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Chong Chen

Publications and source records attributed to Chong Chen.

At least 163 records · Page 9Linked to original sources
arXiv2020

Automatic image-based respiratory signal extraction in real-time CMR

Purpose: To develop a fully automatic method for extraction and directionality determination of respiratory signal in free-breathing, real-time (RT) cardiac MRI. Methods: The respiratory signal is extracted by a principal component analysis method from RT cine images. Then, a two-step procedure is used to determine the directionality (sign) of the respiratory signal. First, the signal polarity of all slices is made consistent with a reference slice. Second, a global sign correction is performed by maximizing the correlation of the respiratory signal with the zeroth-moment center curve. The proposed method is evaluated in multi-slice RT cine from eleven volunteers and two patients. The motion in a manually selected region-of-interest is used as reference. Results: The extracted respiratory signal using the proposed method exhibits high, positive correlation with the reference in all cases and is more robust compared to a recently proposed method. To demonstrate its clinical utility, the method is used to identify heartbeats from inspiratory and expiratory phases and, in turn, to account for respiration-induced changes in the cardiac output. Conclusions: The proposed method enables fully automatic extraction and directionality determinations of respiratory signal from RT cardiac cine images, allowing accurate cardiac function quantification.

eess.IV↗
arXiv2020

Spatiotemporal Imaging with Diffeomorphic Optimal Transportation

We propose a variational model with diffeomorphic optimal transportation for joint image reconstruction and motion estimation. The proposed model is a production of assembling the Wasserstein distance with the Benamou--Brenier formula in optimal transportation and the flow of diffeomorphisms involved in large deformation diffeomorphic metric mapping, which is suitable for the scenario of spatiotemporal imaging with large diffeomorphic and mass-preserving deformations. Specifically, we first use the Benamou--Brenier formula to characterize the optimal transport cost among the flow of mass-preserving images, and restrict the velocity field into the admissible Hilbert space to guarantee the generated deformation flow being diffeomorphic. We then gain the ODE-constrained equivalent formulation for Benamou--Brenier formula. We finally obtain the proposed model with ODE constraint following the framework that presented in our previous work. We further get the equivalent PDE-constrained optimal control formulation. The proposed model is compared against several existing alternatives theoretically. The alternating minimization algorithm is presented for solving the time-discretized version of the proposed model with ODE constraint. Several important issues on the proposed model and associated algorithms are also discussed. Particularly, we present several potential models based on the proposed diffeomorphic optimal transportation. Under appropriate conditions, the proposed algorithm also provides a new scheme to solve the models using quadratic Wasserstein distance. The performance is finally evaluated by several numerical experiments in space-time tomography, where the data is measured from the concerned sequential images with sparse views and/or various noise levels.

math.OC↗
arXiv2020

A Novel Neural Network Training Framework with Data Assimilation

In recent years, the prosperity of deep learning has revolutionized the Artificial Neural Networks. However, the dependence of gradients and the offline training mechanism in the learning algorithms prevents the ANN for further improvement. In this study, a gradient-free training framework based on data assimilation is proposed to avoid the calculation of gradients. In data assimilation algorithms, the error covariance between the forecasts and observations is used to optimize the parameters. Feedforward Neural Networks (FNNs) are trained by gradient decent, data assimilation algorithms (Ensemble Kalman Filter (EnKF) and Ensemble Smoother with Multiple Data Assimilation (ESMDA)), respectively. ESMDA trains FNN with pre-defined iterations by updating the parameters using all the available observations which can be regard as offline learning. EnKF optimize FNN when new observation available by updating parameters which can be regard as online learning. Two synthetic cases with the regression of a Sine Function and a Mexican Hat function are assumed to validate the effectiveness of the proposed framework. The Root Mean Square Error (RMSE) and coefficient of determination (R2) are used as criteria to assess the performance of different methods. The results show that the proposed training framework performed better than the gradient decent method. The proposed framework provides alternatives for online/offline training the existing ANNs (e.g., Convolutional Neural Networks, Recurrent Neural Networks) without the dependence of gradients.

cs.LG↗
arXiv2020

Classical-noise-free sensing based on quantum correlation measurement

Quantum sensing, using quantum properties of sensors, can enhance resolution, precision, and sensitivity of imaging, spectroscopy, and detection. An intriguing question is: Can the quantum nature (quantumness) of sensors and targets be exploited to enable schemes that are not possible for classical probes or classical targets? Here we show that measurement of the quantum correlations of a quantum target indeed allows for sensing schemes that have no classical counterparts. As a concrete example, in case where the second-order classical correlation of a quantum target could be totally concealed by non-stationary classical noise, the higher-order quantum correlations can single out a quantum target from the classical noise background, regardless of the spectrum, statistics, or intensity of the noise. Hence a classical-noise-free sensing scheme is proposed. This finding suggests that the quantumness of sensors and targets is still to be explored to realize the full potential of quantum sensing. New opportunities include sensitivity beyond classical approaches, non-classical correlations as a new approach to quantum many-body physics, loophole-free tests of the quantum foundation, et cetera.

quant-ph↗
arXiv2020

Deep Robust Clustering by Contrastive Learning

Recently, many unsupervised deep learning methods have been proposed to learn clustering with unlabelled data. By introducing data augmentation, most of the latest methods look into deep clustering from the perspective that the original image and its transformation should share similar semantic clustering assignment. However, the representation features could be quite different even they are assigned to the same cluster since softmax function is only sensitive to the maximum value. This may result in high intra-class diversities in the representation feature space, which will lead to unstable local optimal and thus harm the clustering performance. To address this drawback, we proposed Deep Robust Clustering (DRC). Different from existing methods, DRC looks into deep clustering from two perspectives of both semantic clustering assignment and representation feature, which can increase inter-class diversities and decrease intra-class diversities simultaneously. Furthermore, we summarized a general framework that can turn any maximizing mutual information into minimizing contrastive loss by investigating the internal relationship between mutual information and contrastive learning. And we successfully applied it in DRC to learn invariant features and robust clusters. Extensive experiments on six widely-adopted deep clustering benchmarks demonstrate the superiority of DRC in both stability and accuracy. e.g., attaining 71.6% mean accuracy on CIFAR-10, which is 7.1% higher than state-of-the-art results.

cs.CV↗
arXiv2020

A Fast and Adaptive Algorithm to Compute the X-ray Transform

We propose a new algorithm to compute the X-ray transform of an image represented by unit (pixel/voxel) basis functions. The fundamental issue is equivalently calculating the intersection lengths of the ray with associated units. For any given ray, we first derive the sufficient and necessary condition for non-vanishing intersectability. By this condition, we then distinguish the units that produce valid intersections with the ray. Only for those units rather than all the individuals, we calculate the intersection lengths by the obtained analytic formula. The proposed algorithm is adapted to 2D/3D parallel beam and 2D fan beam. Particularly, we derive the transformation formulas and generalize the algorithm to 3D circular and helical cone beams. Moreover, we discuss the intrinsic ambiguities of the problem itself, and present a solution. The algorithm not only possesses the adaptability with regard to the center position, scale and size of the image, but also is suited to parallelize with optimality. The comparison study demonstrates the proposed algorithm is fast, more complete, and is more flexible with respect to different scanning geometries and different basis functions. Finally, we validate the correctness of the algorithm by the aforementioned scanning geometries.

math.NA↗
arXiv2020

OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging

Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-tissue contrast without the use of ionizing radiation. Physiological motions and limited speed of MRI data acquisition necessitate development of accelerated methods, which typically rely on undersampling. Recovering diagnostic quality CMR images from highly undersampled data has been an active area of research. Recently, several data acquisition and processing methods have been proposed to accelerate CMR. The availability of data to objectively evaluate and compare different reconstruction methods could expedite innovation and promote clinical translation of these methods. In this work, we introduce an open-access dataset, called OCMR, that provides multi-coil k-space data from 53 fully sampled and 212 prospectively undersampled cardiac cine series.

eess.IV↗
arXiv2020

Characteristics and Importance of "ICME-in-Sheath" Phenomenon and Upper Limit for Geomagnetic Storm Activity

As an important source for large geomagnetic storms, an "ICME-in-sheath" is a completely shocked interplanetary coronal mass ejection (ICME) stuck in the sheath between a shock and host ejecta. Typical characteristics are identified from coordinated multi-sets of observations: (1) it is usually short in duration and lasts a few hours at 1 AU; (2) its solar wind parameters, in particular the magnetic field, seem to keep enhanced for a large range of distances; and (3) common ICME signatures are often lost. The host ejecta could be a single ICME or a complex ejecta, being fast enough to drive a shock. These results clarify previous misinterpretations of this phenomenon as a normal part of a sheath region. The "ICME-in-sheath" phenomenon, together with a preconditioning effect, produced an extreme set of the magnetic field, speed and density near 1 AU in the 2012 July 23 case, all around their upper limits at the same time. This is probably the most extreme solar wind driving at 1 AU and enables us to estimate the plausible upper limit for geomagnetic storm activity. With an appropriate modification in the southward field, we suggest that a geomagnetic storm with a minimum $D_{\rm st}$ of about $-2000$ nT could occur in principle. The magnetopause would be compressed to about 3.3 Earth radii from the Earth's center, well inside the geosynchronous orbit.

astro-ph.SR↗
arXiv2020

Automatic Extraction and Sign Determination of Respiratory Signal in Real-time Cardiac Magnetic Resonance imaging

In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected sequentially under free-breathing conditions. A complete heartbeat from each slice is then used for cardiac function quantification. The inter-slice respiratory mismatch can compromise accurate quantification of cardiac function. Methods based on principal components analysis (PCA) have been proposed to extract the respiratory signal from RT cardiac cine, but these methods cannot resolve the inter-slice sign ambiguity of the respiratory signal. In this work, we propose a fully automatic sign correction procedure based on the similarity of neighboring slices and correlation to the center-of-mass curve. The proposed method is evaluated in eleven volunteers, with ten slices per volunteer. The motion in a manually selected region-of-interest (ROI) is used as a reference. The results show that the extracted respiratory signal has a high, positive correlation with the reference in all cases. The qualitative assessment of images also shows that the proposed approach can accurately identify heartbeats, one from each slice, belonging to the same respiratory phase. This approach can improve cardiac function quantification for RT cine without manual intervention.

eess.IV↗
arXiv2019

Three metallic BN polymorphs: 1D multi-threaded conduction in 3D network

In this paper, three novel metallic sp2/sp3 -hybridized Boron Nitride (BN) polymorphs are proposed by first-principles calculations. One of them, named as tP-BN, is predicted based on the evolutionary particle swarm structural search. tP-BN is constructed by two interlocked rings forming a tube-like 3D network. The stability and band structure calculations show tP-BN is metastable and metallic at zero pressure. Calculations for the density of states and electron orbits confirm that the metallicity originates from the sp2 -hybridized B and N atoms, and forming 1D linear conductive channels in the 3D network. According to the relationship between the atomic structure and electronic properties, another two 3D metastable metallic sp2/sp3 -hybridized BN structures are constructed manually. Electronic properties calculations show that both of these structures have 1D conductive channel along different axes. The polymorphs predicted in this study enrich the structures and provide a different picture of the conductive mechanism of BN compounds.

cond-mat.mtrl-sci↗
arXiv2019

Characteristics of a Gradual Filament Eruption and Subsequent CME Propagation in Relation to a Strong Geomagnetic Storm

An unexpected strong geomagnetic storm occurred on 2018 August 26, which was caused by a slow coronal mass ejection (CME) from a gradual eruption of a large quiet-region filament. We investigate the eruption and propagation characteristics of this CME in relation to the strong geomagnetic storm with remote sensing and in situ observations. Coronal magnetic fields around the filament are extrapolated and compared with EUV observations. We determine the propagation direction and tilt angle of the CME flux rope near the Sun using a graduated cylindrical shell (GCS) model and the Sun-to-Earth kinematics of the CME with wide-angle imaging observations from STEREO A. We reconstruct the flux-rope structure using a Grad-Shafranov technique based on the in situ measurements at the Earth and compare it with those from solar observations and the GCS results. Our conclusions are as follows: (1) the eruption of the filament was unusually slow and occurred in the regions with relatively low critical heights of the coronal field decay index; (2) the axis of the CME flux rope rotated in the corona as well as in interplanetary space, which tended to be aligned with the local heliospheric current sheet; (3) the CME was bracketed between slow and fast solar winds, which enhanced the magnetic field inside the CME at 1 AU; (4) the geomagnetic storm was caused by the enhanced magnetic field and a southward orientation of the flux rope at 1 AU from the rotation of the flux rope.

astro-ph.SR↗
arXiv2019

Triggering InAs/GaAs Quantum Dot nucleation and growth rate determination by in-situ modulation of surface energy

Epitaxial InAs/GaAs Quantum Dots (QDs) are widely used as highly efficient and pure sources of single photons and entangled photon-pairs, however reliable wafer-scale growth techniques have proved elusive. Growth of two-dimensional Quantum Well (QW) thin-films can be achieved with atomic precision down to below the de Broglie wavelength of electrons in the material, exposing the quantum particle-in-a-box energy vs. thickness-squared relationship. However, difficulties in controlling the exact moment of nanostructure nucleation obscure this behaviour in epitaxial QD material, preventing a clear understanding of their growth. In this work we demonstrate that QD nucleation can be induced by directly modulating the crystal surface energy without additional materials or equipment. This gains us quantitative measure of the QD growth rate and enables predictive design of QD growth processes. We believe this technique will be crucial to the realisation of uniform arrays of QDs required for scalable quantum devices at the single-photon level.

cond-mat.mtrl-sci↗
arXiv2019

Isogeometric Analysis for Surface PDEs with Extended Loop Subdivision

We investigate the isogeometric analysis for surface PDEs based on the extended Loop subdivision approach. The basis functions consisting of quartic box-splines corresponding to each subdivided control mesh are utilized to represent the geometry exactly, and construct the solution space for dependent variables as well, which is consistent with the concept of isogeometric analysis. The subdivision process is equivalent to the $h$-refinement of NURBS-based isogeometric analysis. The performance of the proposed method is evaluated by solving various surface PDEs, such as surface Laplace-Beltrami harmonic/biharmonic/triharmonic equations, which are defined on different limit surfaces of the extended Loop subdivision for different initial control meshes. Numerical experiments demonstrate that the proposed method has desirable performance in terms of the accuracy, convergence and computational cost for solving the above surface PDEs defined on both open and closed surfaces. The proposed approach is proved to be second-order accuracy in the sense of $L^2$-norm by theoretical and/or numerical results, which is also outperformed over the standard linear finite element by several numerical comparisons.

math.NA↗
arXiv2019

Characterization of arbitrary-order correlations in quantum baths by weak measurement

Correlations of fluctuations are the driving forces behind the dynamics and thermodynamics in quantum many-body systems. For qubits embedded in a quantum bath, the correlations in the bath are the key to understanding and combating decoherence -a critical issue in quantum information technology. However, there is no systematic method for characterizing the many-body correlations in quantum baths beyond the second order or the Gaussian approximation. Here we present a scheme to characterize the correlations in a quantum bath to arbitrary order. The scheme employs weak measurement of the bath via projective measurement of a central system. The bath correlations, including both the "classical" and the "quantum" parts, can be reconstructed from the correlations of the measurement outputs. The possibility of full characterization of many-body correlations in a quantum bath forms the basis for optimizing quantum control against decoherence in realistic environments, for studying the quantum characteristics of baths, and for quantum sensing of correlated clusters in quantum baths.

quant-ph↗
arXiv2018

A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

We propose a new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging, which is investigated along a general framework that we present with shape theory. This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration. For the latter, we generalize the large deformation diffeomorphic metric mapping framework into the sequentially indirect registration setting. The proposed model is compared theoretically against alternative approaches (optical flow based model and diffeomorphic motion models), and we demonstrate that the proposed model has desirable properties in terms of the optimal solution. The theoretical derivations and efficient algorithms are also presented for a time-discretized scenario of the proposed model, which show that the optimal solution of the time-discretized version is consistent with that of the time-continuous one, and most of the computational components is the easy-implemented linearized deformation. The complexity of the algorithm is analyzed as well. This work is concluded by some numerical examples in 2D space + time tomography with very sparse and/or highly noisy data.

math.NA↗
arXiv2018

Sensitivity of parameter estimation near the exceptional point of a non-Hermitian system

The exceptional points of non-Hermitian systems, where $n$ different energy eigenstates merge into an identical one, have many intriguing properties that have no counterparts in Hermitian systems. In particular, the $ε^{1/n}$ dependence of the energy level splitting on a perturbative parameter $ε$ near an $n$-th order exceptional point stimulates the idea of metrology with arbitrarily high sensitivity, since the susceptibility $dε^{1/n}/dε$ diverges at the exceptional point. Here we theoretically study the sensitivity of parameter estimation near the exceptional points, using the exact formalism of quantum Fisher information. The quantum Fisher information formalism allows the highest sensitivity to be determined without specifying a specific measurement approach. We find that the exceptional point bears no dramatic enhancement of the sensitivity. Instead, the coalescence of the eigenstates exactly counteracts the eigenvalue susceptibility divergence and makes the sensitivity a smooth function of the perturbative parameter.

quant-ph↗
arXiv2017

Quantum metrology in local dissipative environments

Quantum metrology allows us to attain a measurement precision that surpasses the classically achievable limit by using quantum characters. The metrology precision is raised from the standard quantum limit (SQL) to the Heisenberg limit (HL) by using entanglement. However, it was reported that the HL returns to the SQL in the presence of local dephasing environments under the long encoding-time condition. We evaluate here the exact impacts of local dissipative environments on quantum metrology, based on the Ramsey interferometer. It is found that the HL is asymptotically recovered under the long encoding-time condition for a finite number of the probe atoms. Our analysis reveals that this is essentially due to the formation of a bound state between each atom and its environment. This provides an avenue for experimentation to implement quantum metrology under practical conditions via engineering of the formation of the system-environment bound state.

quant-ph↗