A Nested Sensor Array Focusing on Near Field Targets

A nested virtual array subband beamforming system is proposed for applications where broadband signal targets are located within the near field of the array. Subband multirate processing and near field beamforming techniques are used jointly for the nested array to improve the performances and reduce the computational complexity. A new noise model, namely the broadband near field spherically isotropic noise model, is also proposed for the optimization design of near field beamformers. It is shown that near field beamforming is essential for better distance discrimination of near field targets, reduced beampattern variations for broadband signals, and stronger reverberation suppression

Robust Near-Field Adaptive Beamforming with Distance Discrimination

This paper proposes a robust near-field adaptive beamformer for microphone array applications in small rooms. Robustness against location errors is crucial for near-field adaptive beamforming due to the difficulty in estimating near-field signal locations especially the radial distances. A near-field regionally constrained adaptive beamformer is proposed to design a set of linear constraints by filtering on a low rank subspace of the near-field signal over a spatial region and frequency band such that the beamformer response over the designed spatial-temporal region can be accurately controlled by a small number of linear constraint vectors. The proposed constraint design method is a systematic approach which guarantees real arithmetic implementation and direct time domain algorithms for broadband beamforming. It improves the robustness against large errors in distance and directions of arrival, and achieves good distance discrimination simultaneously. We show with a nine-element uniform linear array that the proposed near-field adaptive beamformer is robust against distance errors as large as ±32% of the presumed radial distance and angle errors up to ±20⁰. It can suppress a far field interfering signal with the same angle of incidence as a near-field target by more than 20 dB with no loss of the array gain at the near-field target. The significant distance discrimination of the proposed near-field beamformer also helps to improve the dereverberation gain and reduce the desired signal cancellation in reverberant environments

A Microphone Array System for Multimedia Applications with Near-Field Signal Targets

A microphone array beamforming system is proposed for multimedia communication applications using four sets of small planar arrays mounted on a computer monitor. A new virtual array approach is employed such that the original signals received by the array elements are weighted and delayed to synthesize a large, nonuniformly spaced, harmonically nested virtual array covering the frequency band [50, 7000] Hz of the wideband telephony. Subband multirate processing and near-field beamforming techniques are then used jointly by the nested virtual array to improve the performances in reverberant environments. A new beamforming algorithm is also proposed using a broadband near-field spherically isotropic noise model for array optimization. The near-field noise model assumes a large number of broadband random noises uniformly distributed over a sphere with a finite radius in contrast to the conventional far-field isotropic noise model which has an infinite radius. The radius of the noise model, thus, adds a design parameter in addition to its power for tradeoffs between performance and robustness. It is shown that the near-field beamformers designed by the new algorithm can achieve more than 8-dB reverberation suppression while maintaining sufficient robustness against background noises and signal location errors. Computer simulations and real room experiments also show that the proposed array beamforming system reduces beampattern variations for broadband signals, obtains strong noise and reverberation suppression, and improves the sound quality for near-field targets

Semi-autonomous vehicles as a cognitive assistive device for older adults

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors' features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers

Novel Coronavirus Cough Database: NoCoCoDa

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This article introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media intervi

Magnetic resonance imaging and histology correlation in the neocortex in temporal lobe epilepsy.

OBJECTIVE: To investigate the histopathological correlates of quantitative relaxometry and diffusion tensor imaging (DTI) and to determine their efficacy in epileptogenic lesion detection for preoperative evaluation of focal epilepsy. METHODS: We correlated quantitative relaxometry and DTI with histological features of neuronal density and morphology in 55 regions of the temporal lobe neocortex, selected from 13 patients who underwent epilepsy surgery. We made use of a validated nonrigid image registration protocol to obtain accurate correspondences between in vivo magnetic resonance imaging and histology images. RESULTS: We found T1 to be a predictor of neuronal density in the neocortical gray matter (GM) using linear mixed effects models with random effects for subjects. Fractional anisotropy (FA) was a predictor of neuronal density of large-caliber neurons only (pyramidal cells, layers 3 and 5). Comparing multivariate to univariate mixed effects models with nested variables demonstrated that employing T1 and FA together provided a significantly better fit than T1 or FA alone in predicting density of large-caliber neurons. Correlations with clinical variables revealed significant positive correlations between neuronal density and age (rs  = 0.726, pfwe  = 0.021). This study is the first to relate in vivo T1 and FA values to the proportion of neurons in GM. INTERPRETATION: Our results suggest that quantitative T1 mapping and DTI may have a role in preoperative evaluation of focal epilepsy and can be extended to identify GM pathology in a variety of neurological disorders

In vivo MRI signatures of hippocampal subfield pathology in intractable epilepsy.

OBJECTIVES: Our aim is to assess the subfield-specific histopathological correlates of hippocampal volume and intensity changes (T1, T2) as well as diff!usion MRI markers in TLE, and investigate the efficacy of quantitative MRI measures in predicting histopathology in vivo. EXPERIMENTAL DESIGN: We correlated in vivo volumetry, T2 signal, quantitative T1 mapping, as well as diffusion MRI parameters with histological features of hippocampal sclerosis in a subfield-specific manner. We made use of on an advanced co-registration pipeline that provided a seamless integration of preoperative 3 T MRI with postoperative histopathological data, on which metrics of cell loss and gliosis were quantitatively assessed in CA1, CA2/3, and CA4/DG. PRINCIPAL OBSERVATIONS: MRI volumes across all subfields were positively correlated with neuronal density and size. Higher T2 intensity related to increased GFAP fraction in CA1, while quantitative T1 and diffusion MRI parameters showed negative correlations with neuronal density in CA4 and DG. Multiple linear regression analysis revealed that in vivo multiparametric MRI can predict neuronal loss in all the analyzed subfields with up to 90% accuracy. CONCLUSION: Our results, based on an accurate co-registration pipeline and a subfield-specific analysis of MRI and histology, demonstrate the potential of MRI volumetry, diffusion, and quantitative T1 as accurate in vivo biomarkers of hippocampal pathology

Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)

A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation

Investigation of hippocampal substructures in focal temporal lobe epilepsy with and without hippocampal sclerosis at 7T.

PURPOSE: To provide a more detailed investigation of hippocampal subfields using 7T magnetic resonance imaging (MRI) for the identification of hippocampal sclerosis in temporal lobe epilepsy (TLE). MATERIALS AND METHODS: Patients (n = 13) with drug-resistant TLE previously identified by conventional imaging as having hippocampal sclerosis (HS) or not (nine without HS, four HS) and 20 age-matched healthy controls were scanned and compared using a 7T MRI protocol. Using a manual segmentation scheme to delineate hippocampal subfields, subfield-specific volume changes and apparent transverse relaxation rate ( R2*) were studied between the two groups. In addition, qualitative assessment at 7T and clinical outcomes were correlated with measured subfield changes. RESULTS: Volumetry of the hippocampus at 7T in HS patients revealed significant ipsilateral subfield atrophy in CA1 (P = 0.001) and CA4+DG (P \u3c 0.001). Volumetry also uncovered subfield atrophy in 33% of patients without HS, which had not been detected using conventional imaging. R2* was significantly lower in the CA4+DG subfields (P = 0.001) and the whole hippocampus (P = 0.029) of HS patients compared to controls but not significantly lower than the group without HS (P = 0.077, P = 0.109). No correlation was found between quantitative volumetry and qualitative assessment as well as surgical outcomes (Sub, P = 0.495, P = 0.567, P = 0.528; CA1, P = 0.104 ± 0.171, P = 0.273, P = 0.554; CA2+CA3, P = 0.517, P = 0.952, P = 0.130 ± 0.256; CA4+DG, P = 0.052 ± 0.173, P = 0.212, P = 0.124 ± 0.204; WholeHipp, P = 0.187, P = 0.132 ± 0.197, P = 0.628). CONCLUSION: These preliminary findings indicate that hippocampal subfield volumetry assessed at 7T is capable of identifying characteristic patterns of hippocampal atrophy in HS patients; however, difficulty remains in using imaging to identify hippocampal pathologies in cases without HS. LEVEL OF EVIDENCE: 2 J. MAGN. RESON. IMAGING 2017;45:1359-1370