Giovanni Sebastiani

Mathematical principles of basic magnetic resonance imaging in medicine

Abstract In this paper we review, from a mathematical point of view, the main techniques of magnetic resonance imaging (MRI) used in medicine. A detailed mathematical description of the basic physics and of the image formation process is given in the framework of the classical theory.

Bayesian estimation of relaxation times T1 in MR images of irradiated Fricke-agarose gels

The authors present a novel method for processing T(1)-weighted images acquired with Inversion-Recovery (IR) sequence. The method, developed within the Bayesian framework, takes into account a priori knowledge about the spatial regularity of the parameters to be estimated. Inference is drawn by means of Markov Chains Monte Carlo algorithms. The method has been applied to the processing of IR images from irradiated Fricke-agarose gels, proposed in the past as relative dosimeter to verify radiotherapeutic treatment planning systems. Comparison with results obtained from a standard approach shows that signal-to noise ratio (SNR) is strongly enhanced when the estimation of the longitudinal relaxation rate (R1) is performed with the newly proposed statistical approach. Furthermore, the method allows the use of more complex models of the signal. Finally, an appreciable reduction of total acquisition time can be obtained due to the possibility of using a reduced number of images. The method can also be applied to T(1) mapping of other systems.

Over-relaxation methods and coupled Markov chains for Monte Carlo simulation

This paper is concerned with improving the performance of certain Markov chain algorithms for Monte Carlo simulation. We propose a new algorithm for simulating from multivariate Gaussian densities. This algorithm combines ideas from coupled Markov chain methods and from an existing algorithm based only on over-relaxation. The rate of convergence of the proposed and existing algorithms can be measured in terms of the square of the spectral radius of certain matrices. We present examples in which the proposed algorithm converges faster than the existing algorithm and the Gibbs sampler. We also derive an expression for the asymptotic variance of any linear combination of the variables simulated by the proposed algorithm. We outline how the proposed algorithm can be extended to non-Gaussian densities.

General over-relaxation Markov chain Monte Carlo algorithms for Gaussian densities ☆

We study general over-relaxation Markov chain Monte Carlo samplers for multivariate Gaussian densities. We provide conditions for convergence based on the spectral radius of the transition matrix and on detailed balance. We illustrate these algorithms using an image analysis example.

A Bayesian approach for the median filter in image processing

Abstract In this paper we propose the use of the median filter (MF) within the Bayesian framework. This allows us to develop global methods for both image smoothing and image approximation by the MF ‘roots’. A new method for solving the approximation problem is proposed, which is based on stochastic optimization with constraints. Results of the proposed method for both simulated and real binary images are illustrated and compared to results from a known deterministic method.

Solving an Inverse Diffusion Problem for Magnetic Resonance Dosimetry by a Fast Regularization Method

An inverse diffusion problem that appears in Magnetic Resonance dosimetry is studied. The problem is shown to be equivalent to a deconvolution problem with a known kernel. To cope with the singularity of the kernel, nonlinear regularization functionals are considered which can provide regular solutions, reproduce steep gradients and impose positivity constraints. A fast deterministic algorithm for solving the involved non-convex minimization problem is used. Accurate restorations on real 256×256 images are obtained by the algorithm in a few minutes on a 266-MHz PC that allow to precisely quantitate the relative absorbed dose.