Simulação de um modelo matemático de crescimento tumoral utilizando diferenças finitas / Simulation of a mathematical model of tumoral growth using finite differences

O trabalho expõe um estudo do modelo matemático não linear de crescimento tumoral, proposto por Kolev e Zubik-kowal (2011). O modelo é descrito por um sistema composto de quatro equações diferenciais parciais que representam a evolução da densidade de células cancerígenas, densidade da matriz extracelular (MEC), concentração de enzima degradativa da matriz (EDM) e concentração dos inibidores teciduais de metaloproteinases. Para fins de simulações numéricas utiliza-se o método de diferenças finitas, em que os termos temporais das equações são discretizados utilizando um método de dois estágios. Nos termos espaciais, são utilizadas diferenças finitas centrais. Apresenta-se um estudo de convergência numérica para o esquema proposto, utilizando soluções analíticas fabricadas em uma geometria retangular. Por fim, realiza-se simulações do modelo de crescimento tumoral, utilizando uma malha não regular que representa a geometria de uma mama feminina. Para simular o modelo na geometria não regular, emprega-se a técnica que consiste em aproximar o contorno do domínio físico por segmentos de malha. As simulações demonstraram que o modelo apresenta características importantes das interações entre as células tumorais e o tecido circundante

Data-driven patient-specific breast modeling: a simple, automatized, and robust computational pipeline

Background: Breast-conserving surgery is the most acceptable option for breast cancer removal from an invasive and psychological point of view. During the surgical procedure, the imaging acquisition using Magnetic Image Resonance is performed in the prone configuration, while the surgery is achieved in the supine stance. Thus, a considerable movement of the breast between the two poses drives the tumor to move, complicating the surgeon's task. Therefore, to keep track of the lesion, the surgeon employs ultrasound imaging to mark the tumor with a metallic harpoon or radioactive tags. This procedure, in addition to an invasive characteristic, is a supplemental source of uncertainty. Consequently, developing a numerical method to predict the tumor movement between the imaging and intra-operative configuration is of significant interest. Methods: In this work, a simulation pipeline allowing the prediction of patient-specific breast tumor movement was put forward, including personalized preoperative surgical drawings. Through image segmentation, a subject-specific finite element biomechanical model is obtained. By first computing an undeformed state of the breast (equivalent to a nullified gravity), the estimated intra-operative configuration is then evaluated using our developed registration methods. Finally, the model is calibrated using a surface acquisition in the intra-operative stance to minimize the prediction error. Findings: The capabilities of our breast biomechanical model to reproduce real breast deformations were evaluated. To this extent, the estimated geometry of the supine breast configuration was computed using a corotational elastic material model formulation. The subject-specific mechanical properties of the breast and skin were assessed, to get the best estimates of the prone configuration. The final results are a Mean Absolute Error of 4.00 mm for the mechanical parameters E_breast = 0.32 kPa and E_skin = 22.72 kPa. The optimized mechanical parameters are congruent with the recent state-of-the-art. The simulation (including finding the undeformed and prone configuration) takes less than 20 s. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 15 to 100 iterations depending on the initial parameters for a total time comprised between 5 to 30 min. To our knowledge, our model offers one of the best compromises between accuracy and speed. The model could be effortlessly enriched through our recent work to facilitate the use of complex material models by only describing the strain density energy function of the material. In a second study, we developed a second breast model aiming at mapping a generic model embedding breast-conserving surgical drawing to any patient. We demonstrated the clinical applications of such a model in a real-case scenario, offering a relevant education tool for an inexperienced surgeon

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described