The Experts below are selected from a list of 59004 Experts worldwide ranked by ideXlab platform
Daniil I. Nikitichev - One of the best experts on this subject based on the ideXlab platform.
-
From medical imaging data to 3D printed Anatomical Models
PLOS ONE, 2017Co-Authors: Thore M. Bücking, Emma R. Hill, James Robertson, Efthymios Maneas, Andrew A. Plumb, Daniil I. NikitichevAbstract:Anatomical Models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create cost-efficient patient-specific Models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to 3D printed physical Models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an Anatomical Model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating Models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.
Max Wyss - One of the best experts on this subject based on the ideXlab platform.
-
MIDA: A multimodal imaging-based detailed Anatomical Model of the human head and neck
PLoS ONE, 2015Co-Authors: Maria Ida Iacono, Esther Akinnagbe, Ioannis Vogiatzis Oikonomidis, Bryn Lloyd, K Bower, Esra Neufeld, Johanna Wolf, Bertram J. Wilm, Deepika Sharma, Max WyssAbstract:Computational Modeling and simulations are increasingly being used to complement experimental testing for analysis of safety and efficacy of medical devices. Multiple voxel- and surface-based whole- and partial-body Models have been proposed in the literature, typically with spatial resolution in the range of 1-2 mm and with 10-50 different tissue types resolved. We have developed a multimodal imaging-based detailed Anatomical Model of the human head and neck, named "MIDA". The Model was obtained by integrating three different magnetic resonance imaging (MRI) modalities, the parameters of which were tailored to enhance the signals of specific tissues: i) structural T1- and T2-weighted MRIs; a specific heavily T2-weighted MRI slab with high nerve contrast optimized to enhance the structures of the ear and eye; ii) magnetic resonance angiography (MRA) data to image the vasculature, and iii) diffusion tensor imaging (DTI) to obtain information on anisotropy and fiber orientation. The unique multimodal high-resolution approach allowed resolving 153 structures, including several distinct muscles, bones and skull layers, arteries and veins, nerves, as well as salivary glands. The Model offers also a detailed characterization of eyes, ears, and deep brain structures. A special automatic atlas-based segmentation procedure was adopted to include a detailed map of the nuclei of the thalamus and midbrain into the head Model. The suitability of the Model to simulations involving different numerical methods, discretization approaches, as well as DTI-based tensorial electrical conductivity, was examined in a case-study, in which the electric field was generated by transcranial alternating current stimulation. The voxel- and the surface-based versions of the Models are freely available to the scientific community.
Thore M. Bücking - One of the best experts on this subject based on the ideXlab platform.
-
From medical imaging data to 3D printed Anatomical Models
PLOS ONE, 2017Co-Authors: Thore M. Bücking, Emma R. Hill, James Robertson, Efthymios Maneas, Andrew A. Plumb, Daniil I. NikitichevAbstract:Anatomical Models are important training and teaching tools in the clinical environment and are routinely used in medical imaging research. Advances in segmentation algorithms and increased availability of three-dimensional (3D) printers have made it possible to create cost-efficient patient-specific Models without expert knowledge. We introduce a general workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to 3D printed physical Models. This process is broken up into three steps: image segmentation, mesh refinement and 3D printing. To lower the barrier to entry and provide the best options when aiming to 3D print an Anatomical Model from medical images, we provide an overview of relevant free and open-source image segmentation tools as well as 3D printing technologies. We demonstrate the utility of this streamlined workflow by creating Models of ribs, liver, and lung using a Fused Deposition Modelling 3D printer.
Maria Ida Iacono - One of the best experts on this subject based on the ideXlab platform.
-
Image_1_Radio-Frequency Safety Assessment of Stents in Blood Vessels During Magnetic Resonance Imaging.JPEG
2018Co-Authors: Kyoko Fujimoto, Leonardo M Angelone, Elena Lucano, Sunder S. Rajan, Maria Ida IaconoAbstract:Purpose: The purpose of this study was to investigate the need for high-resolution detailed Anatomical Modeling to correctly estimate radio-frequency (RF) safety during magnetic resonance imaging (MRI). RF-induced heating near metallic implanted devices depends on the electric field tangential to the device (Etan). Etan and specific absorption rate (SAR) were analyzed in blood vessels of an Anatomical Model to understand if a standard gel phantom accurately represents the potential heating in tissues due to passive vascular implants such as stents.Methods: A numerical Model of an RF birdcage body coil and an Anatomically realistic virtual patient with a native spatial resolution of 1 mm3 were used to simulate the in vivo electric field at 64 MHz (1.5 T MRI system). Maximum values of SAR inside the blood vessels were calculated and compared with peaks in a numerical Model of the ASTM gel phantom to see if the results from the simplified and homogeneous gel phantom were comparable to the results from the Anatomical Model. Etan values were also calculated in selected stent trajectories inside blood vessels and compared with the ASTM result.Results: Peak SAR values in blood vessels were up to ten times higher than those found in the ASTM standard gel phantom. Peaks were found in clinically significant Anatomical locations, where stents are implanted as per intended use. Furthermore, Etan results showed that volume-averaged SAR values might not be sufficient to assess RF safety.Conclusion: Computational Modeling with a high-resolution Anatomical Model indicated higher values of the incident electric field compared to the standard testing approach. Further investigation will help develop a robust safety testing method which reflects clinically realistic conditions.
-
MIDA: A multimodal imaging-based detailed Anatomical Model of the human head and neck
PLoS ONE, 2015Co-Authors: Maria Ida Iacono, Esther Akinnagbe, Ioannis Vogiatzis Oikonomidis, Bryn Lloyd, K Bower, Esra Neufeld, Johanna Wolf, Bertram J. Wilm, Deepika Sharma, Max WyssAbstract:Computational Modeling and simulations are increasingly being used to complement experimental testing for analysis of safety and efficacy of medical devices. Multiple voxel- and surface-based whole- and partial-body Models have been proposed in the literature, typically with spatial resolution in the range of 1-2 mm and with 10-50 different tissue types resolved. We have developed a multimodal imaging-based detailed Anatomical Model of the human head and neck, named "MIDA". The Model was obtained by integrating three different magnetic resonance imaging (MRI) modalities, the parameters of which were tailored to enhance the signals of specific tissues: i) structural T1- and T2-weighted MRIs; a specific heavily T2-weighted MRI slab with high nerve contrast optimized to enhance the structures of the ear and eye; ii) magnetic resonance angiography (MRA) data to image the vasculature, and iii) diffusion tensor imaging (DTI) to obtain information on anisotropy and fiber orientation. The unique multimodal high-resolution approach allowed resolving 153 structures, including several distinct muscles, bones and skull layers, arteries and veins, nerves, as well as salivary glands. The Model offers also a detailed characterization of eyes, ears, and deep brain structures. A special automatic atlas-based segmentation procedure was adopted to include a detailed map of the nuclei of the thalamus and midbrain into the head Model. The suitability of the Model to simulations involving different numerical methods, discretization approaches, as well as DTI-based tensorial electrical conductivity, was examined in a case-study, in which the electric field was generated by transcranial alternating current stimulation. The voxel- and the surface-based versions of the Models are freely available to the scientific community.
Galdames, Francisco José - One of the best experts on this subject based on the ideXlab platform.
-
Brain mr Image segmentation for the construction of an Anatomical Model dedicated to mechanical simulation
2012Co-Authors: Galdames, Francisco JoséAbstract:Comment obtenir des données anatomiques pendant une neurochirurgie ? a été ce qui a guidé le travail développé dans le cadre de cette thèse. Les IRM sont actuellement utilisées en amont de l'opération pour fournir cette information, que ce soit pour le diagnostique ou pour définir le plan de traitement. De même, ces images pre-opératoires peuvent aussi être utilisées pendant l'opération, pour pallier la difficulté et le coût des images per-opératoires. Pour les rendre utilisables en salle d'opération, un recalage doit être effectué avec la position du patient. Cependant, le cerveau subit des déformations pendant la chirurgie, phénomène appelé Brain Shift, ce qui altère la qualité du recalage. Pour corriger cela, d'autres données pré-opératoires peuvent être acquises, comme la localisation de la surface corticale, ou encore des images US localisées en 3D. Ce nouveau recalage permet de compenser ce problème, mais en partie seulement. Ainsi, des modèles mécaniques ont été développés, entre autres pour apporter des solutions à l'amélioration de ce recalage. Ils permettent ainsi d'estimer les déformations du cerveau. De nombreuses méthodes existent pour implémenter ces modèles, selon différentes lois de comportement et différents paramètres physiologiques. Dans tous les cas, cela requiert un modèle anatomique patient-spécifique. Actuellement, ce modèle est obtenu par contourage manuel, ou quelquefois semi-manuel. Le but de ce travail de thèse est donc de proposer une méthode automatique pour obtenir un modèle du cerveau adapté sur l'anatomie du patient, et utilisable pour une simulation mécanique. La méthode implémentée se base sur les modèles déformables pour segmenter les structures anatomiques les plus pertinentes dans une modélisation bio-mécanique. En effet, les membranes internes du cerveau sont intégrées: falx cerebri and tentorium cerebelli. Et bien qu'il ait été démontré que ces structures jouent un rôle primordial, peu d'études les prennent en compte. Par ailleurs, la segmentation résultante de notre travail est validée par comparaison avec des données disponibles en ligne. De plus, nous construisons un modèle 3D, dont les déformations seront simulées en utilisant une méthode de résolution par Éléments Finis. Ainsi, nous vérifions par des expériences l'importance des membranes, ainsi que celle des paramètres physiologiques.The general problem that motivates the work developed in this thesis is: how to obtain Anatomical information during a neurosurgery?. Magnetic Resonance (MR) images are usually acquired before the surgery to provide Anatomical information for diagnosis and planning. Also, the same images are commonly used during the surgery, because to acquire MRI images in the operating room is complex and expensive. To make these images useful inside the operating room, a registration between them and the patient's position has to be processed. The problem is that the brain suffers deformations during the surgery, in a process called brain shift, degrading the quality of registration. To correct this, intra-operative information may be used, for example, the position of the brain surface or US images localized in 3D. The new registration will compensate this problem, but only to a certain extent. Mechanical Models of the brain have been developed as a solution to improve this registration. They allow to estimate brain deformation under certain boundary conditions. In the literature, there are a variety of methods for implementing these Models, different equation laws used for continuum mechanic, and different reported mechanical properties of the tissues. However, a patient specific Anatomical Model is always required. Currently, most mechanical Models obtain the associated Anatomical Model by manual or semi-manual segmentation. The aim of this thesis is to propose and implement an automatic method to obtain a Model of the brain fitted to the patient's anatomy and suitable for mechanical Modeling. The implemented method uses deformable Model techniques to segment the most relevant Anatomical structures for mechanical Modeling. Indeed, the internal membranes of the brain are included: falx cerebri and tentorium cerebelli. Even though the importance of these structures is stated in the literature, only a few of publications include them in the Model. The segmentation obtained by our method is assessed using the most used online databases. In addition, a 3D Model is constructed to validate the usability of the Anatomical Model in a Finite Element Method (FEM). And the importance of the internal membranes and the variation of the mechanical parameters is studied
-
Segmentation d'images IRM du cerveau pour la construction d'un modèle anatomique destiné à la simulation bio-mécanique
HAL CCSD, 2012Co-Authors: Galdames, Francisco JoséAbstract:The general problem that motivates the work developed in this thesis is: how to obtain Anatomical information during a neurosurgery?. Magnetic Resonance (MR) images are usually acquired before the surgery to provide Anatomical information for diagnosis and planning. Also, the same images are commonly used during the surgery, because to acquire MRI images in the operating room is complex and expensive. To make these images useful inside the operating room, a registration between them and the patient's position has to be processed. The problem is that the brain suffers deformations during the surgery, in a process called brain shift, degrading the quality of registration. To correct this, intra-operative information may be used, for example, the position of the brain surface or US images localized in 3D. The new registration will compensate this problem, but only to a certain extent. Mechanical Models of the brain have been developed as a solution to improve this registration. They allow to estimate brain deformation under certain boundary conditions. In the literature, there are a variety of methods for implementing these Models, different equation laws used for continuum mechanic, and different reported mechanical properties of the tissues. However, a patient specific Anatomical Model is always required. Currently, most mechanical Models obtain the associated Anatomical Model by manual or semi-manual segmentation. The aim of this thesis is to propose and implement an automatic method to obtain a Model of the brain fitted to the patient's anatomy and suitable for mechanical Modeling. The implemented method uses deformable Model techniques to segment the most relevant Anatomical structures for mechanical Modeling. Indeed, the internal membranes of the brain are included: falx cerebri and tentorium cerebelli. Even though the importance of these structures is stated in the literature, only a few of publications include them in the Model. The segmentation obtained by our method is assessed using the most used online databases. In addition, a 3D Model is constructed to validate the usability of the Anatomical Model in a Finite Element Method (FEM). And the importance of the internal membranes and the variation of the mechanical parameters is studied.Comment obtenir des données anatomiques pendant une neurochirurgie ? a été ce qui a guidé le travail développé dans le cadre de cette thèse. Les IRM sont actuellement utilisées en amont de l'opération pour fournir cette information, que ce soit pour le diagnostique ou pour définir le plan de traitement. De même, ces images pre-opératoires peuvent aussi être utilisées pendant l'opération, pour pallier la difficulté et le coût des images per-opératoires. Pour les rendre utilisables en salle d'opération, un recalage doit être effectué avec la position du patient. Cependant, le cerveau subit des déformations pendant la chirurgie, phénomène appelé Brain Shift, ce qui altère la qualité du recalage. Pour corriger cela, d'autres données pré-opératoires peuvent être acquises, comme la localisation de la surface corticale, ou encore des images US localisées en 3D. Ce nouveau recalage permet de compenser ce problème, mais en partie seulement. Ainsi, des modèles mécaniques ont été développés, entre autres pour apporter des solutions à l'amélioration de ce recalage. Ils permettent ainsi d'estimer les déformations du cerveau. De nombreuses méthodes existent pour implémenter ces modèles, selon différentes lois de comportement et différents paramètres physiologiques. Dans tous les cas, cela requiert un modèle anatomique patient-spécifique. Actuellement, ce modèle est obtenu par contourage manuel, ou quelquefois semi-manuel. Le but de ce travail de thèse est donc de proposer une méthode automatique pour obtenir un modèle du cerveau adapté sur l'anatomie du patient, et utilisable pour une simulation mécanique. La méthode implémentée se base sur les modèles déformables pour segmenter les structures anatomiques les plus pertinentes dans une modélisation bio-mécanique. En effet, les membranes internes du cerveau sont intégrées: falx cerebri and tentorium cerebelli. Et bien qu'il ait été démontré que ces structures jouent un rôle primordial, peu d'études les prennent en compte. Par ailleurs, la segmentation résultante de notre travail est validée par comparaison avec des données disponibles en ligne. De plus, nous construisons un modèle 3D, dont les déformations seront simulées en utilisant une méthode de résolution par Éléments Finis. Ainsi, nous vérifions par des expériences l'importance des membranes, ainsi que celle des paramètres physiologiques