RESUMO
Electrical impedance tomography (EIT) is an imaging technique that attempts to reconstruct the impedance distribution inside an object from the impedance between electrodes placed on the object surface. The EIT reconstruction problem can be approached as a nonlinear nonconvex optimization problem in which one tries to maximize the matching between a simulated impedance problem and the observed data. This nonlinear optimization problem is often ill-posed, and not very suited to methods that evaluate derivatives of the objective function. It may be approached by simulated annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function, which involves a full simulation of the impedance problem at each iteration. A variation of SA is proposed in which the objective function is evaluated only partially, while ensuring boundaries on the behavior of the modified algorithm.
Assuntos
Algoritmos , Impedância Elétrica , Processamento de Imagem Assistida por Computador/métodos , Tomografia/métodos , Simulação por Computador , Cucumis sativus , Modelos Biológicos , Imagens de FantasmasRESUMO
The EIT reconstruction problem can be solved as an optimization problem where the divergence between a simulated impedance domain and the observed one is minimized. This optimization problem can be solved by a combination of Simulated Annealing (SA) for optimization and Finite Element Method (FEM) for simulation of the impedance domain. This combination has usually a very high computational cost, since SA requires an elevated number of objective function evaluations and those, obtained through FEM, are often expansive enough to make the whole process inviable. In here it is presented a new approach for EIT image reconstructions using SA and partial evaluations of objective functions based on overdetermined linear systems. This new reconstruction approach is evaluated with experimental data and compared with previous approaches.
Assuntos
Impedância Elétrica , Processamento de Imagem Assistida por Computador/métodos , Tomografia/métodos , Algoritmos , Análise dos Mínimos Quadrados , Imagens de Fantasmas , Reprodutibilidade dos TestesRESUMO
This work discusses the use of breathing patterns present in time sequences of MR images in the temporal registration of coronal and sagittal images. The registration is done without the use of any triggering information and any special gas to enhance the contrast. The temporal sequences of images are acquired in free breathing. As coronal and sagittal sequences of images are orthogonal to each other, their intersection corresponds to a segment in the three dimensional space. The registration happens by analyzing this intersection segment that is determined by a coronal-sagittal mapping. A time sequence of this intersection segment can be stacked, defining a two dimension spatio-temporal (2DST) image. It is assumed that the diaphragmatic movement is the principal movement and all the lungs structures do move almost synchronously. The synchronization was realized through a pattern named respiratory function. A Hough transform algorithm, using the respiratory function as input, searches for synchronized movements with the respiratory function. Finally, the composition of coronal and sagittal images that are in the same breathing phase is made by comparison of diaphragmatic respiratory patterns. Several results and conclusions are shown.
Assuntos
Algoritmos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Humanos , Pulmão/anatomia & histologia , Respiração , Testes de Função Respiratória , Fatores de TempoRESUMO
In this work, segmentation is an intermediate step in the registration and 3D reconstruction of the lung, where the diaphragmatic surface is automatically and robustly isolated. Usually, segmentation methods are interactive and use different strategies to combine the expertise of humans and computers. Segmentation of lung MR images is particularly difficult because of the large variation in image quality. The breathing is associated to a standard respiratory function, and through 2D image processing, edge detection and Hough transform, respiratory patterns are obtained and, consequently, the position of points in time are estimated. Temporal sequences of MR images are segmented by considering the coherence in time. This way, the lung silhouette can be determined in every frame, even on frames with obscure edges. The lung region is segmented in two steps: a mask containing the lung region is created, and the Hough transform is applied exclusively to mask pixels. The shape of the mask can have a large variation, and the modified Hough transform can handle such shape variation. The result was checked through temporal registration of coronal and sagittal images.