A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Numerical 3D modeling of heat transfer in human tissues for microwave radiometry monitoring of Brown fat metabolismo

Background: Brown adipose tissue (BAT) plays an important role in whole body metabolism and could potentially mediate weight gain and insulin sensitivity. Although some imaging techniques allow BAT detection, there are currently no viable methods for continuous acquisition of BAT energy expenditure. We present a non-invasive technique for long term monitoring of BAT metabolism using microwave radiometry. Methods: A multilayer 3D computational model was created in HFSS™ with 1.5 mm skin, 3-10 mm subcutaneous fat, 200 mm muscle and a BAT region (2-6 cm3) located between fat and muscle. Based on this model, a log-spiral antenna was designed and optimized to maximize reception of thermal emissions from the target (BAT). The power absorption patterns calculated in HFSS™ were combined with simulated thermal distributions computed in COMSOL® to predict radiometric signal measured from an ultra-low-noise microwave radiometer. The power received by the antenna was characterized as a function of different levels of BAT metabolism under cold and noradrenergic stimulation. Results: The optimized frequency band was 1.5-2.2 GHz, with averaged antenna efficiency of 19%. The simulated power received by the radiometric antenna increased 2-9 mdBm (noradrenergic stimulus) and 4-15 mdBm (cold stimulus) corresponding to increased 15-fold BAT metabolism. Conclusions: Results demonstrated the ability to detect thermal radiation from small volumes (2-6 cm3) of BAT located up to 12 mm deep and to monitor small changes (0.5°C) in BAT metabolism. As such, the developed miniature radiometric antenna sensor appears suitable for non-invasive long term monitoring of BAT metabolism.

Kinematics of local and high-z galaxies through 3D modeling of emission-line datacubes

The kinematics is a fundamental tool to infer the dynamical structure of galaxies and to understand their formation and evolution. Spectroscopic observations of gas emission lines are often used to derive rotation curves and velocity dispersions. It is however difficult to disentangle these two quantities in low spatial-resolution data because of beam smearing. In this thesis, we present 3D-Barolo, a new software to derive the gas kinematics of disk galaxies from emission-line data-cubes. The code builds tilted-ring models in the 3D observational space and compares them with the actual data-cubes. 3D-Barolo works with data at a wide range of spatial resolutions without being affected by instrumental biases. We use 3D-Barolo to derive rotation curves and velocity dispersions of several galaxies in both the local and the high-redshift Universe. We run our code on HI observations of nearby galaxies and we compare our results with 2D traditional approaches. We show that a 3D approach to the derivation of the gas kinematics has to be preferred to a 2D approach whenever a galaxy is resolved with less than about 20 elements across the disk. We moreover analyze a sample of galaxies at z~1, observed in the H-alpha line with the KMOS/VLT spectrograph. Our 3D modeling reveals that the kinematics of these high-z systems is comparable to that of local disk galaxies, with steeply-rising rotation curves followed by a flat part and H-alpha velocity dispersions of 15-40 km/s over the whole disks. This evidence suggests that disk galaxies were already fully settled about 7-8 billion years ago. In summary, 3D-Barolo is a powerful and robust tool to separate physical and instrumental effects and to derive a reliable kinematics. The analysis of large samples of galaxies at different redshifts with 3D-Barolo will provide new insights on how galaxies assemble and evolve throughout cosmic time.

3D-modeling of Mercury's solar wind sputtered surface-exosphere environment

The efficiency of sputtered refractory elements by H+ and He++ solar wind ions from Mercury's surface and their contribution to the exosphere are studied for various solar wind conditions. A 3D solar wind-planetary interaction hybrid model is used for the evaluation of precipitation maps of the sputter agents on Mercury's surface. By assuming a global mineralogical surface composition, the related sputter yields are calculated by means of the 2013 SRIM code and are coupled with a 3D exosphere model. Because of Mercury's magnetic field, for quiet and nominal solar wind conditions the plasma can only precipitate around the polar areas, while for extreme solar events (fast solar wind, coronal mass ejections, interplanetary magnetic clouds) the solar wind plasma has access to the entire dayside. In that case the release of particles form the planet's surface can result in an exosphere density increase of more than one order of magnitude. The corresponding escape rates are also about an order of magnitude higher. Moreover, the amount of He++ ions in the precipitating solar plasma flow enhances also the release of sputtered elements from the surface in the exosphere. A comparison of our model results with MESSENGER observations of sputtered Mg and Ca elements in the exosphere shows a reasonable quantitative agreement. (C) 2015 Elsevier Ltd. All rights reserved.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Morphologic evaluation of ruptured abdominal aortic aneurysm by 3D modeling

This thesis was created in Word and converted to PDF using Mac OS X 10.7.5 Quartz PDFContext.

Free tools and strategies for the generation of 3D finite element meshes: Modeling of the cardiac structures

The Finite Element Method is a well-known technique, being extensively applied in different areas. Studies using the Finite Element Method (FEM) are targeted to improve cardiac ablation procedures. For such simulations, the finite element meshes should consider the size and histological features of the target structures. However, it is possible to verify that some methods or tools used to generate meshes of human body structures are still limited, due to nondetailed models, nontrivial preprocessing, or mainly limitation in the use condition. In this paper, alternatives are demonstrated to solid modeling and automatic generation of highly refined tetrahedral meshes, with quality compatible with other studies focused on mesh generation. The innovations presented here are strategies to integrate Open Source Software (OSS). The chosen techniques and strategies are presented and discussed, considering cardiac structures as a first application context. © 2013 E. Pavarino et al.

Basic ingredients for mathematical modeling of tumor growth in vitro: Cooperative effects and search for space

Based on the literature data from HT-29 cell monolayers, we develop a model for its growth, analogous to an epidemic model, mixing local and global interactions. First, we propose and solve a deterministic equation for the progress of these colonies. Thus, we add a stochastic (local) interaction and simulate the evolution of an Eden-like aggregate by using dynamical Monte Carlo methods. The growth curves of both deterministic and stochastic models are in excellent agreement with the experimental observations. The waiting times distributions, generated via our stochastic model, allowed us to analyze the role of mesoscopic events. We obtain log-normal distributions in the initial stages of the growth and Gaussians at long times. We interpret these outcomes in the light of cellular division events: in the early stages, the phenomena are dependent each other in a multiplicative geometric-based process, and they are independent at long times. We conclude that the main ingredients for a good minimalist model of tumor growth, at mesoscopic level, are intrinsic cooperative mechanisms and competitive search for space. © 2013 Elsevier Ltd.

Enhancement of blood-tumor barrier permeability by Sar-[D-Phe8]des-Arg9BK, a metabolically resistant bradykinin B1 agonist, in a rat C6 glioma model

Abstract Background While it is well known that bradykinin B2 agonists increase plasma protein extravasation (PPE) in brain tumors, the bradykinin B1 agonists tested thus far are unable to produce this effect. Here we examine the effect of the selective B1 agonist bradykinin (BK) Sar-[D-Phe8]des-Arg9BK (SAR), a compound resistant to enzymatic degradation with prolonged activity on PPE in the blood circulation in the C6 rat glioma model. Results SAR administration significantly enhanced PPE in C6 rat brain glioma compared to saline or BK (p < 0.01). Pre-administration of the bradykinin B1 antagonist [Leu8]-des-Arg (100 nmol/Kg) blocked the SAR-induced PPE in the tumor area. Conclusions Our data suggest that the B1 receptor modulates PPE in the blood tumor barrier of C6 glioma. A possible role for the use of SAR in the chemotherapy of gliomas deserves further study.

Biophysical 3D modeling approach of the Canary Current System: a ROMS-PISCES coupling study

Máster Universitario en Oceanografía

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Statistical modeling of the eye for multimodal treatment planning for external beam radiation therapy of intraocular tumors

Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors.

Three dimensional (3D) microstructure-based modeling of interfacial decohesion in particle reinforced metal matrix composites

Modeling and prediction of the overall elastic–plastic response and local damage mechanisms in heterogeneous materials, in particular particle reinforced composites, is a very complex problem. Microstructural complexities such as the inhomogeneous spatial distribution of particles, irregular morphology of the particles, and anisotropy in particle orientation after secondary processing, such as extrusion, significantly affect deformation behavior. We have studied the effect of particle/matrix interface debonding in SiC particle reinforced Al alloy matrix composites with (a) actual microstructure consisting of angular SiC particles and (b) idealized ellipsoidal SiC particles. Tensile deformation in SiC particle reinforced Al matrix composites was modeled using actual microstructures reconstructed from serial sectioning approach. Interfacial debonding was modeled using user-defined cohesive zone elements. Modeling with the actual microstructure (versus idealized ellipsoids) has a significant influence on: (a) localized stresses and strains in particle and matrix, and (b) far-field strain at which localized debonding takes place. The angular particles exhibited higher degree of load transfer and are more sensitive to interfacial debonding. Larger decreases in stress are observed in the angular particles, because of the flat surfaces, normal to the loading axis, which bear load. Furthermore, simplification of particle morphology may lead to erroneous results.