Brain tissue classification in Magnetic Resonance Imaging is useful for a wide range of applications. Within this manuscript a novel approach for brain tissue joint segmentation and classification is presented. Starting from the relaxation time estimation, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to improve the correct classification rate. The effectiveness of the approach is evaluated on a simulated case study.
A new application of Compressive Sensing (CS) in Magnetic Resonance Imaging (MRI) field is presented. In particular, first results of the Intra Voxel Analysis (IVA) technique are reported. The idea is to exploit CS peculiarities in order to distinguish different contributions inside the same resolution cell, instead of reconstructing images from not fully sampled k-space acquisition. Applied to MRI field, this means the possibility of estimating the presence of different tissues inside the same voxel, i.e. in one pixel of the obtained image. In other words, the method is the first attempt, as far as we know, of achieving Spectroscopy-like results starting from each pixel of MR images. In particular, tissues are distinguished each others by evaluating their spin-spin relaxation times. Within this manuscript, first results on clinical dataset, in particular a phantom made by aqueous solution and oil and an occipital brain lesion corresponding to a metastatic breast cancer nodule, are reported. Considering the phantom dataset, in particular focusing on the slice where the separation between water and oil occurs, the methodology is able to distinguish the two components with different spin-spin relaxation times. With respect to clinical dataset,focusing on a voxel of the lesion area, the approach is able to detect the presence of two tissues, namely the healthy and the cancer related ones, while in other location outside the lesion only the healthy tissue is detected. Of course, these are the first results of the proposed methodology, further studies on different types of clinical datasets are required in order to widely validate the approach. Although few datasets have been considered, results seem both interesting and promising.
Image formation in Magnetic Resonance Imaging (MRI) is the procedure which allows the generation of the image starting from data acquired in the so called k-space. At the present, many image formation techniques have been presented, working with different k-space filling strategies. Recently, Compressive Sampling (CS) has been successfully used for image formation from non fully sampled k-space acquisitions, due to its interesting property of reconstructing signal from highly undetermined linear systems. The main advantage consists in greatly reducing the acquisition time. Within this manuscript, a novel application of CS to MRI field is presented, named Intra Voxel Analysis (IVA). The idea is to achieve the so-called super resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. For this aim, multiple Spin Echo images acquired with different Echo Times are required. The output of the algorithm is the estimation of the number of contributions present in the same pixel, i.e. the number of tissues inside the same voxel, and their spin-spin relaxation times. This allows us not only to identify the number of involved tissues, but also to discriminate them. At the present, simulated case studies have been considered, obtaining interesting and promising results. In particular, a study on the required number of images, on the estimation noise and on the regularization parameter of different CS algorithms has been conducted. As future work, the method will be applied to real clinical datasets, in order to validate the estimations.
Magnetic Resonance Imaging is a very powerful techniques for soft tissue diagnosis. At the present, the clinical evaluation is mainly conducted exploiting the amplitude of the recorded MR image which, in some specific cases, is modified by using contrast enhancements. Nevertheless, spin-lattice (T1) and spin-spin (T2) relaxation times can play an important role in many pathology diagnosis, such as cancer, Alzheimer or Parkinson diseases. Different algorithms for relaxation time estimation have been proposed in literature. In particular, the two most adopted approaches are based on Least Squares (LS) and on Maximum Likelihood (ML) techniques. As the amplitude noise is not zero mean, the first one produces a biased estimator, while the ML is unbiased but at the cost of high computational effort. Recently the attention has been focused on the estimation in the complex, instead of the amplitude, domain. The advantage of working with real and imaginary decomposition of the available data is mainly the possibility of achieving higher quality estimations. Moreover, the zero mean complex noise makes the Least Square estimation unbiased, achieving low computational times. First results of complex domain relaxation times estimation on real datasets are presented. In particular, a patient with an occipital lesion has been imaged on a 3.0T scanner. Globally, the evaluation of relaxation times allow us to establish a more precise topography of biologically active foci, also with respect to contrast enhanced images.
Along Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems use more than one SAR antennas (typically
two), mounted on the same platform and displaced along the platform moving direction, to detect slow ground moving
targets. The phase of the ATI signal is related to the target motion parameters and may thus be used to estimate the radial
velocity. In this paper we approach the velocity estimation problem using statistical techniques based on the statistical
distribution of the measured interferometric phases. We analyze the radial velocity estimation with respect to ATI system
parameters, such as velocity values, the signal to clutter ratio (SCR), the clutter to noise ratio (CNR), considering a
deterministic target whose velocity is estimated using a Gaussian model. This model allows to take into account the lack
of knowledge of the target radar cross section (RCS) values and provides an analytical form for the interferometric phase
probability density function. Simulations results show that the adoption of Maximum Likelihood (ML) techniques, to
perform a joint estimation of velocity and SCR, and multi-channel configurations, to overcome ambiguities problems,
provide very good velocity estimation accuracy.
In this paper, we propose an improvement of the Chirped-Pulsed Frequency Modulation (C-PFM) FBGs reading
technique  as supported by new experimental results. The C-PFM technique, which was basically translated from its
counterpart in the field of radar signal analysis, exploits the intensity modulation of the probe signal (the light traveling
along the fiber in our case) by means of a sinusoid with a linearly variable frequency and a train of pulses, to improve the
spatial resolution of the acquisition system. The response discrimination of the FBG sensors is achieved thanks to an infiber
linear filter and a novel adaptive numerical filtering as it will be better explained in the following. Using a peculiar
time window shaping (Blackman) of the light pulse we intended to improve the cross-talk features of the reading
In this paper we consider the detection of a moving point target using two SAR antennas mounted on the same aircraft and spatially separated in the along track (cross range) direction. We consider two cases: conventionally quantized raw data and signum coded (SC or one bit coded) raw data. The performance of the presented along track interferometric system is evaluated by comparing the probability of false alarm and the probability of detection obtained in the two cases. Numerical results show that is possible to achieve comparable performances for both coding techniques.
Purpose of the present paper is to investigate the possibility to reconstruct strongly discontinuous terrain height profiles, starting from more than one wrapped interferometric phase signals obtained at different working frequencies. In absence of phase noise, the use of two frequencies that are in inational ratio allows to have a unique solution. The presence of the decorrelation noise, however, makes such methods not applicable in practical cases. We propose an unwrapping method based on a Maximum Likelihood estimation technique and using frequency diversity. Since it does not exploit the phase gradient, it assures the uniqueness of the solution, also in the case of piece-wise continuous elevation patterns with strong discontinuities. This result is derived from the consideration that the likelihood function obtained combining frequency diversity information exhibits a unique global maximum.
KEYWORDS: Signal to noise ratio, Synthetic aperture radar, Fourier transforms, Computer simulations, Data processing, Signal processing, Integrated circuits, Convolution, Digital electronics, Device simulation
Processing of Synthetic Aperture Radar (SAR) data can be implemented either in space domain or in transformed (spatial) frequency domain. The latter is the usual technique, due to availability of Fast Fourier Transform codes. However, for one bit coded signals direct space domain processing may be a very convenient alternative. It is noted that one bit coding does not impair amplitude and phase information, it the signal to noise ratio is small; and that processing of one bit coded sequences can be conveniently implemented by simple digital integrated circuits. A processor for one-bit coded SAR data has been designed for real-time operations: simulation and expected performance of this processor is presented.