Single Echo Research Articles

Diffusion tensor imaging in evaluation of intradural spinal tumors – A case series

Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging (MRI) technique which utilizes diffusion measurements in multiple directions to provide information regarding tissue structure by parameters such as fractional anisotropy (FA) and apparent diffusion coefficient along with generation of fiber tractography maps. We investigated DTI using single shot echo planar imaging in 20 diffusion directions on 3T MRI in nine patients diagnosed with intradural spinal tumors and found significant reduWction in FA value within the lesion compared to FA value at normal cord. Tractography maps generated by DTI were useful in differentiating intramedullary from extramedullary location of tumor. Tractography also provided useful information regarding tract infiltration or displacement in cases of intramedullary neoplasms. Thus, DTI proved helpful in classification, characterization, and treatment planning of intradural spinal tumors.

A novel framework for in-vivo diffusion tensor distribution MRI of the human brain

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe subvoxel heterogeneity by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring multiple diffusion encoding (MDE) images and estimating DTD from them in the human brain in vivo. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined diffusion encoding parameters we show that iPFG retains salient features of a traditional multiple-PFG (mPFG/MDE) sequence while reducing the echo time and coherence pathway artifacts thereby extending its applications beyond DTD MRI. Our DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape, and orientation distributions to best fit the measured MDE images. From these tensors we obtain the spectrum of diffusion tensor ellipsoid sizes and shapes, and the microscopic orientation distribution function (μODF) and microscopic fractional anisotropy (μFA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results revealed microscopic anisotropy in various gray and white matter regions and skewed MD distributions in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and elucidated the source of diffusion heterogeneity which may help improve the diagnosis of various neurological diseases and disorders.

Speech recognition in echoic environments and the effect of aging and hearing impairment

Temporal modulations provide critical cues for speech recognition. When the temporal modulations are distorted by, e.g., reverberations, speech intelligibility drops, and the drop in speech intelligibility can be explained by the amount of distortions to the speech modulation spectrum, i.e., the spectrum of temporal modulations. Here, we test a condition in which speech is contaminated by a single echo. Speech is delayed by either 0.125 s or 0.25 s to create an echo, and these two conditions notch out the temporal modulations at 2 or 4 Hz, respectively. We evaluate how well young and older listeners can recognize such echoic speech. For young listeners, the speech recognition rate is not influenced by the echo, even when they are exposed to the first echoic sentence. For older listeners, the speech recognition rate drops to less than 60% when listening to the first echoic sentence, but rapidly recovers to above 75% with exposure to a few sentences. Further analyses reveal that both age and the hearing threshold influence the recognition of echoic speech for the older listeners. These results show that the recognition of echoic speech cannot be fully explained by distortions to the modulation spectrum, and suggest that the auditory system has mechanisms to effectively compensate the influence of single echoes.

Airborne Streak Tube Imaging LiDAR Processing System: A Single Echo Fast Target Extraction Implementation

In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.

Rapid high-fidelity mapping using single-shot overlapping-echo acquisition and deep learning reconstruction.

To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid mapping. In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver maps less than 90 ms per slice. The self-registered B0 information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments. The quantitative results of GRE-MOLED mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient=0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNRand BOLD sensitivity in task-based FMRI. GRE-MOLED is a new real-time technique for quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

Angular Super-Resolution of Multi-Channel APAR in Interference Environments

Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is studied for active phased array radar (APAR) in this paper. Since the super-resolution ability of the conventional MUSIC algorithm and iterative adaptive algorithm (IAA) algorithm are limited in single snap echo, the iterative re-weighted least squares (IRLS) algorithm under p-norm constraint is proposed. Further, the near main lobe interference suppression ability is enhanced by the adaptive diagonal loading method. The performance differences of IAA and IRLS algorithms for single-target detection and double-target angular super-resolution are analyzed in detail by numerical simulation on three scenes of no interference, side-lobe interference, and near-main-lobe interference. The simulation results show that the proposed algorithm can effectively solve the problem of target angle estimation and super-resolution based on single sample echo in an interference environment, including near main-lobe interference.

Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture.

Brain tissue microarchitecture is characterized by heterogeneous degrees of diffusivity and rates of transverse relaxation. Unlike standard diffusion MRI with a single echo time (TE), which provides information primarily on diffusivity, relaxation-diffusion MRI involves multiple TEs and multiple diffusion-weighting strengths for probing tissue-specific coupling between relaxation and diffusivity. Here, we introduce a relaxation-diffusion model that characterizes tissue apparent relaxation coefficients for a spectrum of diffusion length scales and at the same time factors out the effects of intra-voxel orientation heterogeneity. We examined the model with an in vivo dataset, acquired using a clinical scanner, involving different health conditions. Experimental results indicate that our model caters to heterogeneous tissue microstructure and can distinguish fiber bundles with similar diffusivities but different relaxation rates. Code with sample data is available at https://github.com/dryewu/RDSI.

Pseudo multishot echo-planar imaging for geometric distortion improvement.

Conventional echo-planar imaging (EPI) uses a radiofrequency pulse for excitation and a prolonged echo train to sample k space, while off-resonance and T2 * decay effects caused by magnetic susceptibility variation accumulate within each echo, leading to geometric distortion. Multishot EPI methods, which divide k space into segments, can shorten the effective echo spacing and reduce the distortion on EPI images. But multiple shots cost longer scan time and render susceptibility to motion. In this study, we propose a new "multishot" EPI method termed pseudo multishot EPI (pmsEPI), in which phase-encoding lines are segmented as in multishot EPI but are collected within a single shot. With the magnetization divided into different pathways via interleaved excitation instead of refocusing in a single long echo train, the total phase error accumulation is reduced in each segmented acquisition, thereby improving distortion of the resultant EPI image. The performance of the pmsEPI method is demonstrated by phantom and in vivo brain experiments on a 3-T scanner. The experimental results show that the distortion displacements of pmsEPI acquisition compared with conventional EPI decrease by 50% with two pseudo shots and 66% with three pseudo shots, validating the ability of the method to obtain images with reduced distortion in a single shot, although magnetization splitting may induce more than 40% SNR loss and minor artifacts. Specifically, the ability of pmsEPI in diffusion-weighted imaging with different trajectory options is highlighted, and the flexibility is demonstrated in a single-shot blip up and down acquisition.

Rapid estimation of 2D relative -maps from localizers inthe human heart at 7T using deep learning.

Subject-tailored parallel transmission pulses for ultra-high fields bodyapplications are typically calculated based on subject-specific -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D -maps from a single gradient echo localizer to overcome long calibration times. 126 channel-wise, complex, relative 2D -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only -shimming was subsequently performed on the estimated -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. The deep learning-based -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted -maps comparable to the acquired ground truth and anatomical scans reflect the resulting -pattern using the deep learning-based maps. The feasibility of estimating 2D relative -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.

Intravoxel Incoherent Motion MRI and PET in Locally Advanced Cervical Carcinoma Undergoing Concurrent Chemoradiation Therapy

<h3>Purpose/Objective(s)</h3> Dynamic contrast enhanced (DCE) CT or MRI and FDG PET have been used to evaluate the impact of tumor perfusion and metabolism on the course and effectiveness of concurrent chemoradiation therapy (CCRT) for cervical cancer. Combining perfusion and metabolic measures allows better identification of radioresistant sub-volumes: however, use of contrast and additional dose are barriers to frequent dual perfusion-metabolic imaging. Here, we investigated a contrast-free, non-ionizing alternative to DCE: intravoxel incoherent motion (IVIM) MRI which provides both perfusion and diffusion measurements. Our objective was to evaluate changes of PET and IVIM parameters in cervical cancer patients before and during CCRT and to correlate these parameters and their changes with tumor volume response during CCRT. <h3>Materials/Methods</h3> Pre- and on-treatment IVIM scans were performed on 15 cervical cancer patients undergoing definitive chemoradiation (median [range] age: 66 [29 to 85]; FIGO: 1 IB1, 1 IIA, 3 IIB, 1 IIIB, 1 IIIC, 5 IIIC1, 2 IIIC2, 1 IVA; hist: 11 squamous cell carcinoma, 3 endocervical adenocarcinoma, 1 large cell neuroendocrine carcinoma), of whom 14 also underwent pre-treatment PET scans. The on-treatment MRI occurred generally during the last week or shortly after completing EBRT. Diffusion weighted images (DWIs) were acquired at 3T scanners using a free-breathing single shot spin echo planar imaging (EPI) sequence. IVIM MRI maps were generated by Bayesian fitting of a two-compartment IVIM model to the DWIs. Three-dimensional co-registered maps of PET standardized uptake value (SUV) and IVIM diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) were generated. Summary statistics (mean +/- standard deviation) for these and other imaging parameters were calculated pre- and on-treatment. Univariate analysis was performed to determine relationships between relative change in gross tumor volume (ΔGTV) and pre-treatment imaging measurements. <h3>Results</h3> In tumors, pre- versus on-treatment values significantly changed for the following parameters: ΔGTV (66.8 +/- 43.2 vs. 32.0+/-23.0 ml, p<.001) and D (0.00109 +/- 0.00023 vs. 0.00120 +/- 0.00026 mm²/s, p=.02). The perfusion fraction f increased and D* decreased, but not significantly (f: 0.127 +/- 0.022 vs. 0.154 +/- 0.057 %, p=.07, D*: 0.0137 +/- 0.0035 vs. 0.0133 +/- 0.0039 mm²/s, p=.6). Pre-treatment SUV_max (r=.76, p=.002) and SUV_mean (r=.70, p=.007) were positively related with ΔGTV, while pre-treatment f, D*, and D were negatively but not significantly related with ΔGTV (r=-.44, p=.1; D*: r=-.14, p=.6; D: r=-.12, p=.7). <h3>Conclusion</h3> Both perfusion fraction and diffusion coefficient IVIM increased in locally advanced cervical carcinoma patients undergoing CCRT – reflecting changes in the tumor microenvironment during treatment. These weak relationships observed here suggest a potential complementary role and motivating the combined use of these modalities for tumor characterization.

Evaluation of a crowd sourced bathymetric approach

Crowd-Sourced Bathymetry is the process of generating a harbor chart through collecting, enriching, processing, and aggregating depth (and other) measurements from a host of vessels, using standard navigation instruments, while engaged in routine maritime operations. This study explores the accuracy and limitations of one particular approach towards utilizing Crowd-Sourced data, comparing the results obtained from DockTech’s approach to that of a classic MBES survey. As DockTech is a for-profit startup that sells their product to various stakeholders in the maritime supply chain, a balance between protection of proprietary interest and scientific collaboration is necessary. As such, while specific details in regards to both chosen methodologies and their particular evaluations cannot be discussed, a general overview will be provided and the results of DockTech’s solution will be evaluated. This study is based on depth measurements collected by service vessels, active on a daily basis in Ashdod Port over the course of two months. Tug and Pilot boats use stand-alone GNSS navigation and navigation class Single Beam echo sounders for safety purposes when traversing the port and maneuvering between the wharfs. These depths, averaged over a relatively rough grid, were used to produce a chart of part of the port. Data from the service vessels did not include RTK GNSS navigation and hence ellipsoid referenced surveying techniques were not used in lieu of water level monitoring. This chart was then compared to another chart produced from a professional hydrographic survey of the port, using state of the art equipment and strict hydrographic control. The differences were then analyzed according to the latest edition of the IHO S-44 standards. Recommendations were suggested for some relatively simple measures which could enhance the accuracy achieved and the reliability of such a chart, at least for harbor maintenance purposes.

Evaluating the efficacy of multi-echo ICA denoising on model-based fMRI

fMRI is an indispensable tool for neuroscience investigation, but this technique is limited by multiple sources of physiological and measurement noise. These noise sources are particularly problematic for analysis techniques that require high signal-to-noise ratio for stable model fitting, such as voxel-wise modeling. Multi-echo data acquisition in combination with echo-time dependent ICA denoising (ME-ICA) represents one promising strategy to mitigate physiological and hardware-related noise sources as well as motion-related artifacts. However, most studies employing ME-ICA to date are resting-state fMRI studies, and therefore we have a limited understanding of the impact of ME-ICA on complex task or model-based fMRI paradigms. Here, we addressed this knowledge gap by comparing data quality and model fitting performance of data acquired during a visual population receptive field (pRF) mapping (N = 13 participants) experiment after applying one of three preprocessing procedures: ME-ICA, optimally combined multi-echo data without ICA-denoising, and typical single echo processing. As expected, multi-echo fMRI improved temporal signal-to-noise compared to single echo fMRI, with ME-ICA amplifying the improvement compared to optimal combination alone. However, unexpectedly, this boost in temporal signal-to-noise did not directly translate to improved model fitting performance: compared to single echo acquisition, model fitting was only improved after ICA-denoising. Specifically, compared to single echo acquisition, ME-ICA resulted in improved variance explained by our pRF model throughout the visual system, including anterior regions of the temporal and parietal lobes where SNR is typically low, while optimal combination without ICA did not. ME-ICA also improved reliability of parameter estimates compared to single echo and optimally combined multi-echo data without ICA-denoising. Collectively, these results suggest that ME-ICA is effective for denoising task-based fMRI data for modeling analyzes and maintains the integrity of the original data. Therefore, ME-ICA may be beneficial for complex fMRI experiments, including voxel-wise modeling and naturalistic paradigms.

Diagnostic value of water-fat-separated images and CT-like susceptibility-weighted images extracted from a single ultrashort echo time sequence for the evaluation of vertebral fractures and degenerative changes of the spine

ObjectivesTo evaluate the performance of single-echo Dixon water-fat imaging and computed tomography (CT)–like imaging based on a single ultrashort echo time (sUTE) MR sequence for imaging of vertebral fractures as well as degenerative bone changes of the spine in comparison to conventional CT and MR sequences.MethodsThirty patients with suspected acute vertebral fractures were examined using a 3-T MRI, including an sUTE sequence as well as short-tau inversion recovery (STIR) and T1-weighted sequences. During postprocessing, water-fat separation was performed by solving the smoothness-constrained inverse water-fat problem based on a single-complex UTE image. By removing the unwanted low-frequency phase terms, additional MR-based susceptibility-weighted-like (SW-like) images with CT-like contrast were created. Two radiologists evaluated semi-quantitative and quantitative features of fractures and degenerative changes independently and separately on CT and MR images.ResultsIn total, all 58 fractures were accurately detected of whom 24 were correctly classified as acute fractures with an edema detected on the water-fat-separated UTE images, using STIR and T1w sequences as standard of reference. For the morphological assessment of fractures and degenerative changes, the overall agreement between SW-like images and CT was substantial to excellent (e.g., Genant: κ 0.90 (95% confidence interval 0.54–1.00); AO/Magerl: κ 0.75 (95% confidence interval 0.43–1.00)). Overall inter-reader agreement for water-fat-separated UTE images and SW-like images was substantial to almost perfect.ConclusionDetection and assessment of vertebral fractures and degenerative bone changes of the spine were feasible and accurate using water-fat-separated images as well as SW-like images, both derived from the same sUTE-Dixon sequence.Key Points• The detection of acute vertebral fractures was feasible using water-fat-separated images and CT-like images reconstructed from one sUTE sequence.• Assessment of the vertebral fractures using SW-like images with CT-like contrast was found to be comparable to conventional CT.• sUTE imaging of the spine can help reduce examination times and radiation exposure.

Visualization of the Anal Dimple by Fetal MRI: Is It Feasible?

Introduction: The aim of this study was to determine the feasibility of fetal MRI in identifying the normal anal dimple (AD) and compare it with prenatal ultrasound (US). Methods: Retrospective review of 130 patients with both fetal MRI and US. The gestational age (GA) was stratified into four groups: (1) 16 to 21 weeks-6 days; (2) 22 to 27 weeks-6 days; (3) 28 to 33 weeks-6 days; and (4) 34 weeks and beyond. Steady-state free precession (SSFP) and single shot fast spin echo (SSFSE) axial T2 MRI and transverse US images of the fetal perineum were analyzed, and visualization of the AD was determined. Clinical indication, gender, single versus multiple gestation, best MRI sequence where it was seen, and postnatal AD information were recorded. Results: The AD was visualized in 125/130 fetal MRIs, and visualization was independent of GA (p 0.230). US visualized the AD in 67/130 cases, and the best GA for visualization was in group 3 (p < 0.001). There was no difference in AD visualization between SSFSE and SSFP sequences (p 0.167). Conclusion: Prenatal visualization of the AD by MRI is feasible and superior to US, independent of GA. Adding AD visualization to routine screening prenatal US and MRI may increase recognition of anorectal malformation.

Interobserver Variability of Gross Tumor Volume Delineation for Colorectal Liver Metastases Using Computed Tomography and Magnetic Resonance Imaging

PurposeThe purpose of this study was to evaluate the interobserver variability in the contouring of the gross tumor volume (GTV) on magnetic resonance (MR) imaging and computed tomography (CT) for colorectal liver metastases in the setting of SABR. Methods and MaterialsThree expert radiation oncologists contoured 10 GTV volumes on 3 MR imaging sequences and on the CT image data set. Three metrics were chosen to evaluate the interobserver variability: the conformity index, the DICE coefficient, and the maximum Hausdorff distance (HDmax). Statistical analysis of the results was performed using a 1-sided permutation test. ResultsFor all 3 metrics, the MR liver acquisition volume acquisition (MR LAVA) showed the lowest interobserver variability. Analysis showed a significant difference (P < .01) in the mean DICE, an overlap metric, for MR LAVA (0.82) and CT (0.74). The HDmax that highlights boundary errors also showed a significant difference (P = .04) with MR LAVA having a lower mean HDmax (7.2 mm) compared with CT (5.7 mm). The mean HDmax for both MR single shot fast spin echo (SSFSE) (19.3 mm) and diffusion weighted image (9.5 mm) showed large interobserver variability with MR SSFSE having a mean HDmax of 19.3 mm. A volume comparison between MR LAVA and CT showed a significantly higher volume for small GTVs (<5 cm3) when using MR LAVA for contouring in comparison to CT. ConclusionsThis study reported the lowest interobserver variability for the MR LAVA, thus indicating the benefit of using MR to complement CT when contouring GTV for colorectal liver metastases.

A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar

It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering.

Iterative static field map estimation for off-resonance correction in non-Cartesian susceptibility weighted imaging.

PurposePatient‐induced inhomogeneities in the magnetic field cause distortions and blurring during acquisitions with long readouts such as in susceptibility‐weighted imaging (SWI). Most correction methods require collecting an additional ΔB0 field map to remove these artifacts.TheoryThe static ΔB0 field map can be approximated with an acceptable error directly from a single echo acquisition in SWI. The main component of the observed phase is linearly related to ΔB0 and the echo time (TE), and the relative impact of non‐ ΔB0 terms becomes insignificant with TE >20 ms at 3 T for a well‐tuned system.MethodsThe main step is to combine and unfold the multi‐channel phase maps wrapped many times, and several competing algorithms are compared for this purpose. Four in vivo brain data sets collected using the recently proposed 3D spreading projection algorithm for rapid k‐space sampling (SPARKLING) readouts are used to assess the proposed method.ResultsThe estimated 3D field maps generated with a 0.6 mm isotropic spatial resolution provide overall similar off‐resonance corrections compared to reference corrections based on an external ΔB0 acquisitions, and even improved for 2 of 4 individuals. Although a small estimation error is expected, no aftermath was observed in the proposed corrections, whereas degradations were observed in the references.ConclusionA static ΔB0 field map estimation method was proposed to take advantage of acquisitions with long echo times, and outperformed the reference technique based on an external field map. The difference can be attributed to an inherent robustness to mismatches between volumes and external ΔB0 maps, and diverse other sources investigated.

Robust Space–Time Joint Sparse Processing Method with Airborne Active Array for Severely Inhomogeneous Clutter Suppression

Due to clutter inhomogeneity, the clutter suppression ability of space–time adaptive processing (STAP) is usually constrained by the insufficient number of independent and identically distributed (IID) clutter training samples and, as a result, is sacrificed to achieve the demanded sample reduction. Moreover, since clutter heterogeneity is exacerbated in the real environment, the IID training sample size can be heavily reduced, leading to the deterioration in clutter suppression. To solve this problem, a novel robust space–time joint sparse processing method with airborne active array is proposed. This method has several outstanding advantages: (1) only the single snapshot cell under test (CUT) data is used for the superior clutter suppression performance; and (2) the proposed method completely removes the dependence of the system processing ability on IID training samples. In this paper, the signal model of uniform transmitting subarray diversity is first established to obtain the single snapshot echo observed CUT data. Then, with the matched reconstruction, the single snapshot data are equivalently converted into multi-frame echo data. Finally, a fast multi-frame echo data joint sparse Bayesian algorithm is used to achieve heterogeneous clutter suppression. Numerous experiments were performed to verify the advantages of the proposed method.

Multi-Platforms and Multi-Sensors Integrated Survey for the Submerged and Emerged Areas

In this paper, the state-of-the-art concerning new methodologies for surveying in coastal areas in order to obtain an efficient quantification of submerged and emerged environments is described and evaluated. This work integrates an interdisciplinary approach involving both geomatics and robotics and focuses on definition, implementation, and development of a methodology to execute integrated aerial and underwater survey campaigns in shallow water areas. A preliminary test was performed at Gorzente Lakes near Genoa (Italy), to develop and integrate different survey techniques, enabling working in a smarter way, reducing costs and increasing safety for the operators. In this context, Remote Sensing techniques were integrated with a UAV (Unmanned Aerial Vehicle) carrying an aerial optical sensor for photogrammetry and with an ASV (Autonomous Surface Vehicle) expressly addressed to work in extremely shallow water with underwater acoustic sensors (single echo sounder). The obtained continuous seamless DSM (Digital Surface Model) for the entire environment was reconstructed by the combination of different sensing systems by limiting reliance on the GNSS (Global Navigation Satellite System) support. The obtained DSM was displayed in a 3D model leading to the evaluation of the water flow volume and rendering of 3D visualization.

Maternal psychological distress during the COVID-19 pandemic and structural changes of the human fetal brain

BackgroundElevated maternal psychological distress during pregnancy is linked to adverse outcomes in offspring. The potential effects of intensified levels of maternal distress during the COVID-19 pandemic on the developing fetal brain are currently unknown.MethodsWe prospectively enrolled 202 pregnant women: 65 without known COVID-19 exposures during the pandemic who underwent 92 fetal MRI scans, and 137 pre-pandemic controls who had 182 MRI scans. Multi-plane, multi-phase single shot fast spin echo T2-weighted images were acquired on a GE 1.5 T MRI Scanner. Volumes of six brain tissue types were calculated. Cortical folding measures, including brain surface area, local gyrification index, and sulcal depth were determined. At each MRI scan, maternal distress was assessed using validated stress, anxiety, and depression scales. Generalized estimating equations were utilized to compare maternal distress measures, brain volume and cortical folding differences between pandemic and pre-pandemic cohorts.ResultsStress and depression scores are significantly higher in the pandemic cohort, compared to the pre-pandemic cohort. Fetal white matter, hippocampal, and cerebellar volumes are decreased in the pandemic cohort. Cortical surface area and local gyrification index are also decreased in all four lobes, while sulcal depth is lower in the frontal, parietal, and occipital lobes in the pandemic cohort, indicating delayed brain gyrification.ConclusionsWe report impaired fetal brain growth and delayed cerebral cortical gyrification in COVID-19 pandemic era pregnancies, in the setting of heightened maternal psychological distress. The potential long-term neurodevelopmental consequences of altered fetal brain development in COVID-era pregnancies merit further study.