Alexandre Hostettler

Bulk modulus and volume variation measurement of the liver and the kidneys in vivo using abdominal kinetics during free breathing

This article presents a method of predictive simulation, patient-dependant, in real time of the abdominal organ positions during free breathing. The method, that considers both influence of the abdominal breathing and thoracic breathing, needs a tracking of the patient skin and a model of the patient-specific modification of the diaphragm shape. From a measurement of the abdomen viscera kinematic during free breathing, we evaluate through a finite element analysis, the stress field sustained by the organs for a hyperelastic mechanical behaviour using large strain theory. From this analysis, we deduce an in vivo Poissons ratio and a homogeneous bulk modulus of the liver and kidneys, and compare it to the ones in vitro available in the literature.

Virtual reality and augmented reality applied to laparoscopic and notes procedures

Computer-assisted surgery led to a major improvement in medicine. Such an improvement can be summarized in three major steps. The first one consists in an automated 3D modelling of patients from their medical images. The second one consists in using this modelling in surgical planning and simulator software offering then the opportunity to train the surgical gesture before carrying it out. The last step consists in intraoperatively superimposing preoperative data onto the real view of patients. This augmented reality provides surgeons a view in transparency of their patient allowing to track instruments and improve pathology targeting. We will present here our results in these different domains applied to laparoscopic and NOTES procedures.

Real time simulation of organ motions induced by breathing: first evaluation on patient data

In this paper we present a new method to predict in real time from a preoperative CT image the internal organ motions of a patient induced by his breathing. This method only needs the segmentation of the bones, viscera and lungs in the preoperative image and a tracking of the patient skin motion. Prediction of internal organ motions is very important for radiotherapy since it can allow to reduce the healthy tissue irradiation. Moreover, guiding system for punctures in interventional radiology would reduce significantly their guidance inaccuracy. In a first part, we analyse physically the breathing motion and show that it is possible to predict internal organ motions from the abdominal skin position. Then, we propose an original method to compute from the skin position a deformation field to the internal organs that takes mechanical properties of the breathing into account. Finally, we show on human data that our simulation model can provide a prediction of several organ positions (liver, kidneys, lungs) at 14 Hz with an accuracy within 7 mm

Simulation of pneumoperitoneum for laparoscopic surgery planning

Laparoscopic surgery planning is usually realized on a preoperative image that does not correspond to the operating room conditions. Indeed, the patient undergoes gas insufflation (pneumoperitoneum) to allow instrument manipulation inside the abdomen. This insufflation moves the skin and the viscera so that their positions do no longer correspond to the preoperative image, reducing the benefit of surgical planning, more particularly for the trocar positioning step. A simulation of the pneumoperitoneum influence would thus improve the realism and the quality of the surgical planning. We present in this paper a method to simulate the movement of skin and viscera due to the pneumoperitoneum. Our method requires a segmented preoperative 3D medical image associated to realistic biomechanical parameters only. The simulation is performed using the SOFA simulation engine. The results were evaluated using computed tomography [CT] images of two pigs, before and after pneumoperitoneum. Results show that our method provides a very realistic estimation of skin, viscera and artery positions with an average error within 1 cm.

ORBSLAM-Based Endoscope Tracking and 3D Reconstruction

We aim to track the endoscope location inside the surgical scene and provide 3D reconstruction, in real-time, from the sole input of the image sequence captured by the monocular endoscope. This information offers new possibilities for developing surgical navigation and augmented reality applications. The main benefit of this approach is the lack of extra tracking elements which can disturb the surgeon performance in the clinical routine. It is our first contribution to exploit ORBSLAM, one of the best performing monocular SLAM algorithms, to estimate both of the endoscope location, and 3D structure of the surgical scene. However, the reconstructed 3D map poorly describe textureless soft organ surfaces such as liver. It is our second contribution to extend ORBSLAM to be able to reconstruct a semi-dense map of soft organs. Experimental results on in-vivo pigs, shows a robust endoscope tracking even with organs deformations and partial instrument occlusions. It also shows the reconstruction density, and accuracy against ground truth surface obtained from CT.

A Real-Time Predictive Simulation of Abdominal Organ Positions Induced by Free Breathing

Prediction of abdominal organ positions during free breathing is a major challenge from which several medical applications could benefit. For instance, in radiotherapy it would reduce the healthy tissue irradiation. In this paper, we present a method to predict in real-time the abdominal organs position during free breathing. This method needs an abdo-thoracic preoperative CT image, a second one limited to the diaphragm zone, and a tracking of the patient skin motion. It also needs the segmentation of the skin, the viscera volume and the diaphragm in both preoperative images. First, a physical analysis of the breathing motion shows it is possible to predict abdominal organs position from the skin position and a modeling of the diaphragm motion. Then, we present our original method to compute a deformation field that considers the abdominal and thoracic breathing influence. Finally, we show on two human data that our simulation model can predict several organs position at 50 Hz with accuracy within 2-3 mm.

Registration of Preoperative Liver Model for Laparoscopic Surgery from Intraoperative 3D Acquisition

Augmented reality improves the information display during intervention by superimposing hidden structures like vessels. This support is particularly appreciated in laparoscopy where operative conditions are difficult. Generally, the displayed model comes from a preoperative image which does not undergo the deformations due to pneumoperitoneum. We propose to register a preoperative liver model on intraoperative data obtained from a rotational C-arm 3D acquisition. Firstly, we gather the two models in the same coordinate frame according to anatomical structures. Secondly, preoperative model shape is deformed with respect to the intraoperative data using a biomechanical model. We evaluate our method on two in vivo datasets and obtain an average error of 4 mm for the whole liver surface and 10 mm for the vessel position estimation.

Fast segmentation of abdominal wall: application to sliding effect removal for non-rigid registration

The non-rigid registration of abdominal images is still a big challenge due to the breathing motion. Indeed, the sliding between the abdominal wall and the abdominal viscera makes the local deformation field discontinuous; it means that the classical registration approach, which assumes a smooth global deformation field cannot provide accurate and clinical-required results. Other new approaches intend to add in regularization a term to allow discontinuous deformation field near sliding boundary, however, the performance of such approaches needs to be further evaluated. We propose a new approach to perform abdominal image registration including a priori knowledge of the sliding area. Our strategy is to firstly delineate the abdominal wall in source and target images and create new images containing viscera only. Then a state-of-the-art non-rigid registration algorithm is adopted for the registration of the viscera region. In this paper, we firstly show why and how a quick interactive delineation of the full abdominal wall (AW) can be performed using B-spline interpolation. Secondly, we evaluate our registration approach on arterial and venous phase CT images. The results of our approach are compared to the one obtained using the same algorithm with the same parameters on the original data (without segmentation). The registration errors (mean ± SD) with our approach are: liver (1.94 ± 2.76 mm), left kidney (0.38 ± 0.66 mm), right kidney (0.42 ± 0.82 mm), spleen (4.15 ± 3.68 mm), which is much better than the registration result without segmentation: liver (6.48 ± 10.00 mm), left kidney (3.14 ± 3.39 mm), right kidney (2.79 ± 3.12 mm), spleen (17.45 ± 12.39 mm). The results clearly demonstrate our approach is a promising method to remove the sliding motion effect on the non-rigid registration of abdominal images.

A low cost simulator to practice ultrasound image interpretation and probe manipulation: Design and first evaluation

Ultrasonography is the lowest cost no risk medical imaging technique. However, reading an ultrasound (US) image as well as performing a good US probe positioning remain difficult tasks. Education in this domain is today performed on patients, thus limiting it to the most common cases and clinical practice. In this paper, we present a low cost simulator that allows US image practice and realistic probe manipulation from CT data. More precisely, we tackle in this paper the issue of providing a cost effective realistic interface for the probe manipulation with a basic haptic feedback.

Simulation of the abdominal wall and its arteries after pneumoperitoneum for guidance of port positioning in laparoscopic surgery

During laparoscopic surgery, the trocar insertion can injure arteries of the abdominal wall. Although these arteries are visible in a preoperative computed tomography [CT] with contrast medium, it is difficult for the surgeon to estimate their true intraoperative positions since the pneumoperitoneum dramatically stretches the abdominal wall. A navigation system showing the artery position would thus be very helpful for the surgeon. We present in this paper a method to simulate the position of the abdominal wall and its arteries after pneumoperitoneum. Our method requires a segmented preoperative CT image and an intraoperative surface reconstruction of the skin. The intraoperative skin surface allows us to compute a displacement field of the abdominal wall’s outer surface that we propagate to estimate the artery position.