Ischial pressure ulcer is an important risk for every paraplegic person and a major public health issue. Pressure ulcers appear following excessive compression of buttock's soft tissues by bony structures, and particularly in ischial and sacral bones. Current prevention techniques are mainly based on daily skin inspection to spot red patches or injuries. Nevertheless, most pressure ulcers occur internally and are difficult to detect early. Estimating internal strains within soft tissues could help to evaluate the risk of pressure ulcer. A subject-specific biomechanical model could be used to assess internal strains from measured skin surface pressures. However, a realistic 3D non-linear Finite Element buttock model, with different layers of tissue materials for skin, fat and muscles, requires somewhere between minutes and hours to compute, therefore forbidding its use in a real-time daily prevention context. In this article, we propose to optimize these computations by using a reduced order modeling technique (ROM) based on proper orthogonal decompositions of the pressure and strain fields coupled with a machine learning method. ROM allows strains to be evaluated inside the model interactively (i.e. in less than a second) for any pressure field measured below the buttocks. In our case, with only 19 modes of variation of pressure patterns, an error divergence of one percent is observed compared to the full scale simulation for evaluating the strain field. This reduced model could therefore be the first step towards interactive pressure ulcer prevention in a daily set-up.
International audienceAccurate localization of the target is essential to reduce morbidity during brain tumor removal interventions. Yet, image-guided neurosurgery faces an important issue for large skull openings where brain soft-tissues can exhibit large deformations in the course of surgery. As a consequence of this "brain-shift" the pre-operatively acquired images no longer correspond to reality and subsequent neuronavigation is therefore strongly compromised. In this article we present a neuronavigator which addresses this issue and offers passive help to the surgeon by displaying the position of the guided tools with respect to the corrected location of the anatomical features. This low-cost system relies on localized 2D Doppler ultrasound imaging of the brain which makes it possible to track the vascular tree deformation throughout the intervention. An elastic registration procedure is used to match the shifted tree with its pre-operative structure identified within Magnetic Resonance Angiography images. A patient speci c Finite Element biomechanical model of the brain further extends the resulting sparse deformation field to the overall organ volume. Finally, the estimated global deformation is applied to all pre-operatively available volumetric images or data, such as tumor contours, and the corrected planning is displayed to the surgeon. The system, tested on a patient presenting a large meningioma, was able to compensate within seconds for the intraoperatively observed brain-shift, reducing the mean error on tumor margin localization from 3.5 mm (max=7.6 mm, RMS=3.7 mm) to 0.9 mm (max=1.7 mm, RMS=1.0 mm)
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.