2006
Brain tumor image segmentation using neural networks
M. Martín-Landrove, R. Villalta
Proc. Intl. Soc. Mag. Reson. Med. 14 (2006)
Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool that allows distinguishing brain malignant tumors from non-anaplastic tumors [1]. Metabolic maps can be obtained by the Chemical Shift Imaging (CSI) technique but they lack the spatial resolution necessary for therapy considerations [2,3]. Relaxation studies have been used long ago for the assessment of tumors, being the T2-map of a tissue often used as a basis for interpreting clinical images [4]. The combination of both techniques allows for the determination of nosologic maps with appropriate spatial resolution to establish, through segmentation, an accurate determination of Gross Tumor Volume or GTV commonly used in radiotherapy treatment planning. Neural networks have been extensively used for pattern recognition and classification. In the present work, it is proposed the use neural networks to obtain nosologic maps using information coming from MRS and Relaxometry.
Segmentation of brain tumor images using in vivo spectroscopy, relaxometry and diffusometry by magnetic resonance
M. Martín-Landrove
REVISTA MEXICANA DE FíSICA S 52 (3) 55–59, 2006
A new methodology is developed for the segmentation of brain tumor images using information obtained by different magnetic resonance techniques such as in vivo spectroscopy, relaxometry and diffusometry. In vivo spectroscopy is used as a sort of virtual biopsy to characterize the different tissue types present in the lesion (active tumor, necrotic tissue or edema and normal or non-affected tissue). Due to the fact that in vivo spectroscopy information lacks the spatial resolution for treatment considerations, this information has to be combined or fused with images obtained by relaxometry and diffusometry with excellent spatial resolution. Some segmentation schemes are presented and discussed, using the high spatial resolution techniques individually or combined. The results show that segmentation done in this way is highly reliable for the application of future therapies such as radiosurgery or radiotherapy.
Nosologic maps of brain tumor images obtained from combination of different MRI modalities
Miguel Martín-Landrove
Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3, 2006
Tissue classification is a necessary step to obtain the spatial distribution of a pathology or nosologic map and typically it is performed by the combination of different medical image modalities, including histopathological studies. In previous work, combination of different magnetic resonance (MR) modalities such as in vivo spectroscopy, relaxometry and diffusometry have been proposed to obtain nosologic maps with appropriate spatial resolution for treatment considerations. Due to the low spatial resolution of localized in vivo spectroscopy and low longitudinal resolution in relaxation and diffusion-weighted images a partial volume problem is always present. Present work attempts to overcome this problem by careful analysis of transversal relaxation rate and apparent diffusion coefficient distributions obtained by an Inverse Laplace Transform Algorithm (ILTA).
Stochastic approach to ill-posed inverse Laplace transform applications
Rafael Martín, Miguel Martín-Landrove
Simulación y Modelado en Ingeniería y Ciencias, B. Gámez, D. Ojeda, G. Larrazábal, M. Cerrolaza (Eds.), SVMNI, 2006
The ill-posed nature of the numerical inverse Laplace transform in applications related to signal analysis in time domain has been studied extensively in the literature with very limited success. Approaches like SVD and regularization methods are not able to provide the information for entire spectrum for relaxation rates since they have to work in a predefined relaxation rate reduced domain. In this work a method capable to work with the entire relevant relaxation rate domain based in a stochastic approach is examined. This method considers a relaxation rate spectrum as a superposition of three-parameter elementary functions and a version of simulated annealing algorithm which chooses the right position in the relaxation rate domain for a small set of these elementary functions. Different replica groups with the same original data and a cost function of the same order are obtained as a result of a parallel computation and their superposition is going to provide the final spectrum evaluation. Applications of this new procedure to brain tumor studies, phantom gel dosimetry in three dimensions for radiation therapy applications and rock core analysis are also discussed.
Brain tumor image segmentation using in vivo spectroscopy, relaxometry and diffusometry by magnetic resonance
Miguel Martín-Landrove, Igor Bautista, Raúl Villalta
Simulación y Modelado en Ingeniería y Ciencias, B. Gámez, D. Ojeda, G. Larrazábal, M. Cerrolaza (Eds.), SVMNI, 2006
A methodology is developed for the segmentation of brain tumor images combining MR modalities: in vivo spectroscopy, relaxometry and diffusometry. In vivo spectroscopy is used to characterize tissue types in the lesion, but lacks the spatial resolution for treatment considerations. The relaxation and diffusion coded images were analyzed by means of a novel Inverse Laplace Transform Algorithm (ILTA) to obtain relaxation and diffusion distribution functions. Combination of these MR modalities leads to the construction of nosologic maps to assess the presence of pathology. A simple segmentation scheme based on relaxation and diffusion distributions is presented. Segmentation obtained could be very useful for radiotherapy treatment planning in the definition of Gross Tumor Volume (GTV) and follow-up of brain tumors during cancer therapy.
Measurement of the improvement in ability laparoscopy using a virtual simulator
Bricelis Urbina, Ernesto Coto, José Manuel Motezuma, Marvi Martinez, Miguel Cerrolaza, Omaira Rodríguez
Simulación y Modelado en Ingeniería y Ciencias, B. Gámez, D. Ojeda, G. Larrazábal, M. Cerrolaza (Eds.), SVMNI, 2006
Laparoscopic surgery is a surgical technique which demands surgeon visuospatial and motor skills that seldom are exercised. The surgical instruments handling and laparoscopic camera navigation are complicated tasks in small spaces such as the abdominal cavity, where the visualized image is magnified and the size of the instruments reduces the skill, eliminates the tactile sensation and reduces kinesthetic force feedback to the surgeon. In addition, the surgeon must interpret a 3D space from images transmitted by video to a 2D monitor. LAPAROS is a developed tool thanks to the multidisciplinary work of the Computer Graphics Laboratory, the Bioengineering Center and the Experimental Surgery Institute at the Central University of Venezuela. This system for laparoscopic surgery training presents a dual platform: mechanical and virtual simulator. The mechanical simulator SIMULAP v.1 has been developed bearing in mind design aspects resembling to the human torso. The virtual simulator provides three training levels: basic, average and advanced, each one focused to develop specific abilities and skills in the surgeons. In this work we present the measurement of the effectiveness of the basic level of our system in the laparoscopic training process. The tests were applied to a group of seven expert surgeons in laparoscopy and to eleven medicine students without any knowledge in the area. These tests consisted in the accomplishment of seven training sessions in the virtual simulator and one in the mechanical simulator. The results were obtained through careful measurement of the movement precision, ability in handling of instruments and speed of execution of the exercises proposed in the mechanical simulator.
Electrical impedance tomography: standardizing the procedure in pneumology
Bruno de Lema, Pere Casan, Pere Riu
Arch Bronconeumol. 2006;42(6):299-301
The following conditions are optimal for obtaining an adequate number of informative images by electric impedance tomography: a) patient seated or standing with hands at the nape of the neck; b) breathing at rest; c) recording of at least 300 images (at a frequency of 10 Hz), and d) readings taken at the sixth intercostal space.
Density-dependence as a size-independent regulatory mechanism
Harold P. de Vladar
Journal of Theoretical Biology 238 (2006) 245–256
The growth function of populations is central in biomathematics. The main dogma is the existence of density-dependence mechanisms, which can be modelled with distinct functional forms that depend on the size of the population. One important class of regulatory functions is the y-logistic, which generalizes the logistic equation. Using this model as a motivation, this paper introduces a simple dynamical reformulation that generalizes many growth functions. The reformulation consists of two
equations, one for population size, and one for the growth rate. Furthermore, the model shows that although population is densitydependent, the dynamics of the growth rate does not depend either on population size, nor on the carrying capacity. Actually, the growth equation is uncoupled from the population size equation, and the model has only two parameters, a Malthusian parameter r and a competition coefficient y. Distinct sign combinations of these parameters reproduce not only the family of y-logistics, but also the van Bertalanffy, Gompertz and Potential Growth equations, among other possibilities. It is also shown that, except for two critical points, there is a general size-scaling relation that includes those appearing in the most important allometric theories, including the recently proposed Metabolic Theory of Ecology. With this model, several issues of general interestare discussed such as the growth of animal population, extinctions, cell growth and allometry, and the effect of environment over a population.
