153 research outputs found
Modelling the non-linear viscoelastic behaviour of brain tissue in torsion
No full textBrain tissue accommodates non-linear deformations and exhibits time-dependent mechanical behaviour. The latter is one of the most pronounced features of brain tissue, manifesting itself primarily through viscoelastic effects such as stress relaxation. To investigate its viscoelastic behaviour, we performed ramp-and-hold relaxation tests in torsion on freshly slaughtered cylindrical ovine brain samples (25 mm diameter and ∼10 mm height). The tests were conducted using a commercial rheometer at varying twist rates of {40, 240, 400} rad m−1 s−1, with the twist remaining fixed at ∼88 rad m−1, which generated two independent datasets for torque and normal force. The complete set of viscoelastic material parameters was estimated via a simultaneous fit to the analytical expressions for the torque and normal force predicted by the modified quasi-linear viscoelastic model. The model's predictions were further validated through finite element simulations in FEniCS. Our results show that the modified quasi-linear viscoelastic model—recently reappraised and largely unexploited—accurately fits the experimental data. Moreover, the estimated material parameters are in line with those obtained in previous studies on brain samples under torsion. These material parameters could enhance our understanding of slow-progressing pathologies such as tumour growth or neurodegeneration and inform the development of improved in silico models for brain surgery planning and training. Our novel testing protocol also offers an efficient, robust and reliable method for determining the viscoelastic properties of brain tissue under much more rapid loading conditions, which are of crucial importance for modelling traumatic brain injury.This publication has emanated from research jointly funded by Taighde Éireann – Research Ireland under grant number GOIPG/2024/3552 (Griffen Small), and by the College of Science and Engineering at the University of Galway under the Millennium Fund scheme for the project “Modelling Brain Mechanics” (Valentina Balbi). Francesca Ballatore acknowledges support from the PNRR M4C2 through the project “Made in Italy Circolare e Sostenibile (MICS)”, CUP: E13C22001900001. The authors are grateful to the anonymous reviewers for their constructive criticisms, helpful suggestions and insights.peer-reviewe
Meningioma and peritumoral edema segmentation of preoperative MRI brain scans
No full textThis work focuses the attention on the segmentation of meningioma and peritumoral edema from multispectral brain MR imagery. Precise tumour and edema delineation and volume quantification from preoperative MRI data contribute to formulate surgical indications in elderly patients harbouring intracranial meningioma. The authors propose a fully automatic procedure based on the allied use of Graph Cut and support vector machine. The overall strategy combines the advantages of the image-based and machine learning techniques adopted, optimising the balancing between accuracy and stability/reproducibility of the results. Experimental results, obtained by processing in-house collected data, prove that the method is robust and oriented to the use in clinical practice
Morphoelastic control of gastro-intestinal organogenesis: Theoretical predictions and numerical insights
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Fully automatic brain tumor segmentation by using competitive EM and graph cut
No full textManual MRI brain tumor segmentation is a difficult and time consuming
task which makes computer support highly desirable. This paper
presents a hybrid brain tumor segmentation strategy characterized by the allied
use of Graph Cut segmentation method and Competitive Expectation Maximization
(CEM) algorithm. Experimental results were obtained by processing inhouse
collected data and public data from benchmark data sets. To see if the
proposed method can be considered an alternative to contemporary methods,
the results obtained were compared with those obtained by authors who undertook
the Multi-modal Brain Tumor Segmentation challenge. The results obtained
prove that the method is competitive with recently proposed approaches
Poynting effect of brain matter in torsion
No full textWe investigate experimentally and model theoretically the mechanical behaviour of brain matter in torsion. Using a strain-controlled rheometer, we perform torsion tests on fresh porcine brain samples. We quantify the torque and the normal force required to twist a cylindrical sample at constant twist rate. Data fitting gives a mean value for the shear modulus of mu = 900 +/- 312 Pa and for the second Mooney-Rivlin parameter of c(2) = 297 +/- 189 Pa, indicative of extreme softness. Our results show that brain always displays a positive Poynting effect; in other words, it expands in the direction perpendicular to the plane of twisting. We validate the experiments with finite element simulations and show that when a human head experiences a twisting motion in the horizontal plane, the brain can experience large forces in the axial direction.We thank Badar Rashid for Fig. 1(a–c); David McManus for help
with the dissection of pig heads; Xiaolin Li for technical assistance with the rheometer; Christiane Go¨rgen for help with fitting
in RStudio; and Giuseppe Saccomandi for insightful discussions
on the modelling of torsion. The work has received funding from
the European Union’s Horizon 2020 Research and Innovation
Programme under the Marie Skłodowska-Curie grant agreements No. 705532 (Valentina Balbi and Michel Destrade) and
No. 642662 (Antonia Trotta and Aisling Nı´ Annaidh)
Violenza sessuale in famiglia e diritto vivente
No full textL'autore affronta il delicato tema della violenza sessuale in famiglia, soprattutto alla luce di un panorama normativo decisamente inadeguato e delle forti incertezze giurisprudenziali che lo connotano.The author addresses the delicate issue of sexual violence in the family, especially in the light of a an inadequate regulatory landscape and the strong jurisprudential uncertainties that connote it
Study of the Prognostic Relevance of Longitudinal Brain Atrophy in Post-traumatic Diffuse Axonal Injury Using Graph-Based MRI Segmentation Techniques
No full textDelayed brain atrophy, measured on
seriated MRI volumetric scans may represents an useful biomarker to predict the prognosis of these
patients because data coming from recent observations show relevant correlations between the amount of
WM atrophy and prognosis assessed by neuropsychological tests. Here we present a new graph-based
method for MRI brain segmentation, and its application to the problem of atrophy estimation for
prognostic inference
Glial brain tumor detection by using symmetry analysis
No full textIn this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed.
A clustering method based on energy minimization through Graph-Cut is applied on the volume computed as a difference between the left hemisphere and the right hemisphere mirrored across the symmetry plane. Differential analysis involves the loss the knowledge of the tumor side. Through an histogram analysis the ill hemisphere is recognized. Many experiments are performed to assess the performance of the detection strategy on MRI volumes in presence of tumors varied in terms of shapes positions and intensity levels. The experiments showed good results also in complex situations
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