Investigating the relationship between intracranial atherosclerotic plaque remodelling and diabetes using high-resolution vessel wall imaging

BACKGROUND Intracranial atherosclerosis,a leading cause of stroke,involves arterial plaque formation.This study explores the link between plaque remodelling patterns and diabetes using high-resolution vessel wall imaging(HR-VWI).AIM To investigate the factors of intracranial atherosclerotic remodelling patterns and the relationship between intracranial atherosclerotic remodelling and diabetes mellitus using HR-VWI.METHODS Ninety-four patients diagnosed with middle cerebral artery or basilar artery INTRODUCTION Intracranial atherosclerotic disease is one of the main causes of ischaemic stroke in the world,accounting for approx-imately 10%of transient ischaemic attacks and 30%-50%of ischaemic strokes[1].It is the most common factor among Asian people[2].The adaptive changes in the structure and function of blood vessels that can adapt to changes in the internal and external environment are called vascular remodelling,which is a common and important pathological mechanism in atherosclerotic diseases,and the remodelling mode of atherosclerotic plaques is closely related to the occurrence of stroke.Positive remodelling(PR)is an outwards compensatory remodelling where the arterial wall grows outwards in an attempt to maintain a constant lumen diameter.For a long time,it was believed that the degree of stenosis can accurately reflect the risk of ischaemic stroke[3-5].Previous studies have revealed that lesions without significant luminal stenosis can also lead to acute events[6,7],as summarized in a recent meta-analysis study in which approximately 50%of acute/subacute ischaemic events were due to this type of lesion[6].Research[8,9]has pointed out that the PR of plaques is more dangerous and more likely to cause acute ischaemic stroke.Previous studies[10-13]have found that there are specific vascular remodelling phenomena in the coronary and carotid arteries of diabetic patients.However,due to the deep location and small lumen of intracranial arteries and limitations of imaging techniques,the relationship between intracranial arterial remodelling and diabetes is still unclear.In recent years,with the development of magnetic resonance technology and the emergence of high-resolution(HR)vascular wall imaging,a clear and multidimensional display of the intracranial vascular wall has been achieved.Therefore,in this study,HR wall imaging(HR-VWI)was used to display the remodelling characteristics of bilateral middle cerebral arteries and basilar arteries and to explore the factors of intracranial vascular remodelling and its relationship with diabetes.

In vivo pilot study into superficial microcirculatory characteristics of colorectal adenomas using novel high-resolution magnifying endoscopy with blue laser imaging

BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a novel high-resolution magnification endoscopy with blue laser imaging(BLI),thus providing a new insight into the microcirculation of early colon tumors.AIM To observe the superficial microcirculation of colorectal adenomas using the novel magnifying colonoscope with BLI and quantitatively analyzed the changes in hemodynamic parameters.METHODS From October 2019 to January 2020,11 patients were screened for colon adenomas with the novel high-resolution magnification endoscope with BLI.Video images were recorded and processed with Adobe Premiere,Adobe Photoshop and Image-pro Plus software.Four microcirculation parameters:Microcirculation vessel density(MVD),mean vessel width(MVW)with width standard deviation(WSD),and blood flow velocity(BFV),were calculated for adenomas and the surrounding normal mucosa.RESULTS A total of 16 adenomas were identified.Compared with the normal surrounding mucosa,the superficial vessel density in the adenomas was decreased(MVD:0.95±0.18 vs 1.17±0.28μm/μm2,P<0.05).MVW(5.11±1.19 vs 4.16±0.76μm,P<0.05)and WSD(11.94±3.44 vs 9.04±3.74,P<0.05)were both increased.BFV slowed in the adenomas(709.74±213.28 vs 1256.51±383.31μm/s,P<0.05).CONCLUSION The novel high-resolution magnification endoscope with BLI can be used for in vivo study of adenoma superficial microcirculation.Superficial vessel density was decreased,more irregular,with slower blood flow.

High-resolution magnetic resonance imaging in the diagnosis and management of vertebral artery dissection:A case report

BACKGROUND Vertebral artery dissection(VAD)is a rare but life-threatening condition characterized by tearing of the intimal layer of the vertebral artery,leading to stenosis,occlusion or rupture.The clinical presentation of VAD can be heterogeneous,with common symptoms including headache,dizziness and balance problems.Timely diagnosis and treatment are crucial for favorable outcomes;however,VAD is often missed due to its variable clinical presentation and lack of robust diagnostic guidelines.High-resolution magnetic resonance imaging(HRMRI)has emerged as a reliable diagnostic tool for VAD,providing detailed visualization of vessel wall abnormalities.CASE SUMMARY A young male patient presented with an acute onset of severe headache,vomiting,and seizures,followed by altered consciousness.Imaging studies revealed bilateral VAD,basilar artery thrombosis,multiple brainstem and cerebellar infarcts,and subarachnoid hemorrhage.Digital subtraction angiography(DSA)revealed vertebral artery stenosis but failed to detect the dissection,potentially because intramural thrombosis obscured the VAD.In contrast,HRMRI confirmed the diagnosis by revealing specific signs of dissection.The patient was managed conservatively with antiplatelet therapy and other supportive measures,such as blood pressure control and pain management.After 5 mo of rehabilitation,the patient showed significant improvement in swallowing and limb strength.CONCLUSION HR-MRI can provide precise evidence for the identification of VAD.

HIGH-RESOLUTION 35GHz COHERENT LFM-CW RADAR FOR TWO-DIMENSIONAL IMAGING

This paper presents the system design and experimental results of a 35GHz coherent LFM-CW radar for use in near range two-dimensional imaging. Special techniques, including FM sweep linearization, range-segment processing, system background voltage suppression and dynamic range compression are also presented.

Electromagnetic scattering and imaging simulation of extremely large-scale sea-ship scene based on GPU parallel technology

Aiming to solve the bottleneck problem of electromagnetic scattering simulation in the scenes of extremely large-scale seas and ships,a high-frequency method by using graphics processing unit(GPU)parallel acceleration technique is proposed.For the implementation of different electromagnetic methods of physical optics(PO),shooting and bouncing ray(SBR),and physical theory of diffraction(PTD),a parallel computing scheme based on the CPU-GPU parallel computing scheme is realized to balance computing tasks.Finally,a multi-GPU framework is further proposed to solve the computational difficulty caused by the massive number of ray tubes in the ray tracing process.By using the established simulation platform,signals of ships at different seas are simulated and their images are achieved as well.It is shown that the higher sea states degrade the averaged peak signal-to-noise ratio(PSNR)of radar image.

A fast decoupled ISAR high-resolution imaging method using structural sparse information under low SNR

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

Research on monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration

In this paper,we proposed a monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration for missile-borne detector.Through iteration,the proposed algorithm automatically selects the echo signal of isolated strong-scattering points from the receiving echo signal data to accurately estimate the actual optimal monopulse response curve(MRC) of the same distance range,and we applied optimal MRC to realize the azimuth self-focusing in the process of imaging.We use real-time echo data to perform error correction for obtaining the optimal MRC,and the azimuth angulation accuracy may reach the optimum at a certain distance dimension.We experimentally demonstrate the validity,reliability and high performance of the proposed algorithm.The azimuth angulation accuracy may reach up to ten times of the detection beam-width.The simulation experiments have verified the feasibility of this strategy,with the average height measurement error being 7.8%.In the out-field unmanned aerial vehicle(UAV) tests,the height measurement error is less than 25 m,and the whole response time can satisfy the requirements of a missile-borne detector.

High-resolution forward-looking imaging algorithm for missile-borne detectors

Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions;thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

Exploratory study of non-invasive,high-resolution functional macular imaging in subjects with diabetic retinopathy

AIM:To evaluate a high-resolution functional imaging device that yields quantitative data regarding macular blood flow and capillary network features in eyes with diabetic retinopathy(DR).METHODS:Prospective,cross-sectional comparative case-series in which blood flow velocities(BFVs)and noninvasive capillary perfusion maps(nCPMs)in macular vessels were measured in patients with DR and in healthy controls using the Retinal Functional Imager(RFI)device.RESULTS:A total of 27 eyes of 21 subjects were studied[9 eyes nonproliferative diabetic retinopathy(NPDR),9 eyes proliferative diabetic retinopathy(PDR)and 9 controls].All diabetic patients were type 2.All patients with NPDR and 5 eyes with PDR also had diabetic macular edema(DME).The NPDR group included eyes with severe(n=3)and moderate NPDR(n=6),and were symptomatic.A significant decrease in venular BFVs was observed in the macular region of PDR eyes when compared to controls(2.61±0.6 mm/s and 2.92±0.72 mm/s in PDR and controls,respectively,P=0.019)as well as PDR eyes with DME compared to NPDR eyes(2.36±0.51 mm/s and 2.94±1.09 mm/s in PDR with DME and NPDR,respectively,P=0.01).CONCLUSION:The RFI,a non-invasive imaging tool,provides high-resolution functional imaging of the retinal microvasculature and quantitative measurement of BFVs in visually impaired DR patients.The isolated diminish venular BFVs in PDR eyes compared to healthy eyes and PDR eyes with DME in comparison to NPDR eyes may indicate the possibility of more retinal vein compromise than suspected in advanced DR.

Operation Control of a High-resolution Plasma Imaging System in KT5D Torus

This paper describes the control software together with the operational hardware, which successfully realizes the operation of a new fully programmable imaging system with high spatial and temporal resolutions on the KT5D magnetic torus, for observing the visible l ight emission from the plasma discharge.

OPTIMIZATION OF WEIGHTED HIGH-RESOLUTION RANGE PROFILE FOR RADAR TARGET RECOGNITION

For the recognition of high-resolution range profile （HRRP） in radar, the weighted HRRP can reduce the instability of range cells caused by the attitude change of targets. A novel approach is proposed to optimize the weighted HRRP. In the approach, the separability of weighted HRRPs in different targets is measured by de- signing an objective function, and the weighted coefficients are computed by using the gradient descent method, thus enhancing the influence of stable range cells. Simulation results based on five aircraft models show that the approach can effectively optimize the weighted HRRP and improve the recognition accuracy.

Entropy function optimization for radar imaging

The convergence performance of the minimum entropy auto-focusing（MEA） algorithm for inverse synthetic aperture radar（ISAR） imaging is analyzed by simulation. The results show that a local optimal solution problem exists in the MEA algorithm. The cost function of the MEA algorithm is not a downward-convex function of multidimensional phases to be compensated. Only when the initial values of the compensated phases are chosen to be near the global minimal point of the entropy function, the MEA algorithm can converge to a global optimal solution. To study the optimal solution problem of the MEA algorithm, a new scheme of entropy function optimization for radar imaging is presented. First, the initial values of the compensated phases are estimated by using the modified Doppler centroid tracking （DCT）algorithm. Since these values are obtained according to the maximum likelihood （ML） principle, the initial phases can be located near the optimal solution values. Then, a fast MEA algorithm is used for the local searching process and the global optimal solution can be obtained. The simulation results show that this scheme can realize the global optimization of the MEA algorithm and can avoid the selection and adjustment of parameters such as iteration step lengths, threshold values, etc.

Deep convolutional neural network for meteorology target detection in airborne weather radar images

Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Application of Superresolution Techniques to Radar Imaging

Modern spectral estimation techniques (superresolution in technical jargon) have been applied to many fields of signal processing since many years[1][2]. Application to radar imaging, mainlyto ISAR (Inverse Synthetic Aperture Radar) is documented in some recent papers[3] to [6]. Applications have been attempted also to SAR (Synthetic Aperture Radar)[7][8]. In these fields the benefit ofspectral estimation reveals in a resolution beyond the Rayleigh limits set by compressed pulse andsynthetic aperture lengths. Furthermore very low sidelobes of point scatterer response are obtained.In this paper superresolution has been applied both to simulated stepped-frequency ISAR dataand to real ERS-1 SAR data; the achieved results are encouraging and suggest a more extensivepractical application of the technique. The paper is organized in two parts. In the first we have applied the autoregressive (AR) and the minimum variance (MV)-Capon methods to improve therange resolution of simulated ISAR data. In the second part we have conceived an upgraded versionof spectral analysis (SPECAN) processing to obtain a SAR image of better quality. The method hasbeen tested on recorded live data of ERS-1 mission.

Novel imaging methods of stepped frequency radar based on compressed sensing

The theory of compressed sensing （CS） provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Ex- periments from both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier trans- form method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless.

Root imaging from ground penetrating radar data by CPSO-OMP compressed sensing

As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.

Use of high-resolution X-ray computed tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide copper ore particles

Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance. To quantify the mineral dissemination and pore space distribution of an ore particle, a cylindrical copper oxide ore sample (I center dot 4.6 mm x 5.6 mm) was scanned using high-resolution X-ray computed tomography (HRXCT), a nondestructive imaging technology, at a spatial resolution of 4.85 mu m. Combined with three-dimensional (3D) image analysis techniques, the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated. In addition, the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques. Furthermore, the pore phase features, including the pore size distribution, pore surface area, pore fractal dimension, pore centerline, and the pore connectivity, were investigated quantitatively. The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated, with a large surface area and low connectivity. This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.

Improved HHT and its application in narrowband radar imaging for precession cone-shaped targets

Imaging the spatial precession cone-shaped targets with narrowband radar is a new technical approach in mid-course recognition problem. However, most existing time-frequency methods still have some inevitable deficiencies for extracting microDoppler information in practical applications, which leads to blurring of the image. A new narrowband radar imaging algorithm for the precession cone-shaped targets is proposed. The instantaneous frequency of each scattering point is gained by using the improved Hilbert-Huang transform, then the positions of scattering points in the parameter domain are reconstructed. Numerical simulation and experiment results confirm the effectiveness and high precision of the proposed algorithm.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.