Matrix Reconstruction Research Articles

System Matrix Reconstruction Algorithm for Thermoacoustic Imaging With Magnetic Nanoparticles Based on Acoustic Reciprocity Theorem.

According to the acoustic reciprocity theorem (ART), we propose a system matrix reconstruction algorithm of thermoacoustic imaging for magnetic nanoparticles (MNPs) by a single-pulse magnetic field. In both cases of inhomogeneous and homogeneous acoustic velocity, we respectively derive the linear equation between the sound pressure detection value and the distribution of MNPs. The image reconstruction problem is converted to an inverse matrix solution by using the truncated singular value decomposition (TSVD) method. In forward problem, the calculated forward results are consistent with the simulated thermoacoustic signal signals. In inverse problem, we build the two-dimensional breast cancer model. The TSVD method based on the ART faithfully reflects the distribution of abnormal tissue labeled by the MNPs. In the experiment, the biological sample injected with the MNPs is used as the imaging target. The reconstructed image well reflects the cross-sectional images of the MNPs area. The TSVD method based on the ART takes into account energy attenuation and inhomogeneous acoustic velocity, and use a non-focused broadband ultrasonic transducer as the receiver to obtain a larger imaging field-of-view (FOV). By comparing the image metrics, we prove that the algorithm is superior to the traditional time reversal method. The TSVD method based on the ART can better suppress noise, which is expected to reduce the cost by reducing the number of detectors. It is of great significance for future clinical applications.

Optimization of chest CT protocols based on pixel image matrix, kernels and iterative reconstruction levels – A phantom study

IntroductionThis study investigated the impact of high matrix image reconstruction in combination with different reconstruction kernels and levels of iterative reconstructions on image quality in chest CT. MethodsAn anthropomorphic chest phantom (Kyoto Kagaku Co., Ltd., Kyoto, Japan), and a Catphan® 600 (The Phantom Laboratory, Greenwich, NY, USA) phantom were scanned using a dual source scanner. Standard institutional protocol with 512 × 512 matrix was used as a reference. Reconstructions were performed for 768 × 768 and 1024 × 1024 matrices and all possible combinations of three different kernels and five levels of iterative reconstructions were included. Signal difference to noise ratio (SdNR) and line pairs per cm (lp/cm) were manually measured. A Linear regression model was applied for objective image analysis (SdNR) and inter–and intra-reader agreement was given as Cohen's kappa for the visual image assessment. ResultsMatrix size did not have a significant impact on SdNR (p = 0.595). Kernel (p = 0.014) and ADMIRE level (p = 0.001) had a statistically significant impact on SdNR. The spatial resolution ranged from 7 lp/cm to 9 lp/cm. The highest spatial resolution was achieved using kernel Br64 and ADMIRE 1, 2 and 3 in both 768- and 1024-matrices, and with Br59 with ADMIRE 2 and 4 and 768-matrix, all visualizing 9 lp/cm. Both readers scored kernel Br59 highest, and the scoring increased with increasing levels of Iterative Reconstruction. ConclusionMatrix size did not influence image quality, however, the choice of kernel and degree of IR had an impact on objective and visual image quality in 768 - and 1024-matrices, suggesting that increased degree of IR may improve diagnostic image quality in chest CT. Implications for practiceImage quality in CT of the lung may be improved by increasing the level of IR.

Automatic Hyperparameter Tuning in Sparse Matrix Factorization

We study the problem of hyperparameter tuning in sparse matrix factorization under a Bayesian framework. In prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by a variational Bayes method under several approximations. Based on this solution, we propose a novel numerical method of hyperparameter tuning by evaluating the zero point of the normalization factor in a sparse matrix prior. We also verify that our method shows excellent performance for ground-truth sparse matrix reconstruction by comparing it with the widely used algorithm of sparse principal component analysis.

A Decade of Nipple-Sparing Mastectomy: Lessons Learned in 3035 Immediate Implant-Based Breast Reconstructions.

Nipple-sparing mastectomy is commonly performed for breast cancer treatment or prevention. The authors present one of the largest breast reconstruction series in the literature. A single-institution retrospective review was conducted from 2007 to 2019. The authors' query identified 3035 implant-based breast reconstructions after nipple-sparing mastectomy, including 2043 direct-to-implant and 992 tissue expander-to-implant reconstructions. The overall major complication rate was 9.15%, and the nipple necrosis rate was 1.20%. Therapeutic mastectomy was associated with higher overall complications and explantations compared with prophylactic mastectomy ( P < 0.01). In comparisons of unilateral and bilateral procedures, bilateral mastectomy had an increased risk for complications (OR, 1.46; 95% CI, 0.997 to 2.145; P = 0.05). Tissue-expander reconstructions had higher rates of nipple necrosis (1.9% versus 0.88%; P = 0.015), infection (4.2% versus 2.8%; P = 0.04), and explantation (5.1% versus 3.5%; P = 0.04) compared with direct-to-implant reconstruction. When assessing plane of reconstruction, the authors found similar rates of complications between subpectoral dual-plane and prepectoral reconstruction. There was no difference in complications between reconstruction with acellular dermal matrix or mesh compared with total or partial muscle coverage without acellular dermal matrix/mesh (OR, 0.749; 95% CI, 0.404 to 1.391; P = 0.361). Multivariable regression analysis revealed preoperative radiotherapy (OR, 2.465; 95% CI, 1.579 to 3.848; P < 0.001), smoking (OR, 2.53; 95% CI, 1.581 to 4.054; P < 0.001), and a periareolar incision (OR, 3.657; 95% CI, 2.276 to 5.875; P < 0.001) to be the strongest predictors of complications and nipple necrosis ( P < 0.05). Nipple-sparing mastectomy and immediate breast reconstruction has a low rate of complications. In this series, radiation therapy, smoking, and incision choice predicted overall complications and nipple necrosis, whereas direct-to-implant reconstruction and acellular dermal matrix or mesh did not increase risk. Therapeutic, III.

Deterministic Construction of Compressed Sensing Measurement Matrix with Arbitrary Sizes via QC-LDPC and Arithmetic Sequence Sets

It is of great significance to construct deterministic measurement matrices with good practical characteristics in Compressed Sensing (CS), including good reconstruction performance, low memory cost and low computing resources. Low-density-parity check (LDPC) codes and CS codes can be closely related. This paper presents a method of constructing compressed sensing measurement matrices based on quasi-cyclic (QC) LDPC codes and arithmetic sequence sets. The cyclic shift factor in each submatrix of QC-LDPC is determined by arithmetic sequence sets. Compared with random matrices, the proposed method has great advantages because it is generated based on a cyclic shift matrix, which requires less storage memory and lower computing resources. Because the restricted isometric property (RIP) is difficult to verify, mutual coherence and girth are used as computationally tractable indicators to evaluate the measurement matrix reconstruction performance. Compared with several typical matrices, the proposed measurement matrix has the minimum mutual coherence and superior reconstruction capability of CS signal according to one-dimensional (1D) signals and two-dimensional (2D) image simulation results. When the sampling rate is 0.2, the maximum (minimum) gain of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) is up to 2.89 dB (0.33 dB) and 0.031 (0.016) while using 10 test images. Meanwhile, the reconstruction time is reduced by nearly half.

Fully on-chip photonic turnkey quantum source for entangled qubit/qudit state generation

Integrated photonics has recently become a leading platform for the realization and processing of optical entangled quantum states in compact, robust and scalable chip formats, with applications in long-distance quantum-secured communication, quantum-accelerated information processing and nonclassical metrology. However, the quantum light sources developed so far have relied on external bulky excitation lasers, making them impractical prototype devices that are not reproducible, hindering their scalability and transfer out of the laboratory into real-world applications. Here we demonstrate a fully integrated quantum light source that overcomes these challenges through the integration of a laser cavity, a highly efficient tunable noise suppression filter (>55 dB) exploiting the Vernier effect, and a nonlinear microring for entangled photon-pair generation through spontaneous four-wave mixing. The hybrid quantum source employs an electrically pumped InP gain section and a Si3N4 low-loss microring filter system, and demonstrates high performance parameters, that is, pair emission over four resonant modes in the telecom band (bandwidth of ~1 THz) and a remarkable pair detection rate of ~620 Hz at a high coincidence-to-accidental ratio of ~80. The source directly creates high-dimensional frequency-bin entangled quantum states (qubits/qudits), as verified by quantum interference measurements with visibilities up to 96% (violating Bell’s inequality) and by density matrix reconstruction through state tomography, showing fidelities of up to 99%. Our approach, leveraging a hybrid photonic platform, enables scalable, commercially viable, low-cost, compact, lightweight and field-deployable entangled quantum sources, quintessential for practical, out-of-laboratory applications such as in quantum processors and quantum satellite communications systems.

Reciprocity evaluation based adaptive CCM reconstruction in FDD multi-antenna systems

In view of the difficulty of obtaining downlink channel state information, partial reciprocity based channel covariance matrix (CCM) reconstruction has attracted a lot of attention in frequency division duplex (FDD) multi-antenna systems. Taking both the impact of CCM reconstruction on system performance and design complexity, we investigate an adaptive CCM reconstruction in this paper. Specifically, to effectively evaluate the validity of the reciprocity, we firstly analyze the characteristics of the partial reciprocity and define a reciprocity evaluation criterion. Then, we propose a partial antenna based angular power spectrum (APS) estimating algorithm to further reduce the complexity of the CCM reconstruction. Finally, simulation results demonstrate the superiority of our proposed schemes.

EZH2 controls epicardial cell migration during heart development.

Enhancer of zeste homolog 2 (EZH2) is an important transcriptional regulator in development that catalyzes H3K27me3. The role of EZH2 in epicardial development is still unknown. In this study, we show that EZH2 is expressed in epicardial cells during both human and mouse heart development. Ezh2 epicardial deletion resulted in impaired epicardial cell migration, myocardial hypoplasia, and defective coronary plexus development, leading to embryonic lethality. By using RNA sequencing, we identified that EZH2 controls the transcription of tissue inhibitor of metalloproteinase 3 (TIMP3) in epicardial cells during heart development. Loss-of-function studies revealed that EZH2 promotes epicardial cell migration by suppressing TIMP3 expression. We also found that epicardial Ezh2 deficiency-induced TIMP3 up-regulation leads to extracellular matrix reconstruction in the embryonic myocardium by mass spectrometry. In conclusion, our results demonstrate that EZH2 is required for epicardial cell migration because it blocks Timp3 transcription, which is vital for heart development. Our study provides new insight into the function of EZH2 in cell migration and epicardial development.

Synthesis, structure and electromagnetic properties of FeCoCu/C nanocomposites

FeCoCu ternary nanoparticles distributed and stabilized in the carbon matrix of FeCoCu/C metal-carbon nanocomposites have been synthesized using controlled IR pyrolysis of precursors consisting of the “polymer / iron acetylacetate / cobalt and copper acetates” type system obtained by joint dissolution of components followed by solvent removal. The effect of the synthesis temperature on the structure, composition and electromagnetic properties of the nanocomposites has been studied. By XRD was shown that the formation of the FeCoCu ternary nanoparticles occurs due to the interaction of Fe3С with the nanoparticles of the CoCu solid solution. An increase in the synthesis temperature leads to an increase in the size of the metal nanoparticles due to their agglomeration and coalescence as a result of matrix reconstruction. Furthermore, ternary alloy nanoparticles having a variable composition may form depending on the synthesis temperature and the content ratio of the metals. Raman spectroscopy has shown that the crystallinity of the carbon matrix of the nanocomposites increases with the synthesis temperature. The frequency responses of the relative permittivity and permeability of the nanocomposites have been studied at 3–13 GHz. It has been shown that a change in the content ratio of the metals noticeably increases both the dielectric and the magnetic losses. The former loss is caused by the formation of a complex nanostructure of the nanocomposite carbon matrix while the latter one originates from an increase in the size of the nanoparticles and a shift of the natural ferromagnetic resonance frequency to the low-frequency region. The reflection loss has been calculated using a standard method from the experimental data on the frequency responses of the relative permittivity and permeability. It has been shown that the frequency range and the absorption of electromagnetic waves (from –20 to –52 dB) can be controlled by varying the content ratio of the metals in the precursor. The nanocomposites obtained as a result of the experiment deliver better results in comparison with FeCo/C nanocomposites synthesized under similar conditions.

Approach for Identifying Cartesian Stiffness of a 5-Degree-of-Freedom Hybrid Robot for Machining

Abstract This article presents a systematic approach for identifying the Cartesian stiffness of a 5-degree-of-freedom (DOF) hybrid robot for machining that includes a parallel mechanism and an A/C wrist. The novelty of this approach is that the elasticities of both links and joints in the parallel mechanism are integrated into the compliance (inverse of stiffness) parameters at the limb level. By identifying the compliance parameters at the limb level rather than at the joint/link level, the number of parameters to be identified is significantly reduced and the complexity of the identification problem is decreased. Based on screw theory, the Cartesian stiffness model of this hybrid robot is established first. Then, by reconstructing this stiffness model, a linear regression model suitable for estimating the compliance parameter is derived. In addition, a two-step systematic procedure for parameter estimation is introduced, including the reconstruction of the design matrix and robust ridge estimation. Finally, both computer simulations and experiments are carried out to demonstrate the validity of the proposed approach. The simulation results show that the predictive deviations of the end-effector deflections identified by ridge estimation are less than those estimated by linear least squares, confirming its greater robustness. The experimental results indicate that the developed method has potential in industrial settings.

Speckle attenuation for optical coherence tomography images using the generalized low rank approximations of matrices.

A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.

High resolution adaptive estimator of sinusoidal based on covariance matrix reconstruction

Exponentially decaying sinusoidal signal is often observed in engineering and research fields. In some practical applications, the frequencies of multiple signals are close to each other. It would face a challenging task to resolve these crowded signals processed by discrete Fourier transformation, especially in the case of the lack of sampling time and the fast-decaying signals. The Localised Capon estimator (LoCapE) has demonstrated its advantage in improving the resolution of exponentially decaying sinusoidal signals. However, the amplitude and the frequency of each component extracted by the LoCapE are not accurate due to the effect from interference components. Here, we improve the LoCapE method by reconstructing the interference-plus-noise covariance matrix instead of the signal covariance matrix. Experimental results on simulated and real exponentially decaying sinusoidal data show that the proposed method is more accurate in extracting parameters at the lack of sampling points.

A model-based image reconstruction algorithm for near real-time transcranial photoacoustic imaging

Photoacoustic imaging is a promising hybrid imaging modality that can visualize both microscopic and macroscopic structures and oxygenation changes in brain function. Phase aberration caused by the skull is a major barrier for high-quality photoacoustic imaging of the brain. Time-reversal methods have been used to address this issue, though they rely on solving the full-wave equation, and is therefore computationally heavy and slow. Herein, a near real-time model-based image reconstruction algorithm is proposed. The acoustic forward model is mathematically described as a model matrix and image reconstructions are performed by employing the pseudoinverse of the model matrix and calculating the initial pressure distribution. The pseudoinverse matrix only needs to be computed once for the same experimental setup and acoustic medium and is obtained offline prior to imaging. The proposed algorithm shows equivalent image quality but considerably faster reconstruction time (&amp;gt;40 times faster), compared to the time-reversal method and therefore could potentially enable near real-time photoacoustic imaging.

Mainlobe Interference Suppression for Radar Network via RPCA-Based Covariance Matrix Reconstruction

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This article proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multiradar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time-domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vector (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.

Chitosan-Based Biomaterials for Tissue Regeneration.

Chitosan is a chitin-derived biopolymer that has shown great potential for tissue regeneration and controlled drug delivery. It has numerous qualities that make it attractive for biomedical applications such as biocompatibility, low toxicity, broad-spectrum antimicrobial activity, and many others. Importantly, chitosan can be fabricated into a variety of structures including nanoparticles, scaffolds, hydrogels, and membranes, which can be tailored to deliver a desirable outcome. Composite chitosan-based biomaterials have been demonstrated to stimulate in vivo regeneration and the repair of various tissues and organs, including but not limited to, bone, cartilage, dental, skin, nerve, cardiac, and other tissues. Specifically, de novo tissue formation, resident stem cell differentiation, and extracellular matrix reconstruction were observed in multiple preclinical models of different tissue injuries upon treatment with chitosan-based formulations. Moreover, chitosan structures have been proven to be efficient carriers for medications, genes, and bioactive compounds since they can maintain the sustained release of these therapeutics. In this review, we discuss the most recently published applications of chitosan-based biomaterials for different tissue and organ regeneration as well as the delivery of various therapeutics.

Robust adaptive beamforming via covariance matrix reconstruction with diagonal loading on interference sources covariance matrix

The majority of the existing adaptive beamforming methods utilize the interference-plus-noise covariance matrix (INCM) to prevent the signal of interest (SOI) from being suppressed. However, the removal of the SOI component will lead to a decrease in the performance at low input signal-to-noise ratio (SNR). In this paper, a novel interference sources covariance matrix (ISCM) diagonal loading method is proposed to improve the robustness of the beamformer. First of all, we employ the peaks of the Capon spectrum to calculate the corresponding nominal steering vectors (SVs), and then accurate SVs of the SOI and interferences are obtained by subspace projection. Instead of utilizing the orthogonality, the proposed method preserves the non-orthogonality between SVs to estimate an accurate signal-plus-interference sources covariance matrix (SISCM), and then obtains a reconstructed array covariance matrix (RACM). Furthermore, we conduct diagonal loading on ISCM and construct a modified array covariance matrix (MACM) to keep the interference-to-signal ratio (ISR) after loading at a proper large value. Simulation results demonstrate the robustness of the proposed method, and indicate that the proposed method not only has the low computational complexity but also performs well at both low and high input SNR.

A New Spectral Compression Method based on the Minimization of the Color Difference and Root Mean Square Error

This paper describes two new interim connection spaces (ICSs), P-2OC and P-3OC, for spectral image compression and reconstruction. For this type of ICS, the weighting table or compression matrix H is modelled as a variable. The associated reconstruction matrix N is chosen as the Weiner estimation matrix. The objective function f(H) is the combination of the averages of the CIEDE2000 color difference (&#x394;E00) and the root mean square error (RMSE) between the original and reconstructed reflectance values. Hence, the compression matrix H is determined by solving the nonlinear minimization problem based on the Munsell training dataset. The proposed ICSs, P-2OC and P-3OC, were tested and compared, respectively, with ICS-2SI and ICS-3SI developed by Zhang et&#xA0;al.&#xA0;(JOSAA, Vol. 29, pp. 1027&#x2013;1034) in 2012 using the NCS dataset, and 2 spectral images. Performance tests showed that the proposed P-2OC and P-3OC ICSs are better than the ICS-2SI and ICS-3SI ICSs, respectively, in terms of RMSE, goodness of fit coefficient (GFC), and &#x394;E00 under CIE illuminants D65, A, C and F11. Therefore, it is expected that the P-2OC and P-3OC ICSs can find applications in spectral image compression and cross-media reproduction.

Intracavity adaptive correction of an unstable standing-wave resonator based on reconstruction matrix optimization.

Due to the existence of the expanding beam portion in the positive branch confocal unstable resonator, the laser passes through the intracavity deformable mirror (DM) twice with different apertures, which makes it complicated to calculate the required compensation surface of the DM. In this paper, an adaptive compensation method for intracavity aberrations based on reconstruction matrix optimization is proposed to solve this problem. A collimated probe laser of 976 nm and a Shack-Hartmann wavefront sensor (SHWFS) are introduced from the outside of the resonator to detect intracavity aberrations. The feasibility and effectiveness of this method are verified by numerical simulations and the passive resonator testbed system. By adopting the optimized reconstruction matrix, the control voltages of the intracavity DM can be directly calculated from the SHWFS slopes. After compensation by the intracavity DM, the beam quality β of the annular beam coupled out from the scraper is improved from 6.2 times diffraction limit to 1.6 times diffraction limit.

Homodyne nonclassical area as a nonclassicality indicator

We propose a legitimate and easily computable nonclassicality indicator for the states of the electromagnetic field based on the standard deviation in the measurement of the homodyne rotated quadrature operator. The proposed nonclassicality indicator is the nonclassical area projected by the optical tomogram of the quantum state of light on the optical tomographic plane. If the nonclassical area projected by the optical tomogram of a quantum state is greater than zero, the state is nonclassical, and the area is zero for the pure classical state. It is also noted that the nonclassical area of a quantum state increases with an increase in the strength of nonclassicality-inducing operations on the state, such as squeezing, photon addition, etc. We have tested the validity of the nonclassical area measure by calculating the same for certain well-known nonclassical states, and found that essential features of the nonclassicality shown by the states are captured in the nonclassical area. We also show that the nonclassical area is robust against environment-induced decoherence of the states. The nonclassical area projected by the optical tomogram of a quantum state of light is experimentally tractable using the balanced homodyne detection of the quadrature operator of the field, avoiding the reconstruction of the density matrix or the quasiprobability distribution of the state.

Reconstruction of enterprise debt networks based on compressed sensing

This study aims at the problem of reconstruction the unknown links in debt networks among enterprises. We use the topological matrix of the enterprise debt network as the object of reconstruction and use the time series data of accounts receivable and payable as input and output information in the debt network to establish an underdetermined linear system about the topological matrix of the debt network. We establish an iteratively reweighted least-squares algorithm, which is an algorithm in compressed sensing. This algorithm uses reweighted ell _2-minimization to approximate ell _1-norm of the target vectors. We solve the ell _1-minimization problem of the underdetermined linear system using the iteratively reweighted least-squares algorithm and obtain the reconstructed topological matrix of the debt network. Simulation experiments show that the topology matrix reconstruction method of enterprise debt networks based on compressed sensing can reconstruct over 70% of the unknown network links, and the error is controlled within 2%.