Capturing contact in mitral valve dynamic closure with fluid-structure interaction simulation.

PURPOSE: Realistic fluid-structure interaction (FSI) simulation of the mitral valve opens the way toward planning for surgical repair. In the literature, blood leakage is identified by measuring the flow rate, but detailed information about closure efficiency is missing. We present in this paper an FSI model that improves the detection of blood leakage by building a map of contact. METHODS: Our model is based on the immersed boundary method that captures a map of contact and perfect closure of the mitral valve, without the presence of orifice holes, which often appear with existing methods. We also identified important factors influencing convergence issues. RESULTS: The method is demonstrated in three typical clinical situations: mitral valve with leakage, bulging, and healthy. In addition to the classical ways of evaluating MV closure, such as stress distribution and flow rate, the contact map provides easy detection of leakage with identification of the sources of leakage and a quality assessment of the closure. CONCLUSIONS: Our method significantly improves the quality of the simulation and allows the identification of regurgitation as well as a spatial evaluation of the quality of valve closure. Comparably fast simulation, ability to simulate large deformation, and capturing detailed contact are the main aspects of the study.

Automatic extraction of the mitral valve chordae geometry for biomechanical simulation.

PURPOSE: Mitral valve computational models are widely studied in the literature. They can be used for preoperative planning or anatomical understanding. Manual extraction of the valve geometry on medical images is tedious and requires special training, while automatic segmentation is still an open problem. METHODS: We propose here a fully automatic pipeline to extract the valve chordae architecture compatible with a computational model. First, an initial segmentation is obtained by sub-mesh topology analysis and RANSAC-like model-fitting procedure. Then, the chordal structure is optimized with respect to objective functions based on mechanical, anatomical, and image-based considerations. RESULTS: The approach has been validated on 5 micro-CT scans with a graph-based metric and has shown an [Formula: see text] accuracy rate. The method has also been tested within a structural simulation of the mitral valve closed state. CONCLUSION: Our results show that the chordae architecture resulting from our algorithm can give results similar to experienced users while providing an equivalent biomechanical simulation.

Fast image-based mitral valve simulation from individualized geometry.

BACKGROUND: Common surgical procedures on the mitral valve of the heart include modifications to the chordae tendineae. Such interventions are used when there is extensive leaflet prolapse caused by chordae rupture or elongation. Understanding the role of individual chordae tendineae before operating could be helpful to predict whether the mitral valve will be competent at peak systole. Biomechanical modelling and simulation can achieve this goal. METHODS: We present a method to semi-automatically build a computational model of a mitral valve from micro CT (computed tomography) scans: after manually picking chordae fiducial points, the leaflets are segmented and the boundary conditions as well as the loading conditions are automatically defined. Fast finite element method (FEM) simulation is carried out using Simulation Open Framework Architecture (SOFA) to reproduce leaflet closure at peak systole. We develop three metrics to evaluate simulation results: (i) point-to-surface error with the ground truth reference extracted from the CT image, (ii) coaptation surface area of the leaflets and (iii) an indication of whether the simulated closed leaflets leak. RESULTS: We validate our method on three explanted porcine hearts and show that our model predicts the closed valve surface with point-to-surface error of approximately 1 mm, a reasonable coaptation surface area, and absence of any leak at peak systole (maximum closed pressure). We also evaluate the sensitivity of our model to changes in various parameters (tissue elasticity, mesh accuracy, and the transformation matrix used for CT scan registration). We also measure the influence of the positions of the chordae tendineae on simulation results and show that marginal chordae have a greater influence on the final shape than intermediate chordae. CONCLUSIONS: The mitral valve simulation can help the surgeon understand valve behaviour and anticipate the outcome of a procedure.

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

We 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 radiograph, simulation of 4D sinograms to test tomography reconstruction tools.

Toward a realistic simulation of organ dissection.

Whilst laparoscopic surgical simulators are becoming increasingly realistic they can not, as yet, fully replicate the experience of live surgery. In particular tissue dissection in one task that is particularly challenging to replicate. Limitation of current attempts to simulate tissue dissection include: poor visual rendering; over simplification of the task and; unrealistic tissue properties. In an effort to generate a more realistic model of tissue dissection in laparoscopic surgery we propose a novel method based on task analysis. Initially we have chosen to model only the basic geometrics of this task rather than a whole laparoscopic procedure. Preliminary work has led to the development of a real time simulator performing organ dissection with a haptic thread at 1000Hz. A virtual cutting tool, manipulated through a haptic device, in combination with 1D and 2D soft-tissue models accuratelyreplicatetheprocessoflaparoscopictissuedissection.

Tuning of patient-specific deformable models using an adaptive evolutionary optimization strategy.

We present and analyze the behavior of an evolutionary algorithm designed to estimate the parameters of a complex organ behavior model. The model is adaptable to account for patient's specificities. The aim is to finely tune the model to be accurately adapted to various real patient datasets. It can then be embedded, for example, in high fidelity simulations of the human physiology. We present here an application focused on respiration modeling. The algorithm is automatic and adaptive. A compound fitness function has been designed to take into account for various quantities that have to be minimized. The algorithm efficiency is experimentally analyzed on several real test cases: 1) three patient datasets have been acquired with the "breath hold" protocol, and 2) two datasets corresponds to 4-D CT scans. Its performance is compared with two traditional methods (downhill simplex and conjugate gradient descent): a random search and a basic real-valued genetic algorithm. The results show that our evolutionary scheme provides more significantly stable and accurate results.

A method to compute respiration parameters for patient-based simulators.

We propose a method to automatically tune a patient-based virtual environment training simulator for abdominal needle insertion. The key attributes to be customized in our framework are the elasticity of soft-tissues and the respiratory model parameters. The estimation is based on two 3D Computed Tomography (CT) scans of the same patient at two different time steps. Results are presented on four patients and show that our new method leads to better results than our previous studies with manually tuned parameters.

Open surgery simulation of inguinal hernia repair.

Inguinal hernia repair procedures are often one of the first surgical procedures faced by junior surgeons. The biggest challenge in this procedure for novice trainees is understanding the 3D spatial relations of the complex anatomy of the inguinal region, which is crucial for the effective and careful handling of the present anatomical structures in order to perform a successful and lasting repair. Such relationships are difficult to illustrate and comprehend through standard learning material. This paper presents our work in progress to develop a simulation-based teaching tool allowing junior surgeons to train the Lichtenstein tension-free open inguinal hernia repair technique for direct and indirect hernias, as well as to enforce their understanding of the spatial relations of the involved anatomy.

Developing an immersive ultrasound guided needle puncture simulator.

We present an integrated system for training ultrasound guided needle puncture. Our aim is to provide a cost effective and validated training tool that uses actual patient data to enable interventional radiology trainees to learn how to carry out image-guided needle puncture. The input data required is a computed tomography scan of the patient that is used to create the patient specific models. Force measurements have been made on real tissue and the resulting data is incorporated into the simulator. Respiration and soft tissue deformations are also carried out to further improve the fidelity of the simulator.

Haptic simulation of the liver with respiratory motion.

During a standard procedure of liver biopsy, the motion due to respiration may be difficult to handle. The patient is often requested to hold his breath or to breathe shallowly. Ideally, this physiological behaviour should be taken into account in a virtual reality biopsy simulator. This paper presents a framework that accurately simulates respiratory motion, allowing for the fine tuning of relevant parameters in order to produce a patient-specific breathing pattern that can then be incorporated into a simulation with real-rime haptic interaction. This work has been done as part of the CRaIVE collaboration [1], which aims to build interventional radiology simulators.

CT scan merging to enhance navigation in interventional radiology simulation.

We present a method to merge two distinct CT scans acquired from different patients such that the second scan can supplement the first when it is missing necessary supporting anatomy. The aim is to provide vascular intervention simulations with full body anatomy. Often, patient CT scans are confined to a localised region so that the patient is not exposed to more radiation than necessary and to increase scanner throughput. Unfortunately, this localised scanning region may be limiting for some applications where surrounding anatomy may be required and where approximate supporting anatomy is acceptable. The resulting merged scan can enhance body navigation simulations with X-ray rendering by providing a complete anatomical reference which may be useful in training and rehearsal. An example of the use of our CT scan merging technique in the field of interventional radiology is described.

A comparison framework for breathing motion estimation methods from 4-D imaging.

Motion estimation is an important issue in radiation therapy of moving organs. In particular, motion estimates from 4-D imaging can be used to compute the distribution of an absorbed dose during the therapeutic irradiation. We propose a strategy and criteria incorporating spatiotemporal information to evaluate the accuracy of model-based methods capturing breathing motion from 4-D CT images. This evaluation relies on the identification and tracking of landmarks on the 4-D CT images by medical experts. Three different experts selected more than 500 landmarks within 4-D CT images of lungs for three patients. Landmark tracking was performed at four instants of the expiration phase. Two metrics are proposed to evaluate the tracking performance of motion-estimation models. The first metric cumulates over the four instants the errors on landmark location. The second metric integrates the error over a time interval according to an a priori breathing model for the landmark spatiotemporal trajectory. This latter metric better takes into account the dynamics of the motion. A second aim of this paper is to estimate the impact of considering several phases of the respiratory cycle as compared to using only the extreme phases (end-inspiration and end-expiration). The accuracy of three motion estimation models (two image registration-based methods and a biomechanical method) is compared through the proposed metrics and statistical tools. This paper points out the interest of taking into account more frames for reliably tracking the respiratory motion.