first exhalation position of each subject, the drifts, respectively, are used for modelling.…”

Section: Drift Compensationmentioning

confidence: 99%

3D Organ Motion Prediction for MR-Guided High Intensity Focused Ultrasound

Arnold

Preiswerk

Fasel

et al. 2011

Lecture Notes in Computer Science

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Abstract. MR-guided High Intensity Focused Ultrasound is an emerging non-invasive technique capable of depositing sharply localised energy deep within the body, without affecting the surrounding tissues. This, however, implies exact knowledge of the target's position when treating mobile organs. In this paper we present an atlas-based prediction technique that trains an atlas from time-resolved 3D volumes using 4DMRI, capturing the full patient specific motion of the organ. Based on a breathing signal, the respiratory state of the organ is then tracked and used to predict the target's future position. To additionally compensate for the non-periodic slower organ drifts, the static motion atlas is combined with a population-based statistical exhalation drift model. The proposed method is validated on organ motion data of 12 healthy volunteers. Experiments estimating the future position of the entire liver result in an average prediction error of 1.1 mm over time intervals of up to 13 minutes.

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Section: Drift Compensationmentioning

confidence: 99%

3D Organ Motion Prediction for MR-Guided High Intensity Focused Ultrasound

Arnold

Preiswerk

Fasel

et al. 2011

Lecture Notes in Computer Science

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“…Von Siebenthal et al use previously acquired images to predict respiratory motion [43]. However, the breathing cycle is not reproducible and makes this approach unsatisfactory.…”

Section: Respiratory Motionmentioning

confidence: 99%

Development and validation of real-time simulation of X-ray imaging with respiratory motion

Vidal

Villard

2016

Computerized Medical Imaging and Graphics

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soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: i) the respiration against anatomical data, and ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools.}, pmid = {to appear}, publisher = {Elsevier} } AbstractWe present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: i) the respiration against anatomical data, and ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiographs, simulation of 4D sinograms to test tomography reconstruction tools.

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“…King et al also proposed a method for estimation of intraoperative liver shape by registering the statistical model to an intraoperative 2D ultrasound image. von Siebenthal et al [61] developed an inter-subject statistical respiration model, which was constructed from 4D CT images of multiple subjects. In their method, a 3D surface model of each subject was divided into small cells based on anatomical features on the liver, and then it was registered to a reference shape model by using correspondence of the cells.…”

Section: Livermentioning

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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Inter-subject Modelling of Liver Deformation During Radiation Therapy

Cited by 32 publications

References 10 publications

3D Organ Motion Prediction for MR-Guided High Intensity Focused Ultrasound

3D Organ Motion Prediction for MR-Guided High Intensity Focused Ultrasound

Development and validation of real-time simulation of X-ray imaging with respiratory motion

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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