Longitudinal Voxel-based morphometry with unified segmentation: evaluation on simulated Alzheimer’s disease

The goal of this work is to evaluate Voxel-Based Morphometry and three longitudinally-tailored methods of VBM.We use a cohort of simulated images produced by deforming original scans using a Finite Element Method, guided to emulate Alzheimer-like changes. The simulated images provide quite realistic data with a known pattern of spatial atrophy, with which VBM’s findings can be meaningfully compared. We believe this is the first evaluation of VBM for which anatomically-plausible ‘gold-standard’ results are available. The three longitudinal VBM methods have been implemented within the unified segmentation framework of SPM5; one of the techniques is a newly developed procedure, which shows promising potential

Compressed Quantitative MRI: Bloch Response Recovery through Iterated Projection

Inspired by the recently proposed Magnetic Resonance Fingerprinting technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are: a random pulse excitation sequence following the MRF technique; a random EPI subsampling strategy and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to achieve accurate recovery of the proton density, $T_1$, $T_2$ and off-resonance maps simultaneously from a limited number of samples.Comment: 5 pages 2 figure

An inversion method based on random sampling for real-time MEG neuroimaging

The MagnetoEncephaloGraphy (MEG) is a non-invasive neuroimaging technique with a high temporal resolution which can be successfully used in real-time applications, such as brain-computer interface training or neurofeedback rehabilitation. The localization of the active area of the brain from MEG data results in a highly ill-posed and ill-conditioned inverse problem that requires fast and efficient inversion methods to be solved. In this paper we use an inversion method based on random spatial sampling to solve the MEG inverse problem. The method is fast, efficient and has a low computational load. The numerical tests show that the method can produce accurate map of the electric activity inside the brain even in case of deep neural sources

LSB neural network based segmentation of MR brain images

Least square backpropagation (LSB) algorithm is employed to train a three-layer neural network for segmentation of magnetic resonance (MR) brain images. The simulation results demonstrate the use of LSB algorithm as a promising method for the segmentation of multi-modal medical images. The training time has been dramatically reduced comparing with that of BP network. The influence of the number of neurones in the hidden layer of the network is discussed in the paper

Inhomogeneity correction of magnetic resonance images by minimization of intensity overlapping

Proceeding of: IEEE 2003 International Conference on Image Processing (ICIP), Barcelona, Spain, 14-17 Sept. 2003This work presents a new algorithm (NIC; Non uniform Intensity Correclion) for the correction of intensity inhomogeneities in magnetic resonance images. The algorithm has been validated by means of realistic phantom images and a set of 24 real images. Evaluation using previously proposed phantom images for inhomogeneity correction algorithms allowed us to obtain results fully comparable to the previous literature on the topic. This new algorithm was also compared, using a real image dataset, to other widely used methods which are freely available in the Internet (N3, SPM'99 and SPM2). Standard quality criteria have been used for determining the goodness of the different methods. The new algorithm showed better results removing the intensity inhomogeneities and did not produce degradation when used on images free from this artifact

Filtros suavizadores en imágenes sintéticas de resonancia magnética cerebral: un estudio comparativo

This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during the course of this evaluation are available freely on the Internet and are reported in specialized literature as synthetic images called BrainWeb. The images that belong to this DB were contaminated with Rician noise, this being the most frequent type of noise in real MRI images. Also, the techniques that are usually considered to minimize the impact of Rician noise on the quality of BrainWeb images are matched with the Gaussian filter (GF) and an anisotropic diffusion filter, based on the gradient of the image (GADF). Each of these filters has 2 parameters that control their operation and, therefore, undergo a rigorous tuning process to identify the optimal values that guarantee the best performance of both the GF and the GADF. The peak of the signal-to-noise ratio (PSNR) and the computation time are considered as key elements to analyze the behavior of each of the filtering techniques applied. The results indicate that: a) both filters generate PSNR values comparable to each other. b) The GF requires a significantly shorter computation time to soften the Rician noise present in the considered DB. Keywords: Synthetic Cerebral images, Magnetic resonance, Rician noise, Gaussian filter, Anisotropic diffusion filter, PSNR.Este artículo presenta la evaluación de dos técnicas computacionales para el suavizado de ruido, que puede estar presente en imágenes sintéticas o phantoms numéricos de resonancia magnética (MRI). Las imágenes que servirán como bases de datos (DB) para el desarrollo de la mencionada evaluación están disponibles, de manera libre, en la Internet y se reportan, en la literatura especializada, como imágenes sintéticas denominadas BrainWeb. Las imágenes pertenecientes a esta DB fueron contaminadas con ruido Riciano debido a que este es el tipo de ruido más frecuente en imágenes de MRI reales. Por otra parte, las técnicas consideradas para minimizar el impacto de este ruido, en la calidad de las imágenes de la BrainWeb, se hacen coincidir con el filtro Gausiano (GF) y un filtro de difusión anisotrópica, basado en el gradiente de la imagen (GADF). Cada uno de estos filtros posee 2 parámetros que controlan su funcionamiento y, por ende, deben someterse a un proceso de entonación riguroso para identificar los valores óptimos que garanticen el mejor desempeño tanto del GF como del GADF. El pico de la relación señal a ruido (PSNR) y el tiempo de cómputo son considerados como elementos clave para analizar el comportamiento de cada una de las técnicas de filtrado aplicadas. Los resultados indican que: a) Ambos filtros generan valores de PSNR comparables entre sí. b) El GF requiere de un tiempo de cómputo, significativamente, menor para suavizar el ruido Riciano presente en la DB considerada. Palabras clave: Imágenes sintéticas cerebrales, Resonancia magnética, Ruido Riciano, Filtro Gausiano, Filtro de difusión anisotrópica, PSNR