In flight performance and first results of FREGATE

The gamma-ray detector of HETE-2, called FREGATE, has been designed to detect gamma-ray bursts in the energy range [6-400] keV. Its main task is to alert the other instruments of the occurrence of a gamma-ray burst (GRB) and to provide the spectral coverage of the GRB prompt emission in hard X-rays and soft gamma-rays. FREGATE was switched on on October 16, 2000, one week after the successful launch of HETE-2, and has been continuously working since then. We describe here the main characteristics of the instrument, its in-flight performance and we briefly discuss the first GRB observations.Comment: Invited lecture at the Woods Hole 2001 GRB Conference, 8 pages, 15 figure

Towards Interactive Planning of Coil Embolization in Brain Aneurysms

The original publication is available at www.springerlink.comInternational audienceMany vascular pathologies can now be treated in a minimally invasive way thanks to interventional radiology. Instead of open surgery, it allows to reach the lesion of the arteries with therapeutic devices through a catheter. As a particular case, intracranial aneurysms are treated by filling the localized widening of the artery with a set of coils to prevent a rupture due to the weakened arterial wall. Considering the location of the lesion, close to the brain, and its very small size, the procedure requires a combination of careful planning and excellent technical skills. An interactive and reliable simulation, adapted to the patient anatomy, would be an interesting tool for helping the interventional neuroradiologist plan and rehearse a coil embolization procedure. This paper describes an original method to perform interactive simulations of coil embolization and proposes a clinical metric to quantitatively measure how the first coil fills the aneurysm. The simulation relies on an accurate reconstruction of the aneurysm anatomy and a real-time model of the coil for which sliding and friction contacts are taken into account. Simulation results are compared to real embolization procedure and exhibit good adequacy

Retracting and seeking movements during laparoscopic goal-oriented movements. Is the shortest path length optimal?

Aims- Minimally invasive surgery (MIS) requires a high degree of eye–hand coordination from the surgeon. To facilitate the learning process, objective assessment systems based on analysis of the instruments’ motion are being developed. To investigate the influence of performance on motion characteristics, we examined goaloriented movements in a box trainer. In general, goal-oriented movements consist of a retracting and a seeking phase, and are, however, not performed via the shortest path length. Therefore, we hypothesized that the shortest path is not an optimal concept in MIS. Methods-Participants were divided into three groups (experts, residents, and novices). Each participant performed a number of one-hand positioning tasks in a box trainer. Movements of the instrument were recorded with the TrEndo tracking system. The movement from point A to B was divided into two phases: A-M (retracting) and M-B (seeking). Normalized path lengths (given in %) of the two phases were compared. Results- Thirty eight participants contributed. For the retracting phase, we found no significant difference between experts [median (range) %: 152 (129–178)], residents [164 (126–250)], and novices [168 (136–268)]. In the seeking phase, we find a significant difference (<0.001) between experts [180 (172–247)], residents [201 (163–287)], and novices [290 (244–469)]. Moreover, within each group, a significant difference between retracting and seeking phases was observed. Conclusions- Goal-oriented movements in MIS can be split into two phases: retracting and seeking. Novices are less effective than experts and residents in the seeking phase. Therefore, the seeking phase is characteristic of performance differences. Furthermore, the retracting phase is essential, because it improves safety by avoiding intermediate tissue contact. Therefore, the shortest path length, as presently used during the assessment of basic MIS skills, may be not a proper concept for analyzing optimal movements and, therefore, needs to be revised.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

NiftySim: A GPU-based nonlinear finite element package for simulation of soft tissue biomechanics

Purpose NiftySim, an open-source finite element toolkit, has been designed to allow incorporation of high-performance soft tissue simulation capabilities into biomedical applications. The toolkit provides the option of execution on fast graphics processing unit (GPU) hardware, numerous constitutive models and solid-element options, membrane and shell elements, and contact modelling facilities, in a simple to use library. Methods The toolkit is founded on the total Lagrangian explicit dynamics (TLEDs) algorithm, which has been shown to be efficient and accurate for simulation of soft tissues. The base code is written in C ++++ , and GPU execution is achieved using the nVidia CUDA framework. In most cases, interaction with the underlying solvers can be achieved through a single Simulator class, which may be embedded directly in third-party applications such as, surgical guidance systems. Advanced capabilities such as contact modelling and nonlinear constitutive models are also provided, as are more experimental technologies like reduced order modelling. A consistent description of the underlying solution algorithm, its implementation with a focus on GPU execution, and examples of the toolkit’s usage in biomedical applications are provided. Results Efficient mapping of the TLED algorithm to parallel hardware results in very high computational performance, far exceeding that available in commercial packages. Conclusion The NiftySim toolkit provides high-performance soft tissue simulation capabilities using GPU technology for biomechanical simulation research applications in medical image computing, surgical simulation, and surgical guidance applications

Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element method on more than 100 different liver geometries. Considering a target accuracy threshold of 3 mm for the Euclidean Error, four different scenarios were modeled and assessed: a single liver with an arbitrary force applied (99.96% of samples within the accepted error range), a single liver with two simultaneous forces applied (99.84% samples in range), a single liver with different material properties and an arbitrary force applied (98.46% samples in range), and a much more general model capable of modeling the behavior of any liver with an arbitrary force applied (99.01% samples in range for the median liver). The results show that the Machine Learning models perform extremely well on all the scenarios, managing to keep the Mean Euclidean Error under 1 mm in all cases. Furthermore, the proposed model achieves working frequencies above 100Hz on modest hardware (with frequencies above 1000Hz being easily achievable on more powerful GPUs) thus fulfilling the real-time requirements. These results constitute a remarkable improvement in this field and may involve a prompt implementation in clinical practice.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R, also supported by European FEDER funds.Pellicer-Valero, OJ.; Rupérez Moreno, MJ.; Martinez-Sanchis, S.; Martín-Guerrero, JD. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications. 143:1-12. https://doi.org/10.1016/j.eswa.2019.113083S112143Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. arXiv:1605.08695.Brunon, A., Bruyère-Garnier, K., & Coret, M. (2010). Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure. Journal of Biomechanics, 43(11), 2221-2227. doi:10.1016/j.jbiomech.2010.03.038Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., … Huerta, A. (2013). PGD-Based Computational Vademecum for Efficient Design, Optimization and Control. Archives of Computational Methods in Engineering, 20(1), 31-59. doi:10.1007/s11831-013-9080-xClifford, M. A., Banovac, F., Levy, E., & Cleary, K. (2002). Assessment of Hepatic Motion Secondary to Respiration for Computer Assisted Interventions. Computer Aided Surgery, 7(5), 291-299. doi:10.3109/10929080209146038Cotin, S., Delingette, H., & Ayache, N. (2000). A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. The Visual Computer, 16(8), 437-452. doi:10.1007/pl00007215Duysak, A., Zhang, J. J., & Ilankovan, V. (2003). Efficient modelling and simulation of soft tissue deformation using mass-spring systems. International Congress Series, 1256, 337-342. doi:10.1016/s0531-5131(03)00423-0Fung, Y. C., & Skalak, R. (1981). Biomechanics: Mechanical Properties of Living Tissues. Journal of Biomechanical Engineering, 103(4), 231-298. doi:10.1115/1.3138285González, D., Aguado, J. V., Cueto, E., Abisset-Chavanne, E., & Chinesta, F. (2016). kPCA-Based Parametric Solutions Within the PGD Framework. Archives of Computational Methods in Engineering, 25(1), 69-86. doi:10.1007/s11831-016-9173-4González, D., Cueto, E., & Chinesta, F. (2015). Computational Patient Avatars for Surgery Planning. Annals of Biomedical Engineering, 44(1), 35-45. doi:10.1007/s10439-015-1362-zJahya, A., Herink, M., & Misra, S. (2013). A framework for predicting three-dimensional prostate deformation in real time. The International Journal of Medical Robotics and Computer Assisted Surgery, 9(4), e52-e60. doi:10.1002/rcs.1493Lister, K., Gao, Z., & Desai, J. P. (2010). Development of In Vivo Constitutive Models for Liver: Application to Surgical Simulation. Annals of Biomedical Engineering, 39(3), 1060-1073. doi:10.1007/s10439-010-0227-8Lorente, D., Martínez-Martínez, F., Rupérez, M. J., Lago, M. A., Martínez-Sober, M., Escandell-Montero, P., … Martín-Guerrero, J. D. (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications, 71, 342-357. doi:10.1016/j.eswa.2016.11.037Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Myronenko, A., & Xubo Song. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2262-2275. doi:10.1109/tpami.2010.46Niroomandi, S., Alfaro, I., Cueto, E., & Chinesta, F. (2012). Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models. Computer Methods and Programs in Biomedicine, 105(1), 1-12. doi:10.1016/j.cmpb.2010.06.012Plantefève, R., Peterlik, I., Haouchine, N., & Cotin, S. (2015). Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery. Annals of Biomedical Engineering, 44(1), 139-153. doi:10.1007/s10439-015-1419-zLarge elastic deformations of isotropic materials. I. Fundamental concepts. (1948). Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 240(822), 459-490. doi:10.1098/rsta.1948.0002Large elastic deformations of isotropic materials IV. further developments of the general theory. (1948). 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The ISO Long-Wavelength Spectrometer

The Long-Wavelength Spectrometer (LWS) is one of two complementary spectrometers aboard the European Space Agency's Infrared Space Observatory (ISO) (Kessler et al., 1996). It operates over the wavelength range 43 - 196.9 μm at either medium (about 150 to 200) or high (6800 to 9700) spectral resolving power. This Letter describes the instrument and its modes of operation; a companion paper (Swinyard et al, 1996) describes its performance and calibration

Calibration and performance of the ISO Long-Wavelength Spectrometer

The wavelength and flux calibration, and the in-orbit performance of the Infrared Space Observatory Long-Wavelength Spectrometer (LWS) are described. The LWS calibration is mostly complete and the instrument's performance in orbit is largely as expected before launch. The effects of ionising radiation on the detectors, and the techniques used to minimise them are outlined. The overall sensitivity figures achieved in practice are summarised. The standard processing of LWS data is described

Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels

International audienceIntra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3D data onto the deformed brain surface viewed through a surgical microscope. We formulate our non-rigid registration as a Shape-from-Template problem. A pre-operative 3D wire-like deformable model is registered onto a single 2D image of the cortical vessels, which is automatically segmented. This 3D/2D registration drives the underlying brain structures, such as tumors, and compensates for the brain shift in sub-cortical regions. We evaluated our approach on simulated and real data composed of 6 patients. It achieved good quantitative and qualitative results making it suitable for neurosurgical guidance

Calibration and performance of the ISO Long-Wavelength Spectrometer

The wavelength and flux calibration, and the in-orbit performance of the Infrared Space Observatory Long-Wavelength Spectrometer (LWS) are described. The LWS calibration is mostly complete and the instrument's performance in orbit is largely as expected before launch. The effects of ionising radiation on the detectors, and the techniques used to minimise them are outlined. The overall sensitivity figures achieved in practice are summarised. The standard processing of LWS data is described