Imaging Of Multiple Targets Research Articles

Task Scheduling for Multi-Target ISAR Imaging in Radar Network

For radar imaging of multiple targets in radar network, it is necessary to schedule the imaging tasks among various radars at suitable time to achieve high performance under limited radar resources. In this paper, based on the image quality requirement, a task schedule method is proposed for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network. Due to the image resolution is an important indicator of image quality and the imaging task is time sensitive, the relationship between the imaging resolution and task time is studied firstly. Thereafter, the task scheduling problem is converted into an optimization problem with time window constrains and an improved Quantum Genetic Algorithm (IQGA) is proposed to solve the problem. Then the task scheduling strategy which contains how to allocate the targets to the radars and when to observe the targets can be obtained. Finally, simulation results verify the effectiveness of the proposed method. With the help of the propose method, the multi-target ISAR task can be completed effectively and the resource utilization of the radar network can be improved.

An Inverse Synthetic Aperture Radar Image Modulation Method Based on Coding Phase-Switched Screen

Multiple false target images deception is an effective strategy to protect vital military targets from detection and recognition by inverse synthetic aperture radar (ISAR). Previous research about it focuses mainly on the interrupted-sampling and sub-Nyquist sampling repeater jamming based on digital radio frequency memory (DRFM). The digital image synthesis (DIS) technique or scatter-wave modulation is used to simulate electromagnetic scattering of the real target. However, it is difficult to realize due to the high reconnaissance accuracy requirement and a heavy computational burden. Moreover, the protected target remains in the same location and the false targets' amplitude envelope based on periodic modulation is relatively fixed, which is not conducive to deception. In this paper, an ISAR image modulation method based on coding phase-switched screen (PSS) is proposed against ISAR. The method uses the target bait made of PSS or attaches the PSS to the protected target surface to impose phase modulation onto the reflected signal. As a result, multiple false target images with verisimilar properties are formed around the protected target position when the reflected signal is received and processed by the victim ISAR. Besides, the protected target is concealed and the flexible amplitude control of false targets is achieved. The simulation results are performed to verify the effectiveness of the proposed method.

Circular Scan ISAR Mode Super-Resolution Imaging of Ships Based on a Combination of Data Extrapolation and Compressed Sensing

Airborne radars for maritime surveillance usually operate in scanning mode to detect marine objects over 360 degrees. More recently, the concept of forming inverse synthetic aperture radar (ISAR) images while the antenna is scanning, called circular scan ISAR in this paper, has drawn attention. Circular scan ISAR enables a large coverage area, sequential imaging of multiple targets, and the formation of an image sequence of a target. However, the rapid antenna scanning results in a short accumulation time of the target and reduces the Doppler resolution of the system. This paper analyzes the limit values of the Doppler resolution improvement ratio when super-resolution imaging is performed using only the compressed sensing method by relating the limit values to the restricted isometry constant (RIC) value of the sensing matrices. The limit values are found to be relatively small when the sparsity degree of the sparse signal is relatively large. Therefore, the super-resolution imaging of ships for circular scan ISAR is carried out by extrapolating data in the azimuth direction first and then reconstructing the data via compressed sensing to obtain low side-lobe levels. Finally, the constant false alarm rate (CFAR) detection is performed on the matrix composed of calculated sparse coefficients to improve the signal-to-clutter noise ratio (SCNR). The simulation and real-data processing results are provided to verify the effectiveness of the presented algorithm.

Functional imaging of tumor vasculature using iodine and gadolinium-based nanoparticle contrast agents: a comparison of spectral micro-CT using energy integrating and photon counting detectors

Advances in computed tomography (CT) hardware have propelled the development of novel CT contrast agents. In particular, the spectral capabilities of x-ray CT can facilitate simultaneous imaging of multiple contrast agents. This approach is particularly useful for functional imaging of solid tumors by simultaneous visualization of multiple targets or architectural features that govern cancer development and progression. Nanoparticles are a promising platform for contrast agent development. While several novel imaging moieties based on high atomic number elements are being explored, iodine (I) and gadolinium (Gd) are particularly attractive because of their existing approval for clinical use. In this work, we investigate the in vivo discrimination of I and Gd nanoparticle contrast agents using both dual energy micro-CT with energy integrating detectors (DE-EID) and photon counting detector (PCD)-based spectral micro-CT. Simulations and phantom experiments were performed using varying concentrations of I and Gd to determine the imaging performance with optimized acquisition parameters. Quantitative spectral micro-CT imaging using liposomal-iodine (Lip-I) and liposomal-Gd (Lip-Gd) nanoparticle contrast agents was performed in sarcoma bearing mice for anatomical and functional imaging of tumor vasculature. Iterative reconstruction provided high sensitivity to detect and discriminate relatively low I and Gd concentrations. According to the Rose criterion applied to the experimental results, the detectability limits for I and Gd were approximately 2.5 mg ml−1 for both DE-EID CT and PCD micro-CT, even if the radiation dose was approximately 3.8 times lower with PCD micro-CT. The material concentration maps confirmed expected biodistributions of contrast agents in the blood, liver, spleen and kidneys. The PCD provided lower background signal and better simultaneous visualization of tumor vasculature and intratumoral distribution patterns of nanoparticle contrast agent compared to DE-EID decompositions. Preclinical spectral CT systems such as this could be useful for functional characterization of solid tumors, simultaneous quantitative imaging of multiple targets and for identifying clinically-relevant applications that benefit from the use of spectral imaging. Additionally, it could aid in the development nanoparticles that show promise in the developing field of cancer theranostics (therapy and diagnostics) by measuring vascular tumor biomarkers such as fractional blood volume and the delivery of liposomal chemotherapeutics.

Nanobody Detection of Standard Fluorescent Proteins Enables Multi-Target DNA-PAINT with High Resolution and Minimal Displacement Errors.

DNA point accumulation for imaging in nanoscale topography (PAINT) is a rapidly developing fluorescence super-resolution technique, which allows for reaching spatial resolutions below 10 nm. It also enables the imaging of multiple targets in the same sample. However, using DNA-PAINT to observe cellular structures at such resolution remains challenging. Antibodies, which are commonly used for this purpose, lead to a displacement between the target protein and the reporting fluorophore of 20–25 nm, thus limiting the resolving power. Here, we used nanobodies to minimize this linkage error to ~4 nm. We demonstrate multiplexed imaging by using three nanobodies, each able to bind to a different family of fluorescent proteins. We couple the nanobodies with single DNA strands via a straight forward and stoichiometric chemical conjugation. Additionally, we built a versatile computer-controlled microfluidic setup to enable multiplexed DNA-PAINT in an efficient manner. As a proof of principle, we labeled and imaged proteins on mitochondria, the Golgi apparatus, and chromatin. We obtained super-resolved images of the three targets with 20 nm resolution, and within only 35 minutes acquisition time.

Hydrophilic fluorinated molecules for spectral 19F MRI

Fluorine-19 (19F) Magnetic Resonance Imaging (MRI) is an emerging modality for molecular imaging and cell tracking. The hydrophobicity of current exogenous probes, perfluorocarbons (PFCs) and perfluoropolyethers (PFPEs), limits the formulation options available for in vivo applications. Hydrophilic probes permit more formulation flexibility. Further, the broad Nuclear Magnetic Resonance (NMR) chemical shift range of organofluorine species enables multiple probes with unique 19F MR signatures for simultaneous interrogation of distinct molecular targets in vivo. We report herein a flexible approach to stable liposomal formulations of hydrophilic fluorinated molecules (each bearing numerous magnetically equivalent 19F atoms), with 19F encapsulation of up to 22.7 mg/mL and a per particle load of 3.6 × 106 19F atoms. Using a combination of such probes, we demonstrate, with no chemical shift artifacts, the simultaneous imaging of multiple targets within a given target volume by spectral 19F MRI.

Task Allocation Optimization for Multi-Target ISAR Imaging in Radar Network

For radar imaging of multiple targets in different beams, it is necessary for the radar network to coordinate the imaging task among various radars to achieve the best multi-target imaging performance under limited system resources. This paper proposes a task allocation optimization method of inverse synthetic aperture radar (ISAR) imaging for multiple targets. The imaging resolution is an important indicator of the imaging quality. Therefore, the relationship between the imaging resolution and task time is analyzed at first. Thereafter, the task allocation problem is converted into a time resource optimization problem of the radar network. Combining with the sparse aperture ISAR imaging algorithm, the task allocation optimization model of the radar network is constructed, and the corresponding algorithm for solving the model is proposed. The proposed method can effectively implement the multi-target ISAR imaging task allocation for the radar network. Moreover, it can greatly improve the resource utilization of the radar network, while meeting the imaging resolution requirements. Finally, some experiments have been conducted to verify the effectiveness of the proposed method.

IRIS super-resolution microscopy enables to image multiple protein distributions in a single cell

Cellular phenomena such as cell division and migration are regulated by various proteins. To study the complex cellular systems, super-resolution microscopy that can visualize protein distribution with a resolution of 20-100 nm is useful. But the conventional super-resolution microscopy has a limitation in the number of observable targets due to the number of usable fluorescent dyes (generally two or three). Also the super-resolution microscopy reveals the uneven labeling pattern of antibodies and fluorescent proteins on the target distribution with the high resolution. This means that imaging of the accurate distribution of targets is very difficult with the conventional labeling method. To solve the two problems, we recently have developed a multitarget super-resolution microscopy called image reconstruction by integrating exchangeable single-molecule localization (IRIS) (Kiuchi et al., Nature Methods, 12: 743-746, 2015). In IRIS, exchangeable probes that repeatedly associate with and dissociate from their targets are used for labeling, instead of antibodies and fluorescent proteins. The localization of the binding probe is determined as the central position of the fluorescent speckle with high accuracy. Integration of the positions produces the high resolution image of the target distribution. Washing out the probes and adding new probes one after another allow for the sequential imaging of multiple targets within a single specimen. In addition, the repeated associations of the probes enable to increase the labeling efficiency in the target distribution unlimitedly. To verify the IRIS method, we imaged actin filaments using Lifeact that repeatedly associates with and dissociates from actin filaments. The super-resolved image demonstrated that the labeling density of Lifeact readily reached 60 times the maximum labeling density attainable with antibody binding. For multitarget IRIS imaging, we screened 18 IRIS exchangeable probes out of 46 fragments of 15 cytoskeleton- or focal adhesion-associated proteins. IRIS using these probes discerned the area-specific proximity of actin filaments, microtubules, intermediate filaments and focal adhesions within a single cell (Fig. 1). Thus IRIS approach overcomes the limitation on labeling efficiency and enables to image the multiple target distributions with high fidelity.

A Novel Study on Direct Sampling Method for Imaging Multiple Targets

The Direct Sampling Method (DSM) is well known to be a fast, stable, and effective detection technique in inverse scattering problems, but its analysis is restricted to imaging of a single target. Motivated by this, we investigate the mathematical structure of DSM for imaging small multiple dielectric targets by establishing a relationship with the radii and permittivities of targets, and Bessel function of order zero. This structure explains why DSM can be applied to the imaging of multiple targets. Numerical results support our findings.

Through-Wall Single and Multiple Target Imaging Using MIMO Radar

The ability to perform target detection through walls and barriers is important for law enforcement, homeland security, and search and rescue teams. Multiple-input-multiple-output (MIMO) radar provides an improvement over traditional phased array radars for through-wall imaging. By transmitting independent waveforms from a transmit array to a receive array, an effective virtual array is created. This array has improved degrees of freedom over phased arrays and mono-static MIMO systems. This virtual array allows us to achieve the same effective aperture length as a phased array with a lower number of elements because the virtual array can be described as the convolution of transmit and receive array positions. In addition, data from multiple walls of the same room can be used to collect target information. If two walls are perpendicular to each other and the geometry of transmit and receive arrays is known, then data can be processed independently of each other. Since the geometry of the arrays is known, a target scene can be created where the two data sets overlap. The overlapped scene can then be processed so that image artifacts that do not correlate between the data sets can be excised. The result gives improved target detection, reduction in false alarms, robustness to noise, and robustness against errors such as improperly aligned antennas. This paper explores MIMO radar techniques for target detection and localization behind building walls and addresses different mitigation techniques, such as a singular value decomposition of wavelet transform method to improve localization and detection of targets. Together, these techniques demonstrate methods that show a reduction in size and complexity of traditional through-wall radar systems while still providing accurate detection and localization. The use of the range migration algorithm in single and multi-target scenarios is shown to provide adequate imaging of through the wall targets in near and far field. Also, a multi-view algorithm is used to provide improved target detection and localization by fusing together multiple wall views.

Combining Primed Photoconversion and UV-Photoactivation for Aberration-Free, Live-Cell Compliant Multi-Color Single-Molecule Localization Microscopy Imaging

Super-resolution fluorescence microscopy plays a major role in revealing the organization and dynamics of living cells. Nevertheless, single-molecule localization microscopy imaging of multiple targets is still limited by the availability of suitable fluorophore combinations. Here, we introduce a novel imaging strategy which combines primed photoconversion (PC) and UV-photoactivation for imaging different molecular species tagged by suitable fluorescent protein combinations. In this approach, the fluorescent proteins can be specifically photoactivated/-converted by different light wavelengths using PC and UV-activation modes but emit fluorescence in the same spectral emission channel. We demonstrate that this aberration-free, live-cell compatible imaging method can be applied to various targets in bacteria, yeast and mammalian cells and can be advantageously combined with correlative imaging schemes.

Hybrid SAR/ISAR imaging of ship targets based on parameter estimation

ABSTRACTTypical Synthetic Aperture Radar (SAR) imaging deals with static targets. This means that moving targets appear blurred in SAR images. Difficulties in imaging are especially noticeable for moving ship targets at sea due to sea waves and each targets individual movement characteristics. Presently, there are no satisfactory results for hybrid SAR and Inverse Synthetic Aperture Radar (ISAR) imaging of multiple ship targets. Focussing on this problem, this letter proposes an improved airborne hybrid SAR/ISAR imaging algorithm based on parameter estimation. In this algorithm, Doppler parameters are estimated by using Radon-Wigner transform (RWT) at first. Then, we improve the traditional data extraction method, in which multi-objective problems are solved through data pre-processing and original characteristics of the data are preserved to the maximum extent. Three representative types of the ship data are selected in this letter and we extract them. Finally, effective ISAR motion compensation methods are adopted for the purpose of gaining focus results. The final images verify a strong feasibility of the proposed algorithm and it provides a good idea for moving ship targets imaging.

Sparsity-Driven SAR Imaging for Highly Maneuvering Ground Target by the Combination of Time-Frequency Analysis and Parametric Bayesian Learning

In this paper, a well-focused synthetic aperture radar (SAR) image for highly maneuvering ground target is formed. For high-resolution SAR imaging, the phase modulation from the maneuverer's high-order movements severely degrades the focusing quality of the target image, if the conventional SAR imaging algorithm under the constant target velocity assumption is used. To deal with this problem, a new SAR ground moving target imaging (GMTIm) algorithm is presented with a two-step strategy to obtain a high-resolution maneuvering target image with highly focused responses. Pseudo Wigner–Ville distribution is first employed to access and compensate for the bulk of the high-order phase. Then, to further enhance the target image quality, the SAR-GMTIm problem is solved by sparse Bayesian learning (SBL), where an accurate phase autofocusing is incorporated for the compensation of the residual high-order phase. A novel time-frequency representation, known as Lv's distribution, is adopted to determine the parametric dictionary used in the SBL processing. To accommodate the possible multiple-target imaging scenario, the intended SAR-GMTIm algorithm is developed in a coarse-to-fine compensation procedure. Finally, both simulated data and real Gotcha data are applied to validate the effectiveness and superiority of the proposed SAR imaging algorithm for ground maneuvering targets.

A Dual-Modality System for Both Multi-Color Ultrasound-Switchable Fluorescence and Ultrasound Imaging.

Simultaneous imaging of multiple targets (SIMT) in opaque biological tissues is an important goal for molecular imaging in the future. Multi-color fluorescence imaging in deep tissues is a promising technology to reach this goal. In this work, we developed a dual-modality imaging system by combining our recently developed ultrasound-switchable fluorescence (USF) imaging technology with the conventional ultrasound (US) B-mode imaging. This dual-modality system can simultaneously image tissue acoustic structure information and multi-color fluorophores in centimeter-deep tissue with comparable spatial resolutions. To conduct USF imaging on the same plane (i.e., x-z plane) as US imaging, we adopted two 90°-crossed ultrasound transducers with an overlapped focal region, while the US transducer (the third one) was positioned at the center of these two USF transducers. Thus, the axial resolution of USF is close to the lateral resolution, which allows a point-by-point USF scanning on the same plane as the US imaging. Both multi-color USF and ultrasound imaging of a tissue phantom were demonstrated.

High-Resolution Ultrasound-Switchable Fluorescence Imaging in Centimeter-Deep Tissue Phantoms with High Signal-To-Noise Ratio and High Sensitivity via Novel Contrast Agents.

For many years, investigators have sought after high-resolution fluorescence imaging in centimeter-deep tissue because many interesting in vivo phenomena—such as the presence of immune system cells, tumor angiogenesis, and metastasis—may be located deep in tissue. Previously, we developed a new imaging technique to achieve high spatial resolution in sub-centimeter deep tissue phantoms named continuous-wave ultrasound-switchable fluorescence (CW-USF). The principle is to use a focused ultrasound wave to externally and locally switch on and off the fluorophore emission from a small volume (close to ultrasound focal volume). By making improvements in three aspects of this technique: excellent near-infrared USF contrast agents, a sensitive frequency-domain USF imaging system, and an effective signal processing algorithm, for the first time this study has achieved high spatial resolution (~ 900 μm) in 3-centimeter-deep tissue phantoms with high signal-to-noise ratio (SNR) and high sensitivity (3.4 picomoles of fluorophore in a volume of 68 nanoliters can be detected). We have achieved these results in both tissue-mimic phantoms and porcine muscle tissues. We have also demonstrated multi-color USF to image and distinguish two fluorophores with different wavelengths, which might be very useful for simultaneously imaging of multiple targets and observing their interactions in the future. This work has opened the door for future studies of high-resolution centimeter-deep tissue fluorescence imaging.

UWB Radar Imaging of Multiple Targets through Multi-Layer Walls

In the practical application of Ultra Wideband (UWB) through-wall radar imaging, the walls that are generally encountered consist of multiple layers, and there may be multiple targets, which presents challenges for the imaging algorithm. In this study, both the backprojection (BP) algorithm, which is a time-domain algorithm, and the phase-shift migration (PSM) algorithm, which is a frequency-domain algorithm, are used to locate two targets behind a two-layer wall. The image quality, the azimuth and range resolutions, the image entropy and the processing time of the two algorithms are compared. The simulation results demonstrate that the BP algorithm produces better image quality and that the PSM algorithm is faster. The effect of the sampling interval on the image entropy and processing time is discussed. In addition, the feasibility and the validity of the two algorithms are tested under noisy conditions. The results show that the two algorithms are robust, regardless of the presence of noise.

Wideband MIMO Radar Waveform Design for Multiple Target Imaging

This paper focuses on the design of wideband multiple-input multiple-output (MIMO) radar waveform and its application to multi-target imaging. In a multiple targets scenario, the imaging performances are affected by the transmit beampattern and the power spectral density (PSD) illuminating on targets. Taking into account these factors, we have established an unconstrained optimization model based on jointly minimizing the matching errors of the beampattern and PSDs. To solve the large-scale optimization problem, a conjugate gradient-based wideband MIMO waveform design method is proposed. For the purpose of improving the computational efficiency, the Chirp-Z transform is employed in evaluating the discrete frequency spectrum. Numerical results show that the proposed method can approximately realize the desired multibeam and PSDs. By utilizing a sparse representation method, the designed waveform with randomly distributed PSDs can be applied to multi-target imaging.

Multiplexed fluorescence tomography with spectral and temporal data: demixing with intrinsic regularization

We consider the joint use of spectral and temporal data for multiplexed fluorescence molecular tomography to enable high-throughput imaging of multiple fluorescent targets in bulk tissue. This is a challenging problem due to the narrow near-infrared diagnostic window and relatively broad emission spectra of common fluorophores, and the distortion ("redshift") that the fluorophore signals undergo as they propagate through tissue. We show through a Cramér-Rao lower bound analysis that demixing with spectral-temporal data could result in an order of magnitude improvement in performance over either modality alone. To cope with the resulting large data set, we propose a novel two-stage algorithm that decouples the demixing and tomographic reconstruction operations. In this work we concentrate on the demixing stage. We introduce an approach which incorporates ideas from sparse subspace clustering and compressed sensing and does not require a regularization parameter. We report on simulations in which we simultaneously demixed four fluorophores with closely overlapping spectral and temporal profiles in a 25 mm diameter cross-sectional area with a root-mean-square error of less than 3% per fluorophore, as well as on studies of sensitivity of the method to model mismatch.

Sequential superresolution imaging of multiple targets using a single fluorophore.

Fluorescence superresolution (SR) microscopy, or fluorescence nanoscopy, provides nanometer scale detail of cellular structures and allows for imaging of biological processes at the molecular level. Specific SR imaging methods, such as localization-based imaging, rely on stochastic transitions between on (fluorescent) and off (dark) states of fluorophores. Imaging multiple cellular structures using multi-color imaging is complicated and limited by the differing properties of various organic dyes including their fluorescent state duty cycle, photons per switching event, number of fluorescent cycles before irreversible photobleaching, and overall sensitivity to buffer conditions. In addition, multiple color imaging requires consideration of multiple optical paths or chromatic aberration that can lead to differential aberrations that are important at the nanometer scale. Here, we report a method for sequential labeling and imaging that allows for SR imaging of multiple targets using a single fluorophore with negligible cross-talk between images. Using brightfield image correlation to register and overlay multiple image acquisitions with ~10 nm overlay precision in the x-y imaging plane, we have exploited the optimal properties of AlexaFluor647 for dSTORM to image four distinct cellular proteins. We also visualize the changes in co-localization of the epidermal growth factor (EGF) receptor and clathrin upon EGF addition that are consistent with clathrin-mediated endocytosis. These results are the first to demonstrate sequential SR (s-SR) imaging using direct stochastic reconstruction microscopy (dSTORM), and this method for sequential imaging can be applied to any superresolution technique.

Accurate ultrasound imaging based on range point migration method for the depiction of fetal surface.

The purpose of this study is to evaluate the performance of a modified range point migration (RPM) method using a semi-broad transmit beam for fetal surface imaging. The conventional RPM method depicts accurate images of target surfaces by estimating the reflection point on a target surface from the path length of plural transmit-and-receive element combinations. However, the conventional RPM method depicts false images when echoes from different targets are received simultaneously. For the elimination of false images in the employment of the RPM method, we propose a modified RPM method with a semi-broad transmit beam to decrease the number of targets in each measurement region. The modified RPM method depicted two acrylic cylinders of 2 cm in diameter with a root-mean-square error (RMSE) of 0.062 mm, where the RMSE of the migration method was 0.145 mm. The modified RPM method also succeeded in depicting a 7-month fetal phantom with a RMSE of 0.058 mm relative to a 3D image acquired using optical measurement. This study shows the potential of the modified RPM method in achieving accurate surface imaging of multiple targets using a semi-broad beam, indicating that the method is suitable for fetal surface imaging.