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mcLARO: Multi-Contrast Learned Acquisition and Reconstruction Optimization for simultaneous quantitative multi-parametric mapping

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arXiv 2023年

作者： Zhang, Jinwei Nguyen, Thanh D. Solomon, Eddy Li, Chao Zhang, Qihao Li, Jiahao Zhang, Hang Spincemaille, Pascal Wang, Yi Department of Biomedical Engineering Cornell University IthacaNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Applied Physics Cornell University IthacaNY United States

Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM mapping in a single scan using multi-contrast Learned Acquisition and Reconstruction Optimization (mcLARO). Methods: A pulse sequence was developed by interleaving inversion recovery and T2 magnetization preparations and single-echo and multi-echo gradient echo acquisitions, which sensitized k-space data to T1, T2, T2* and magnetic susceptibility. The proposed mcLARO used a deep learning framework to optimize both the multi-contrast k-space under-sampling pattern and the image reconstruction based on image feature fusion. The proposed mcLARO method with R = 8 under-sampling was validated in a retrospective ablation study using fully sampled data as reference and evaluated in a prospective study using separately acquired conventionally sampled quantitative maps as reference standard. Results: The retrospective ablation study showed improved image sharpness of mcLARO compared to the baseline network without multi-contrast sampling pattern optimization or image feature fusion, and negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values were obtained by the under-sampled reconstructions compared to the fully sampled reconstruction. The prospective study showed small or negligible bias and narrow 95% limits of agreement on regional T1, T2, T2* and QSM values by mcLARO (5:39 mins) compared to reference scans (40:03 mins in total). Conclusion: mcLARO enabled fast sub-millimeter T1, T2, T2* and QSM mapping in a single scan. © 2023, CC BY.

关键词： Mapping

visClust: A visual clustering algorithm based on orthogonal projections

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arXiv 2022年

作者： Breger, Anna Karner, Clemens Ehler, Martin Faculty of Mathematics University of Vienna Austria Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Center of Medical Physics and Biomedical Engineering Medical University of Vienna Austria

We present a novel clustering algorithm, visClust, that is based on lower dimensional data representations and visual interpretation. Thereto, we design a transformation that allows the data to be represented by a binary integer array enabling the use of image processing methods to select a partition. Qualitative and quantitative analyses measured in accuracy and an adjusted Rand-Index show that the algorithm performs well while requiring low runtime and RAM. We compare the results to 6 state-of-the-art algorithms with available code, confirming the quality of visClust by superior performance in most experiments. Moreover, the algorithm asks for just one obligatory input parameter while allowing optimization via optional parameters. The code is made available on GitHub and straightforward to use. Copyright © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

General framework for the mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

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Physical Review Materials 2023年第10期7卷 105604-105604页

作者： Weiwei Jin Amit Datye Udo D. Schwarz Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Department of Chemical and Environmental Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with increasing ɛ̇eng, while for others, σmax increases with increasing ɛ̇eng, and for certain alloys σmax versus ɛ̇eng is nonmonotonic. To understand their strain rate sensitivity, we conduct molecular dynamics simulations of metallic glasses undergoing uniaxial compression at finite strain rates and coupled to heat baths with a range of temperatures T0 and damping parameters b. In the T0→0 and b→0 limits, we find that the compressive strength σmax versus temperature T obeys a “chevron-shaped” scaling relation. In the low-strain-rate regime, σmax decreases linearly with increasing T, whereas σmax grows as a power law with decreasing T in the high-strain-rate regime. For T0>0, σmax(T) deviates from the scaling curve at low strain rates, but σmax(T) rejoins the scaling curve as the strain rate increases. Enhanced dissipation reduces compression-induced heating, which causes σmax(T) to deviate from the b→0 scaling behavior for intermediate strain rates, but σmax(T) converges to the high-strain-rate power-law scaling behavior at sufficiently high strain rates. Determining σmax(T) as a function of b and T0 provides a general framework for explaining the strain rate sensitivity of metallic glasses under compression.

关键词： Compressive strength Mechanical deformation Amorphous materials Metallic glasses Molecular dynamics

Integrable Fractional Modified Korteweg-de Vries, Sine-Gordon, and Sinh-Gordon Equations

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arXiv 2022年

作者： Ablowitz, Mark J. Been, Joel B. Carr, Lincoln D. Department of Applied Mathematics University of Colorado BoulderCO United States Department of Applied Mathematics and Statistics Department of Physics Colorado School of Mines GoldenCO United States Quantum Engineering Program Department of Applied Mathematics and Statistics Department of Physics Colorado School of Mines GoldenCO United States

The inverse scattering transform allows explicit construction of solutions to many physically significant nonlinear wave equations. Notably, this method can be extended to fractional nonlinear evolution equations characterized by anomalous dispersion using completeness of suitable eigenfunctions of the associated linear scattering problem. In anomalous diffusion, the mean squared displacement is proportional to tα, α > 0, while in anomalous dispersion, the speed of localized waves is proportional to Aα, where A is the amplitude of the wave. Fractional extensions of the modified Korteweg-deVries (mKdV), sine-Gordon (sineG) and sinh-Gordon (sinhG) and associated hierarchies are obtained. Using symmetries present in the linear scattering problem, these equations can be connected with a scalar family of nonlinear evolution equations of which fractional mKdV (fmKdV), fractional sineG (fsineG), and fractional sinhG (fsinhG) are special cases. Completeness of solutions to the scalar problem is obtained and, from this, the nonlinear evolution equation is characterized in terms of a spectral expansion. In particular, fmKdV, fsineG, and fsinhG are explicitly written. One-soliton solutions are derived for fmKdV and fsineG using the inverse scattering transform and these solitons are shown to exhibit anomalous dispersion. © 2022, CC BY.

关键词： Dispersion (waves)

Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

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Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

Partial observation can induce mechanistic mismatches in data-constrained models of neural dynamics 38

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Partial observation can induce mechanistic mismatches in dat...

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, William Zavatone-Veth, Jacob A. Ruben, Benjamin S. Pehlevan, Cengiz Biophysics Graduate Program Harvard University CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Center for Brain Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States

One of the central goals of neuroscience is to gain a mechanistic understanding of how the dynamics of neural circuits give rise to their observed function. A popular approach towards this end is to train recurrent neural networks (RNNs) to reproduce experimental recordings of neural activity. These trained RNNs are then treated as surrogate models of biological neural circuits, whose properties can be dissected via dynamical systems analysis. How reliable are the mechanistic insights derived from this procedure? While recent advances in population-level recording technologies have allowed simultaneous recording of up to tens of thousands of neurons, this represents only a tiny fraction of most cortical circuits. Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student, even when the two networks have matching single-unit dynamics. In particular, partial observation of models of low-dimensional cortical dynamics based on functionally feedforward or low-rank connectivity can lead to surrogate models with spurious attractor structure. Our results illustrate the challenges inherent in accurately uncovering neural mechanisms from single-trial data, and suggest the need for new methods of validating data-constrained models for neural dynamics. © 2024 Neural information processing systems foundation. All rights reserved.

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Several Agent-Based and Cellular Automata Mathematical Frameworks for Modeling Pancreatic Cancer

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Several Agent-Based and Cellular Automata Mathematical Frame...

European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2019

作者： Chen, Jiao Vermolen, Fred J. Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam Netherlands Division of Mathematics and Statistics Faculty of Sciences Hasselt University Diepenbeek Belgium

ISBN: (纸本)9783030558734

Mathematical modeling sheds light on cancer research. In addition to reducing animal-based experiments, mathematical modeling is able to provide predictions and prevalidate hypotheses quantitatively. In this work, two different agent-based frameworks regarding cancer modeling are summarised. In contrast, cell-based models focus on the behavior of every single cell and presents the interaction of cells on a small scale, whereas, cellular automata models are used to simulate the interaction of cells with their microenvironment on a large tissue scale. © 2021, Springer Nature Switzerland AG.

关键词： Cells

Machine-learned design principles for enhanced red emission from nitride quantum wells

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arXiv 2024年

作者： Pant, Nick Armitage, Rob Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science & Engineering University of Michigan Ann ArborMI48109 United States Lumileds LLC San JoseCA95131 United States

Significant effort has been devoted toward mitigating polarization fields in nitride LEDs, as they are viewed as detrimental to light emission, albeit with limited success for red emission. Here, we show that such fields can unusually enhance the optical oscillator strength of red-emitting nitride quantum wells. This insight is enabled by machine-learned models trained on multi-scale quantum-mechanical simulations, which allow for a global exploration of the quantum-well design space at greatly reduced computational cost. Larger polarization fields correlate with higher wave-function overlaps for red emission, a consequence of the quantum-confined Stark effect enabling thinner quantum wells. This finding has implications for designing efficient red nitride LEDs, which are crucial for miniaturizing polychromatic LED pixels to the micron scale for extended-reality and biomedical applications. More broadly, this work highlights the power of machine learning in accelerating the discovery of unconventional paradigms for semiconductor design. Copyright © 2024, The Authors. All rights reserved.

关键词： Semiconductor quantum wells