Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method

We investigate the following inverse problem:starting from the acoustic wave equation,reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound.When the amplitudes of the source are known a priori,we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities.When the singularities of the source are known a priori,we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes.The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry.The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

Criteria for assessing the diagnostic significance of modern methods of imaging gastrointestinal diseases in practical gastroenterology

Imaging methods are frequently used to diagnose gastrointestinal diseases and play a crucial role in verifying clinical diagnoses among all diagnostic algorithms.However,these methods have limitations,challenges,benefits,and advantages.Addressing these limitations requires the application of objective criteria to assess the effectiveness of each diagnostic method.The diagnostic process is dynamic and requires a consistent algorithm,progressing from clinical subjective data,such as patient history(anamnesis),and objective findings to diagnostics ex juvantibus.Caution must be exercised when interpreting diagnostic results,and there is an urgent need for better diagnostic tests.In the absence of such tests,preliminary criteria and a diagnosis ex juvantibus must be relied upon.Diagnostic imaging methods are critical stages in the diagnostic workflow,with sensitivity,specificity,and accuracy serving as the primary criteria for evaluating clinical,laboratory,and instrumental symptoms.A comprehensive evaluation of all available diagnostic data guarantees an accurate diagnosis.The“gold standard”for diagnosis is typically established through either the results of a pathological autopsy or a lifetime diagnosis resulting from a thorough examination using all diagnostic methods.

Clinical issues and challenges in imaging of gastrointestinal diseases:A minireview and our experience

Imaging techniques play a crucial role in the modern era of medicine,particularly in gastroenterology.Nowadays,various non-invasive and invasive imaging modalities are being routinely employed to evaluate different gastrointestinal(GI)diseases.However,many instrumental as well as clinical issues are arising in the area of modern GI imaging.This minireview article aims to briefly overview the clinical issues and challenges encountered in imaging GI diseases while highlighting our experience in the field.We also summarize the advances in clinically available diagnostic methods for evaluating different diseases of the GI tract and demonstrate our experience in the area.In conclusion,almost all imaging techniques used in imaging GI diseases can also raise many challenges that necessitate careful consideration and profound expertise in this field.

Autofocus technique for ISAR imaging of uniformly rotating targets based on the ExCoV method

The inverse synthetic aperture radar (ISAR) imaging can be converted into a sparse reconstruction problem and solved by the l1norm minimization algorithm. The basis matrix in sparse ISAR imaging is usually characterized by the unknown rotation rate of a moving target, thus the rotation rate and the sparse signal should be jointly estimated. Especially due to the imperfect coarse motion compensation, we consider the phase error correction problem in the context of the sparse signal reconstruction. To address this issue, we propose an iterative reweighted method, which jointly estimates the rotation rate, corrects the phase error and reconstructs a high resolution ISAR image. The proposed method gives a gradual and interweaved iterative process to refine the unknown parameters to achieve the best sparse representation for the ISAR signals. Particularly, in ISAR image reconstruction, the l1norm minimization algorithm is sensitive to user parameters. Setting these user parameters are not trivial and the reconstruction performance depends significantly on their choices. Then, we consider an expansion-compression variance-component (ExCoV) based method, which is automatic and demands no prior knowledge about signal-sparsity or measurement-noise levels. Both numerical and electromagnetic data experiments are implemented to show the effectiveness of the proposed method. It is shown that the proposed method can estimate the rotation rate and correct the phase errors simultaneously, and its superior performance is proved in terms of high resolution ISAR image. © 2017 Beijing Institute of Aerospace Information.

Efficient hybrid method for time reversal superresolution imaging

An efficient hybrid time reversal（TR） imaging method based on signal subspace and noise subspace is proposed for electromagnetic superresolution detecting and imaging. First, the locations of targets are estimated by the transmitting-mode decomposition of the TR operator（DORT） method employing the signal subspace. Then, the TR multiple signal classification（TR-MUSIC）method employing the noise subspace is used in the estimated target area to get the superresolution imaging of targets. Two examples with homogeneous and inhomogeneous background mediums are considered, respectively. The results show that the proposed hybrid method has advantages in CPU time and memory cost because of the combination of rough and fine imaging.

Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

Advancements and challenges in gastrointestinal imaging

A recent review by Gulinac et al,provides an in-depth analysis of current clinical issues and challenges in gastrointestinal imaging.This editorial highlights the advancements in imaging techniques,including the integration of artificial intelligence and functional imaging modalities,and discusses the ongoing relevance of traditional nuclear medicine tests.The future of gastrointestinal imaging looks promising,with continuous improvements in resolution,enhanced ability to analyze color and texture beyond visual diagnosis,faster image processing,and the application of molecular imaging and nanoparticles expected to enhance diagnostic accuracy and clinical outcomes.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

Distance-Coefficient-Based Imaging Accuracy Improving Method Based on the Lamb Wave

An imaging accuracy improving method is established, within which a distance coefficient including location information between sparse array configuration and the location of defect is proposed to select higher signal- to-noise ratio data from all experimental data and then to use these selected data for elliptical imaging. Tile relationships among imaging accuracy, distance coefficient and residual direct wave are investigated, and then the residual direct wave is introduced to make the engineering application more convenient. The effectiveness of the proposed method is evaluated experimentally by sparse transducer array of a rectangle, and the results reveal that selecting experimental data of smaller distance coefficient can effectively improve imaging accuracy. Moreover, the direct wave difference increases with the decrease of the distance coefficient, which implies that the imaging accuracy can be effectively improved by using the experimental data of the larger direct wave difference.

High-contrast imaging based on wavefront shaping to improve low signal-to-noise ratio photoacoustic signals using superpixel method

Photoacoustic(PA) imaging has drawn tremendous research interest for various applications in biomedicine and experienced exponential growth over the past decade. Since the scattering effect of biological tissue on ultrasound is two-to three-orders magnitude weaker than that of light, photoacoustic imaging can effectively improve the imaging depth.However, as the depth of imaging further increases, the incident light is seriously affected by scattering that the generated photoacoustic signal is very weak and the signal-to-noise ratio(SNR) is quite low. Low SNR signals can reduce imaging quality and even cause imaging failure. In this paper, we proposed a new wavefront shaping and imaging method of low SNR photoacoustic signal using digital micromirror device(DMD) based superpixel method. We combined the superpixel method with DMD to modulate the phase and amplitude of the incident light, and the genetic algorithm(GA) was used as the wavefront shaping algorithm. The enhancement of the photoacoustic signal reached 10.46. Then we performed scanning imaging by moving the absorber with the translation stage. A clear image with contrast of 8.57 was obtained while imaging with original photoacoustic signals could not be achieved. The proposed method opens new perspectives for imaging with weak photoacoustic signals.

AN AUTOMATIC SEGMENTATION METHOD FOR FAST IMAGING IN PET

A new segmentation method has been developed for PET fast imaging. The technique automatically segments the transmission images into different anatomical regions, it efficiently reduced the PET transmission scan time. The result shows that this method gives only 3 min-scan time which is perfect for attenuation correction of the PET images instead of the original 15-30 min-scan time. This approach has been successfully tested both on phantom and clinical data.

SAR imaging method for sea scene target based on improved phase retrieval algorithm

Due to the influence of the platform random motion and electromagnetic propagation in turbulent media, the synthetic aperture radar (SAR) high resolution imaging for the sea scenes where there are large amounts of water returns with some target (land) returns is very difficult. To solve this problem, a SAR imaging method based on the improved phase retrieval (PR) algorithm is proposed. First, a filter is added to the conventional PR algorithm which can reduce the influence of water returns on the reconstruction of the targets and improve the reconstruction result of the targets. Then, the corrupted phase of the Fourier transform of the intensity image in the iterative process, which can improve the stability of the iterative algorithm, is used to reduce the recovery errors, and a better recovery performance is achieved. Finally, several experiments are performed to demonstrate the advantages of the proposed method. © 2016 Beijing Institute of Aerospace Information.

A new method of migration imaging for MT data

Based on the study on electromagnetic field migration by Zhdanov, we have proposed an improved method for the weak points in the research. Firstly, the initial background resistivity should be determined by using 1-D inversion results. Then in the process of continuation, the results are corrected and calculated layer by layer by the iteration method, so that more exact resistivity can be obtained. Secondly, an improved algorithm for finite-difference equation is studied. According to the property of electromagnetic migration field, the algorithm is designed by means of grids varying with geometric progression in the longitudinal direction. Being improved by the techniques mentioned above, better results are obtained by the new method, which has been verified by both the theory model and practical data.

Linearized Proximal Alternating Direction Method of Multipliers for Parallel Magnetic Resonance Imaging

In this study, we propose a linearized proximal alternating direction method with variable stepsize for solving total variation image reconstruction problems. Our method uses a linearized technique and the proximal function such that the closed form solutions of the subproblem can be easily derived.In the subproblem, we apply a variable stepsize, that is like Barzilai-Borwein stepsize, to accelerate the algorithm. Numerical results with parallel magnetic resonance imaging demonstrate the efficiency of the proposed algorithm.

A Compressed Terahertz Imaging Method

A compressed terahertz imaging method using a terahertz time domain spectroscopy system(THz-TDSS)is suggested and demonstrated.In the method,a parallel THz wave with the beam diameter 4 cm from a usual THz-TDSS is used and a square shaped 2D echelon is placed in front of an imaged object.We confirm both in simulation and in experiment that only one terahertz time domain spectrum is needed to image the object.The image information is obtained from the compressed THz signal by deconvolution signal processing,and therefore the whole imaging time is greatly reduced in comparison with some other pulsed THz imaging methods.The present method will hopefully be used in real-time imaging.

A fast-imaging method for airborne gravity gradient data based on tensor invariants

Airborne gravity gradient data contain additional short-wavelength information about the buried geological bodies.This study develops a fast interpretation method based on the gravity gradient data for the sources’spatial location and physical property parameters.This study analyzes the advantages of the source parameter inversion method based on tensor invariants.It proposes a normalized fast-imaging method based on tensor invariants to quickly estimate the spatial location parameters of sources through the local maximum value position of the imaging results.First,the tensor invariant characteristics and the imaging method’s effect in a simple model are analyzed using a theoretical model.Second,to analyze the imaging method’s application effect in complex model conditions,the method’s applicability is quantitatively analyzed using the data added with noise,superimposed anomalies of adjacent sources,and anomalies of deep and shallow geological bodies.The theoretical model’s simulation results show that the model’s imaging results in this study have satisfactory performance on the spatial position estimation of the sources.Finally,the method is applied to the gravity anomaly data corresponding to the Humble salt dome.The imaging results can effectively estimate the distribution of the salt dome’s horizontal and depths,verifying the practicability of the method.

A review of the wave gradiometry method for seismic imaging

As dense seismic arrays at different scales are deployed,the techniques to make full use of array data with low computing cost become increasingly needed.The wave gradiometry method(WGM)is a new branch in seismic tomography,which utilizes the spatial gradients of the wavefield to determine the phase velocity,wave propagation direction,geometrical spreading,and radiation pattern.Seismic wave propagation parameters obtained using the WGM can be further applied to invert 3D velocity models,Q values,and anisotropy at lithospheric(crust and/or mantle)and smaller scales(e.g.,industrial oilfield or fault zone).Herein,we review the theoretical foundation,technical development,and major applications of the WGM,and compared the WGM with other commonly used major array imaging methods.Future development of the WGM is also discussed.

Design of small-scale gradient coils in magnetic resonance imaging by using the topology optimization method

A topology optimization method based on the solid isotropic material with penalization interpolation scheme is utilized for designing gradient coils for use in magnetic resonance microscopy.Unlike the popular stream function method,the proposed method has design variables that are the distribution of conductive material.A voltage-driven transverse gradient coil is proposed to be used as micro-scale magnetic resonance imaging(MRI)gradient coils,thus avoiding introducing a coil-winding pattern and simplifying the coil configuration.The proposed method avoids post-processing errors that occur when the continuous current density is approximated by discrete wires in the stream function approach.The feasibility and accuracy of the method are verified through designing the z-gradient and y-gradient coils on a cylindrical surface.Numerical design results show that the proposed method can provide a new coil layout in a compact design space.

A Study of Different Unwarping Methods for Omnidirectional Imaging

Omni-directional imaging system is becoming more and more common in reducing the maintenance fees and the number of cameras used as well as increasing the angle of view in a single camera. Due to omni-directional images are not directly understandable, an approach namely the un-warping process, has been implemented in converting the omni-directional image to a panoramic image, making it understandable. There are different kinds of methods used for the implementation of this approach. This paper evaluates the performance of the 3 universal un-warping methods currently applied actively around the world in transforming omni-directional image to panoramic image, namely the pano-mapping table method, discrete geometry method （DGT） and the log-polar mapping method. The algorithm of these methods will first be proposed, and the code will then be generated and be tested on several different omni-directional images. The images converted will then be compared among each other and be evaluated based on their performance on the resolutions, quality, algorithm used, complexity based on Big-O computations, processing time, and finally their data compression rate available for each of the methods. The most preferable un-warping method will then be concluded, taking into considerations all these factors.

FITTING CORRECTION METHOD OF RING ARTIFACTS FOR RECONSTRUCTING CONE-BEAM CT IMAGES

In high-resolution cone-beam computed tomography （CBCT） using the flat-panel detector, imperfect or defect detector elements cause ring artifacts due to the none-uniformity of their X-ray response. They often disturb the image quality. A dedicated fitting correction method for high-resolution micro-CT is presented. The method converts each elementary X-ray response curve to an average one, and eliminates response inconsistency among pixels. Other factors of the method are discussed, such as the correction factor variability by different sampling frames and nonlinear factors over the whole spectrum. Results show that the noise and artifacts are both reduced in reconstructed images