An automated magnetoencephalographic data cleaning algorithm.

The problem of cleaning magnetoencephalographic data is addressed in this manuscript. At present, several denoising procedures have been proposed in the literature, nevertheless their adoption is limited due to the difficulty in implementing and properly tuning the algorithms. Therefore, as of today, the gold standard remains manual cleaning.

Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study.

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks.

Denoising of MR images using Kolmogorov-Smirnov distance in a Non Local framework.

Data coming from any acquisition system, such as Magnetic Resonance Imaging ones, are affected by noise. Although modern high field scanners can reach high Signal to Noise Ratios, in some circumstances, for example in case of very weak signals due to a specific acquisition sequence, noise becomes a critical issue that has to be properly handled. In the last years methods based on the so called Non Local Mean have proven to be very effective in denoising tasks.

KSR-NLM: a non local means despeckling filter for ultrasound images based on ratio patch and KS distance.

Speckle noise greatly degrades the quality of ultrasound images. Being signal dependent, it requires the design of specific filters in order to be reduce. Within this manuscript, $a$ novel approach for despeckling ultrasound images is proposed.

Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. 2018, , 1613.

The authors wish to make a correction to their paper [1]. The following Table 1 should be replaced with the table shown below it[..

Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review.

In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it.

Effects of Physical Exercise on Cognitive Functioning and Wellbeing: Biological and Psychological Benefits.

Much evidence shows that physical exercise (PE) is a strong gene modulator that induces structural and functional changes in the brain, determining enormous benefit on both cognitive functioning and wellbeing. PE is also a protective factor for neurodegeneration. However, it is unclear if such protection is granted through modifications to the biological mechanisms underlying neurodegeneration or through better compensation against attacks.

Enhanced Wiener filter for ultrasound image restoration.

Background And Objective: Speckle phenomenon strongly affects UltraSound (US) images. In the last years, several efforts have been done in order to provide an effective denoising methodology. Although good results have been achieved in terms of noise reduction effectiveness, most of the proposed approaches are not characterized by low computational burden and require the supervision of an external operator for tuning the input parameters.

A 3D MRI denoising algorithm based on Bayesian theory.

Background: Within this manuscript a noise filtering technique for magnetic resonance image stack is presented. Magnetic resonance images are usually affected by artifacts and noise due to several reasons. Several denoising approaches have been proposed in literature, with different trade-off between computational complexity, regularization and noise reduction.

Bayesian MRI denoising in complex domain.

In recent years, several efforts have been done for producing Magnetic Resonance Image scanner with higher magnetic field strength mainly for increasing the Signal to Noise Ratio and the Contrast to Noise Ratio of the acquired images. However, denoising methodologies still play an important role for achieving images neatness. Several denoising algorithms have been presented in literature.

Intra voxel analysis in magnetic resonance imaging.

A technique for analyzing the composition of each voxel, in the magnetic resonance imaging (MRI) framework, is presented. By combining different acquisitions, a novel methodology, called intra voxel analysis (IVA), for the detection of multiple tissues and the estimation of their spin-spin relaxation times is proposed. The methodology exploits the sparse Bayesian learning (SBL) approach in order to solve a highly underdetermined problem imposing the solution sparsity.

A New Methodology for 3D Target Detection in Automotive Radar Applications.

Today there is a growing interest in automotive sensor monitoring systems. One of the main challenges is to make them an effective and valuable aid in dangerous situations, improving transportation safety. The main limitation of visual aid systems is that they do not produce accurate results in critical visibility conditions, such as in presence of rain, fog or smoke.

A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times.

Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented.

Speckle reduction based on Wiener filter in ultrasound images.

In this manuscript, a technique for speckle noise reduction in ultrasound images is presented. The method exploits Wiener filter and is able to take into account spatial correlation among noise samples. With respect to classical Wiener filter approach developed in independence hypothesis, the methodology is able to sensibly improve filtering performances, at the cost of no computational time increase.

A DBSCAN based approach for jointly segment and classify brain MR images.

In recent years a growing interest has grown in Magnetic Resonance images segmentation techniques, due to their usefulness in many applications. Within this manuscript, a novel segmentation approach is presented, based on two main innovations. First, it exploits the estimated proton density and relaxation times for each pixel, instead of its gray-level intensity.

A Bayesian approach for relaxation times estimation in MRI.

Relaxation time estimation in MRI field can be helpful in clinical diagnosis. In particular, T1 and T2 changes can be related to tissues modification, being an effective tool for detecting the presence of several pathologies and measure their development, thus their estimation is a useful research field. Currently, most techniques work pixel-wise, and transfer the noise reduction task to post processing filters.

Optimal configuration for relaxation times estimation in complex spin echo imaging.

Many pathologies can be identified by evaluating differences raised in the physical parameters of involved tissues. In a Magnetic Resonance Imaging (MRI) framework, spin-lattice T1 and spin-spin T2 relaxation time parameters play a major role in such an identification. In this manuscript, a theoretical study related to the evaluation of the achievable performances in the estimation of relaxation times in MRI is proposed.

Relaxation time estimation from complex magnetic resonance images.

Magnetic Resonance (MR) imaging techniques are used to measure biophysical properties of tissues. As clinical diagnoses are mainly based on the evaluation of contrast in MR images, relaxation times assume a fundamental role providing a major source of contrast. Moreover, they can give useful information in cancer diagnostic.

Field map reconstruction in magnetic resonance imaging using Bayesian estimation.

Field inhomogeneities in Magnetic Resonance Imaging (MRI) can cause blur or image distortion as they produce off-resonance frequency at each voxel. These effects can be corrected if an accurate field map is available. Field maps can be estimated starting from the phase of multiple complex MRI data sets.