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Pan Liu

Publications and source records attributed to Pan Liu.

At least 55 records · Page 3Linked to original sources
arXiv2022

Cerebro spinal fluid dynamic in front of cardiac and breathing influence

It is still debated how breathing interacts with the CSF. New Phase contrast MRI sequence based on Echo Planar imaging (EPI-PC) can now produce continuously during minutes a velocity map, more or less every 100 ms. We did not found in the literature quantitative evaluation of the CSF stroke volume change during breathing. The aim of this work is to quantify CSF dynamics change in the aqueduct and in the spinal canal during the breathing and cardiac period using EPI-PC.

eess.SP↗
arXiv2022

Accuracy of Real-Time Echo-Planar Imaging Phase Contrast MRI

Compared with CINE phase contrast MRI (CINE-PC), echo-planar imaging phase contrast (EPI-PC) can achieve realtime quantification of blood flow, with lower SNR. In this study, the pulsating real model of the simulated cerebral vasculature was used to verify the accuracy of EPI-PC. The imaging time of EPI-PC was 62ms/image at 100*60 spatial resolution. The reconstructed EPI-PC flow curve was extracted by homemade post-processing software. After comparison with the CINE-PC flow curve, it was concluded that EPI-PC can provide an average flow with less than 3% error, and its flow curve will be similar to the CINE-PC flow curve in shape.

eess.SP↗
arXiv2022

A tool towards EEG semi-autonomous electrode placement

The paper proposes a novel medical device based on a 9 dof IMU to help health professionals performing more precisely the electrode placement task in EEG exams. The tool precisely tells the operator if the 10-20 electrode placement system is being correctly followed. The manual task is of major importance and time consuming, because all the electrodes must be correctly and very precisely placed in the head of the patient. The gold standard process is manual, and although several medical devices (developed to other types of medical procedures) can be applied to increase the precision of the electrode placement, they are still very expensive. The proposed medical device, based only on the sensors of a 9 dof IMU, and the processing capabilities of a microprocessor, diminish the price of the device. Moreover, the size of the apparatus is also diminished, when compared with infrared vision based systems. The developed system includes a visualization subsystem that visualises the position of the electrodes in a virtual head of the patient, using a specific tool, 3DSlicer, to receive and visualise the 3D pose of the medical device when point to the patient head. Preliminary results showed the validity of the proposed device.

physics.med-ph↗
arXiv2022

Flow 2.0 -a flexible, scalable, cross-platform post-processing software for realtime phase contrast sequences

Flow 2.0 is an end-to-end easy-of-use software that allows us to quickly, robustly and accurately perform a batch process real-time phase contrast data and multivariate analysis of the effect of respiration on cerebral fluids circulation. Synopsis (99/100) Real-time phase contrast sequences (RT-PC) have potential value as a scientific and clinical tool in quantifying the effects of respiration on cerebral circulation. To simplify its complicated post-processing process, we developed Flow 2.0 software, which provides a complete post-processing workflow including converting DICOM data, image segmentation, image processing, data extraction, background field correction, antialiasing filter, signal processing and analysis and a novel time-domain method for quantifying the effect of respiration on the cerebral circulation. This end-to-end software allows us to quickly, robustly and accurately perform batch process RT-PC and multivariate analysis of the effects of respiration on cerebral circulation.

eess.SP↗
arXiv2022

Real-Time Phase Contrast MRI to quantify Cerebral arterial flow change during variations breathing

Cerebral arterial blood flow (CABF) can be investigated in few seconds without any synchronization by Real-Time phase contrast. Significant changes in CABF were found between expiration and inspiration during normal breathing of healthy volunteers. Synopsis (100/100) Real-time phase contrast MRI has been applied to investigate cerebral arterial blood flow (CABF) during normal breathing of healthy volunteers. We developed a novel time-domain analysis method to quantify the effect of normal breathing on several parameters of CABF. We found the existence of a delay between the recorded respiratory signal from the belt sensor and the breathing frequency component present in the reconstructed arterial blood flows. During the expiratory, the mean flow rate of CABF increased by 4.4$\pm$1.7%, stroke volume of CABF increased by 9.8$\pm$3.1% and the duration of the cardiac period of CABF increased by 8.1$\pm$3%.

eess.SP↗
arXiv2022

Real order total variation with applications to the loss functions in learning schemes

Loss function are an essential part in modern data-driven approach, such as bi-level training scheme and machine learnings. In this paper we propose a loss function consisting of a $r$-order (an)-isotropic total variation semi-norms $TV^r$, $r\in \mathbb{R}^+$, defined via the Riemann-Liouville (R-L) fractional derivative. We focus on studying key theoretical properties, such as the lower semi-continuity and compactness with respect to both the function and the order of derivative $r$, of such loss functions.

math.AP↗
arXiv2022

Sensing performance enhancement via asymmetric gain optimization in the atom-light hybrid interferometer

The SU (1,1)-type atom-light hybrid interferometer (SALHI) is a kind of interferometer that is sensitive to both the optical phase and atomic phase. However, the loss has been an unavoidable problem in practical applications and greatly limits the use of interferometers. Visibility is an important parameter to evaluate the sensing performance of interferometers. Here, we experimentally demonstrate the mitigating effect of the loss on visibility of the SALHI via asymmetric gain optimization, where the maximum threshold of loss to visibility close to $100\%$ is increased. Furthermore, we theoretically find that the optimal condition for the largest visibility is the same as that for the enhancement of signal-to-noise ratio (SNR) to the best value in the presence of losses using the intensity detection, indicating that the visibility can act as an experimental operational criterion for SNR improvement in practical applications. Improvement of the interference visibility means achievement of SNR enhancement. Our results provide a significant foundation for practical application of the SALHI in radar and ranging measurements.

quant-ph↗
arXiv2021

The microstructural dependence of ionic transport in bi-continuous nanoporous metal

Ionic transports in nanopores hold the key to unlocking the full potential of bi-continuous nanoporous (NP) metals as advanced electrodes in electrochemical devices. The precise control of the uniform NP metal structures also provides us a unique opportunity to understand how complex structures determine transports at nanoscales. For NP Au from the dealloying of a Ag-Au alloy, we can tune the pore size in the range of 13 nm to 2.4 microns and the porosity between 38% and 69% via isothermal coarsening. For NP Ag from the reduction-induced decomposition of AgCl, we can control additionally its structural hierarchy and pore orientation. We measure the effective ionic conductivities of 1 M NaClO4 through these NP metals as membranes, which range from 7% to 44% of that of a free solution, corresponding to calculated pore tortuosities between 2.7 and 1.3. The tortuosity of NP Au displays weak dependences on both the pore size and the porosity, consistent with the observed self-similarity in the coarsening, except for those of pores < 25 nm, which we consider deviating from the well-coarsened pore geometry. For NP Ag, the low tortuosity of the hierarchical structure can be explained with the Maxwell-Garnett equation and that of the oriented structure underlines the random orientation as the cause of slow transport in other NP metals. At last, we achieve high current densities of CO2 reduction with these two low-tortuosity NP Ags, demonstrating the significance of the structure-transport relationships for designing functional NP metals.

physics.app-ph↗
arXiv2021

Global regularity for the 3D Hall-MHD equations with low regularity axisymmetric data

In this paper, we consider the global well-posedness of the incompressible Hall-MHD equations in $\mathbb{R}^3$. We prove that the solution of this system is globally regular if the initial data is axisymmetric and the swirl components of the velocity and magnetic vorticity are trivial. It should be pointed out that the initial data without any smallness and in low regularity spaces. This improves a previous result established in \cite{Fan2013}.

math.AP↗
arXiv2020

The Cooperative Sorting Strategy for Connected and Automated Vehicle Platoons

This paper presents a "cooperative vehicle sorting" strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in the discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic Astar algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step. To improve the algorithm running time and address multiple shortest paths, a distributed stochastic Astar algorithm (DSA) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost). Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA method. The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA. In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system.

eess.SY↗
arXiv2019

$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering

Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can handle. Additionally, the data are often divided by random sampling which may be suboptimal. To address these concerns, we propose the $DC^2$ algorithm. Instead of ensemble instances, we produce structure-preserving signature pieces to be assembled and conquered. $DC^2$ achieves the efficiency of sampling-based large scale kernel methods while enabling parallel multicore or clustered computation. The data partition and subsequent compression are unified by recursive random projections. Empirically dividing the data by random projections induces smaller mean squared approximation errors than conventional random sampling. The power of $DC^2$ is demonstrated by our clustering algorithm $rpfCluster^+$, which is as accurate as some fastest approximate spectral clustering algorithms while maintaining a running time close to that of K-means clustering. Analysis on $DC^2$ when applied to spectral clustering shows that the loss in clustering accuracy due to data division and reduction is upper bounded by the data approximation error which would vanish with recursive random projections. Due to its easy implementation and flexibility, we expect $DC^2$ to be applicable to general large scale learning problems.

cs.LG↗
arXiv2019

Learning optimal orders of the underlying Euclidean norm in total variation image denoising

A novel class of semi-norms, generalising the notion of the isotropic total variation $TV_{2}$ and the an-isotropic total variation $TV_{1}$ is introduced. A supervised learning method via bilevel optimisation is proposed for the computation of optimal parameters for this class of regularizers. Existence of solutions to the bilevel optimisation approach is proven. Moreover, a finite-dimensional approximation scheme for the bilevel optimisation approach is introduced that can numerically compute a global optimizer to any given accuracy.

math.AP↗
arXiv2019

Real order (an)-isotropic total variation in image processing - Part II: Learning of optimal structures

This article is the second work in our series of papers dedicated to image processing models based on the fractional order total variation $TV^r$. In our first work of this series, we studied key analytic properties of these semi-norms. Here we focus on the more applied aspects of such models: first, in order to obtain a better reconstructed image, we propose several extensions of the fractional order total variation. Such generalizations, collectively denoted by RVL, will be modular, i.e. the parameters therein are mutually independent, and can be fine tuned to the particular task. Then, we will study the bilevel training schemes based on RVL, and show that such schemes are well defined, i.e. they admit minimizers. Finally, we will provide some numerical examples, showing that training schemes based on RVL are effectively better than those based on classical regularizer $TV$ and $TGV^2$.

math.OC↗
arXiv2019

Adaptive image processing: a bilevel structure learning approach for mixed-order total variation regularizers

A class of mixed-order \emph{PDE}-constraint regularizer for image processing problem is proposed, generalizing the standard first order total variation $(TV)$. A semi-supervised (bilevel) training scheme, which provides a simultaneous optimization with respect to parameters and the new class of regularizers, is studied. Also, A finite approximation method, which used to solve the global optimization solutions of such training scheme, is introduced and analyzed.

math.AP↗
arXiv2019

Real order (an)-isotropic total variation in image processing - Part I: analytical analysis and functional properties

In this paper, a variational, multi-dimensional model for image reconstruction is proposed, in which the regularization term consists of the $r$-order (an)-isotropic total variation seminorms $TV^r$, with $r\in \mathbb R^+$, defined via the Riemann-Liouville fractional derivative. Key properties, such as the lower semi-continuity and compactness with respect to both the function $u$ and the order of derivative $r$, are studied. This paper, the first of our series works on analytical and numerical aspects of the model, as well as the learning of optimal order $r$ for particular imaging tasks, provides a comprehensive analysis of the behavior of $TV^r$ in the space of functions with bounded (fractional order) total variation.

math.AP↗