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Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

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arXiv 2023年

作者： Yarach, Uten Chatnuntawech, Itthi Liao, Congyu Teerapittayanon, Surat Iyer, Siddharth Srinivasan Kim, Tae Hyung Haldar, Justin Cho, Jaejin Bilgic, Berkin Hu, Yuxin Hargreaves, Brian Setsompop, Kawin Radiologic Technology Department Associated Medical Sciences Chiang Mai University Chiang Mai Thailand National Nanotechnology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Computer Engineering Hongik University Seoul Korea Republic of Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Department of Bioengineering Stanford University StanfordCA United States Packard Electrical Engineering Building 350 Jane Stanford Way StanfordCA94305 United States

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application. © 2023, CC BY.

关键词： Magnetic resonance imaging

Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference

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arXiv 2022年

作者： Dwivedi, Ayush Kumar Chaudhari, Sachin Varshney, Neeraj Varshney, Pramod K. The Signal Processing and Communication Research Center International Institute of Information Technology Hyderabad500032 India The Wireless Networks Division National Institute of Standards and Technology GaithersburgMD20899 United States The Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

In search of a driver for atrial fibrillation

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Heart rhythm 2025年

作者： Lianne N van Staveren Yannick J H J Taverne Richard Hendriks Natasja M S de Groot Department of Cardiology Erasmus Medical Center Rotterdam. Department of Cardiothoracic Surgery Erasmus Medical Center Rotterdam. Department of Microelectronics Signal Processing Systems Faculty of Electrical Engineering mathematics and computer sciences Delft University of Technology Delft. Department of Cardiology Erasmus Medical Center Rotterdam Department of Microelectronics Signal Processing Systems Faculty of Electrical Engineering mathematics and computer sciences Delft University of Technology Delft. Electronic address: n.m.s.degroot@erasmusmc.nl.

BACKGROUND:Short atrial fibrillation cycle lengths (AFCL) and regular activation patterns are associated with drivers of atrial fibrillation (AF), though the relation with underlying patterns of activation is incompletely understood. Previous studies used automated assessment of electrograms to determine fast and regular fibrillatory rates. OBJECTIVE:We investigated the relation between AFCL, temporal variation in AFCL and the occurrence of driver-like patterns of activation using high-density local activation time mapping. METHODS:High-density epicardial mapping of the right atrium and left atrial ventricular groove including Bachmann's bundle was performed in 71 patients admitted for elective cardiac surgery. Recording sites with the shortest median AFCL (mAFCL) or the smallest standard deviation of AFCL (SD) were identified. Patterns of activation included focal or rotational activation, smooth propagation, propagation with conduction block (CB), collision and remnant activity. RESULTS:There was a higher number of fibrillation waves with CB (81[76-85]% versus 74[68-76]%, p<0.001) and fractionated potentials (22[12-37]% vs 12[9-15]%, p<0.001) at mAFCL than at other recording sites. SD sites harbored more smoothly propagating waves (33[24-54]% vs 17[11-25]%, p<0.001) and a higher proportion of single potentials (76[60-89]% vs 59[54-65]%, p<0.001). Both highly regular and fastest reactivated sites did not correspond to the origin of (repetitive) focal fibrillation waves. CONCLUSIONS:During extensive mapping, the fastest or most regularly activated areas are characterized by CB and smoothly propagating fibrillation waves instead of repetitive occurrence of focal or rotational activation patterns.

关键词： atrial fibrillation atrial fibrillation cycle length drivers irregularity mapping

Nonlinear extensions of new causality

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Neuroscience Informatics 2021年第1-2期1卷 100001-100001页

作者： Nariyoshi, Pedro C. Deller, J.R. Department of Electrical and Computer Engineering Statistical Signal Processing Lab BEACON Center Michigan State University East Lansing 48824-1226 MI United States

New causality (NC) is a relatively recent method for inferring causal relationships between time-series data. Similar to other popular causal inference methods, like Granger causality (GC), NC can be evaluated in time or frequency domain. NC derives its value by partitioning a predictive model, grouping them by different inputs, and finding a normalized ratio of the power of all contributions. In its seminal form, NC is defined atop a linear ARMAX models. If the contribution between two time-series cannot be accurately expressed with a linear model, the seminal form of NC cannot accurately measure the causal relationship. In the frequency domain, linear models also prevent cross-frequency contributions from being measured. This work introduces an extension to NC to NARMAX models. This extension reduces to the seminal form when applied to linear models and can be also evaluated in the frequency domain. The nonlinear extension is applied to a range of synthetic models and real EEG data with promising results. A discussion on modeling and its effect on linear and nonlinear NC estimates is provided. © 2021 The Author(s)

关键词： Granger causality New causality Nonlinear modeling

Estimating Rényi's α-cross-entropies in a matrix-based way

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arXiv 2021年

作者： Sledge, Isaac J. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL United States

Conventional information-theoretic quantities assume access to probability distributions. Estimating such distributions is not trivial. Here, we consider function-based formulations of cross entropy that sidesteps this a priori estimation requirement. We propose three measures of Rényi's α-cross-entropies in the setting of reproducing-kernel Hilbert spaces. Each measure has its appeals. We prove that we can estimate these measures in an unbiased, non-parametric, and minimax-optimal way. We do this via sample-constructed Gram matrices. This yields matrix-based estimators of Rényi's α-cross-entropies. These estimators satisfy all of the axioms that Rényi established for divergences. Our cross-entropies can thus be used for assessing distributional differences. They are also appropriate for handling high-dimensional distributions, since the convergence rate of our estimator is independent of the sample dimensionality. Python code for implementing these measures can be found at https://***/isledge/MBRCE. © 2021, CC BY.

关键词： Probability distributions

AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

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JACC: Advances

JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

Reconfigurable Intelligent Surfaces Relying on Non-Diagonal Phase Shift Matrices

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arXiv 2022年

作者： Li, Qingchao El-Hajjar, Mohammed Hemadeh, Ibrahim Shojaeifard, Arman Mourad, Alain A.M. Clerckx, Bruno Hanzo, Lajos The Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom InterDigital LondonEC2A 3QR United Kingdom The Communications and Signal Processing Group Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

Reconfigurable intelligent surfaces (RIS) have been actively researched as a potential technique for future wireless communications, which intelligently ameliorate the signal propagation environment. In the conventional design, each RIS element configures and reflects its received signal independently of all other RIS elements, which results in a diagonal phase shift matrix. By contrast, we propose a novel RIS architecture, where the incident signal impinging on one element can be reflected from another element after an appropriate phase shift adjustment, which increases the flexibility in the design of RIS phase shifts, hence, potentially improving the system performance. The resultant RIS phase shift matrix also has off-diagonal elements, as opposed to the pure diagonal structure of the conventional design. Compared to the state-of-art fully-connected/group-connected RIS structures, our proposed RIS architecture has lower complexity, while attaining a higher channel gain than the group-connected RIS structure, and approaching that of the fully-connected RIS structure. We formulate and solve the problem of maximizing the achievable rate of our proposed RIS architecture by jointly optimizing the transmit beamforming and the non-diagonal phase shift matrix based on alternating optimization and semi-define relaxation (SDR) methods. Moreover, the closed-form expressions of the channel gain, the outage probability and bit error ratio (BER) are derived. Simulation results demonstrate that our proposed RIS architecture results in an improved performance in terms of the achievable rate compared to the conventional architecture, both in single-user as well as in multi-user scenarios. © 2022, CC BY.

关键词： Bit error rate

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

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IFAC-PapersOnLine 2021年第7期54卷 463-468页

作者： Konstantin Usevich Philippe Dreesen Mariya Ishteva Université de Lorraine CNRS CRAN Nancy France KU Leuven Dept. Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Department of Computer Science ADVISE-NUMA campus Geel Belgium

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem.

关键词： Block structured system identification parallel Wiener-Hammerstein systems Volterra kernels low-rank tensor recovery canonical polyadic decomposition

Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems