Resumen: Para la determinación del área quemada y la severidad asociada del incendio de Sierra de Luna (Zaragoza), ocurrido el 4 de julio de 2015, se han calculado tres índices espectrales derivados de Landsat-8: NDVI, NBR y BAI. Comparando los resultados obtenidos por cada uno de ellos, en un incendio con extensas zonas de cultivo entre zonas arboladas, se ha determinado que la combinación de ΔNBR y BAI mejora sustancialmente la determinación del área realmente quemada, tanto en su perímetro exterior como en las zonas aisladas no quemadas de su interior. Para el cálculo de la severidad, se propone una metodología basada en el análisis de las diferencias de NBR, antes y después del incendio, y su combinación con el BAI, en función del valor previo al incendio de los índices NBR y de NDVI.Palabras clave: severidad, Landsat, NDVI, NBR, BAI.
Critical analysis of severity indices and affected surface by the wildland fire on Sierra de Luna (Zaragoza)Abstract: To determine the area burned by fire and its associated severity related to this forest fire taken place in Sierra de Luna (Zaragoza), on July 4 th , 2015, three spectral indices derived from Landsat-8 imagery have been calculated: NDVI, NBR and BAI. Comparing the results obtained from each of them, in a wildland fire with extensive crop areas surrounded by forested areas, it has been demonstrated that combination of ΔNBR and BAI substantially improves the calculation of the burned area, concerning both in its external perimeter and in the unburned zones inside of the perimeter. For severity calculation is proposed a new methodology based on before and after NBR differences and its BAI combination, as a function pre-fire values of NBR and NDVI indices.
Unlike the usual numerical FEM approach to determine the thermally affected layer during the grinding process, we propose a simple analytical approach to estimate the depth of thermal penetration. For this purpose, the one-dimensional definition of depth of thermal penetration is applied to the two-dimensional heat transfer models of straight grinding. A method for computing the depth of thermal penetration in these two-dimensional models is derived and compared to the one-dimensional approximation. For dry grinding, it turns out that the one-dimensional approximation is quite accurate when we consider a moderate percentage in the temperature fall beneath the surface, regardless the type of heat flux profile entering into the workpiece (i.e. constant, linear, triangular or parabolic). In wet grinding, the latter is true if we consider a constant heat flux profile and a high Peclet number, i.e. Pe > 5. Finally, the one-and two-dimensional approaches calculating analytically the depth of thermal penetration have been compared to the temperature field numerically evaluated by a three-dimensional FEM simulation given in the literature, obtaining a quite good agreement.
Synthetic computed tomography (CT) images derived from magnetic resonance images (MRI) are of interest for radiotherapy planning and positron emission tomography (PET) attenuation correction. In recent years, deep learning implementations have demonstrated improvement over atlasbased and segmentation-based methods. Nevertheless, several open questions remain to be addressed, such as which are the best MRI sequence and neural network architecture. In this work, we compared the performance of different combinations of two common MRI sequences (T1-and T2-weighted), and three state-of-the-art neural networks designed for medical image processing (Vnet, HighRes3dNet and ScaleNet). The experiments were conducted on brain datasets from a public database. Our results suggest that T1 images performs better than T2, but the results further improve when combining both sequences. The lowest mean average error over the entire head (MAE = 95.37 ± 11.70 HU) was achieved combining T1 and T2 scans with ScaleNet. All tested deep learning models achieved significantly lower MAE (p < 0.05) than a well-known atlasbased method.
Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.
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