Which Are the Main Surface Disinfection Approaches at the Time of SARS-CoV-2?

Among many guidelines issued by the World Health Organization to prevent contagion from novel coronavirus (SARS-CoV-2), disinfection of animate and inanimate surfaces has emerged as a key issue. One effective approach to prevent its propagation can be achieved by disinfecting air, skin, or surfaces. A thorough and rational application of an Environmental Protection Agent for disinfection of surfaces, as well as a good personal hygiene, including cleaning hands with appropriate products (e.g., 60–90% alcohol-based product) should minimize transmission of viral respiratory pathogens such as SARS-CoV- 2. Critical issues, associated with the potential health hazard of chemical disinfectants and the ineffective duration of most of the treatments, have fostered the introduction of innovative and alternative disinfection approaches. The present review aims to provide an outline of methods currently used for inanimate surface disinfection with a look to the future and a focus on the development of innovative and effective disinfection approaches (e.g., metal nanoparticles, photocatalysis, self-cleaning, and self-disinfection) with particular focus on SARS-CoV-2. The research reviews are, usually, focused on a specific category of disinfection methods, and therefore they are limited. On the contrary, a panoramic review with a wider focus, as the one here proposed, can be an added value for operators in the sector and generally for the scientific community

Which Are the Main Surface Disinfection Approaches at the Time of SARS-CoV-2?

Among many guidelines issued by the World Health Organization to prevent contagion from novel coronavirus (SARS-CoV-2), disinfection of animate and inanimate surfaces has emerged as a key issue. One effective approach to prevent its propagation can be achieved by disinfecting air, skin, or surfaces. A thorough and rational application of an Environmental Protection Agent for disinfection of surfaces, as well as a good personal hygiene, including cleaning hands with appropriate products (e.g., 60\u201390% alcohol-based product) should minimize transmission of viral respiratory pathogens such as SARS-CoV-2. Critical issues, associated with the potential health hazard of chemical disinfectants and the ineffective duration of most of the treatments, have fostered the introduction of innovative and alternative disinfection approaches. The present review aims to provide an outline of methods currently used for inanimate surface disinfection with a look to the future and a focus on the development of innovative and effective disinfection approaches (e.g., metal nanoparticles, photocatalysis, self-cleaning, and self-disinfection) with particular focus on SARS-CoV-2. The research reviews are, usually, focused on a specific category of disinfection methods, and therefore they are limited. On the contrary, a panoramic review with a wider focus, as the one here proposed, can be an added value for operators in the sector and generally for the scientific community

Effect of Phase Correction on DTI and q-space Metrics

International audienceThe non-Gaussian noise distribution, e.g. Rician, in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, diffusion metrics computed on the estimated signal can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset 3 and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging (DTI) and on Mean Apparent Propagator Magnetic Resonance Imaging (MAP-MRI), i.e., q-space metrics. We show that phase correction is an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values

Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-space Metrics

International audienceThe non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values

Unveiling the Dispersion Kernel in DSC-MRI by Means of Dispersion-Compliant Bases and Control Point Interpolation Techniques

International audienceIn DSC-MRI the presence of dispersion affects the estimation, via deconvolution, of the residue function that characterizes the perfusion in each voxel. Dispersion is descibed by a Vascular Transport Function (VTF) which knolewdge is essential to recover a dispersion-free residue function. State-of-the-art techniques aim at characterizing the VTF but assume a specific shape for it, which in reality is unknown. We propose to estimate the residue function without assumptions by means of Dispersion-Compliant Bases (DCB). We use these results to find which VTF model better describes the in vivo data for each tissue type by means of control point interpolation approaches

Elucidating Dispersion Effects in Perfusion MRI by Means of Dispersion-Compliant Bases

International audienceDispersion effects in perfusion MRI data have a relevant influence on the residue function computed from deconvolution of the measured arterial and tissular concentration time-curves. Their characterization allows reliable estimation of hemody-namic parameters and can reveal pathological tissue conditions. However, the time-delay between the measured concentration time-curves is a confounding factor. We perform deconvolution by means of dispersion-compliant bases, separating the effects of dispersion and delay. In order to characterize dispersion, we introduce shape parameters, such as the dispersion time and index. We propose a new formulation for the dispersed residue function and perform in silico experiments that validate the reliability of our approach against the block-circulant Singular Value Decomposition. We successfully apply the approach to stroke MRI data and show that the calculated parameters are coherent with physiological considerations, highlighting the importance of dispersion as an effect to be measured rather than discarded

Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation

The micro-architecture of brain tissue obstructs the movement of diffusing water molecules, causing tissue-dependent, often anisotropic diffusion profiles. In diffusion MRI (dMRI), the relation between brain tissue structure and diffusion anisotropy is studied using oriented diffusion gradients, resulting in tissue-and orientation-dependent diffusion-weighted images (DWIs). Over time, various methods have been proposed that summarize these DWIs, that can be measured at different orientations, gradient strengths and diffusion times into one " diffusion anisotropy " measure. This book chapter is dedicated to understanding the similarities and differences between the diffusion anisotropy metrics that different methods estimate. We first discuss the physical interpretation of diffusion anisotropy in terms of the diffusion properties around nervous tissue. We then explain how DWIs are influenced by diffusion anisotropy and the parameters of the dMRI acquisition itself. We then go through the state-of-the-art of signal-based and multi-compartment-based dMRI methods that estimate diffusion anisotropy-related methods, focusing on their limitations and applications. We finally discuss confounding factors in the estimation of diffusion anisotropy and current challenges

Improved Vascular Transport Function Characterization in DSC-MRI via Deconvolution with Dispersion-Compliant Bases

International audienceBolus dispersion affects the residue function computed via deconvolution of DSC-MRI data. The obtained effective residue function can be expressed as the convolution of the true one with a Vascular Transport Function (VTF) that characterizes dispersion. The state-of-the-art technique CPI+VTF allows to estimate the actual residue function by assuming a model of VTF. We propose to perform deconvolution representing the effective residue function with Dispersion-Compliant Bases (DCB) with no assumptions on the VTF, and then apply the CPI+VTF on DCB results, to improve performance