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2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020

作者： Gao, Yongfeng Liang, Zhengrong Lu, Siming Shi, Yongyi Chang, Shaojie Hou, Wei The Department of Radiology Stony Brook University Stony Brook NY11794 United States The Department of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony Brook University Stony Brook NY11794 United States The Department of Family Population and Preventive Medicine Stony Brook University Stony Brook NY11794 United States

ISBN: (纸本)9781728176932

Although Bayesian theory has been successfully applied for count-limited medical image reconstruction in the past two decades, its wide applications in clinic has been hampered by its hyperparameter β, which is traditionally determined by a trial-error style. To eliminate the cumbersome style, this work aims to present a selection method by mimicking the physical model of cooling down the temperature adaptively for an economic high-quality crystal. From the basic Bayes' law, the physical meaning of hyperparameter can be interpreted as the ratio of the data uncertainty (or variance α) and the prior tolerance (or σ) by formulating the probability distribution functions (FDFs) of the data fidelity and prior expectation. Inspired by this idea, the prior tolerance σ can be treated as the temperature of the texture patterns, and β can be adjusted according to different texture pattern status by satisfying the condition of each PDF in the Bayes' Law during the iteration. In Simulated phantom study, realistic Poisson noise added to the pre-log transmission data model was used. Both phantom simulation and clinical patient data results show that the proposed method can provide comparable reconstructed image quality comparing to the conventional methods but with much less reconstruction time. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stop the iteration process. © 2020 IEEE

关键词： Phantoms

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“Surgical Gps” Proof of Concept for Scoliosis Surgery

“Surgical Gps” Proof of Concept for Scoliosis Surgery

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Spring Simulation Conference (SpringSim)

作者： Austin Tapp Michel Audette Electrical Computer & Biomedical Engineering Old Dominion University Norfolk VA USA Computational Modeling & Simulation Engineering Old Dominion University Norfolk VA USA

ISBN: (数字)9781565553705

ISBN: (纸本)9781728163642

Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time.

关键词： Solid modeling Surgery Ontologies Deformable models Computational modeling Ligaments Finite element analysis

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The Effect of Stochastic Bursting on Biological Clock Precision

The Effect of Stochastic Bursting on Biological Clock Precis...

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European Control Conference (ECC)

作者： James MacLaurin Abhyudai Singh Department of Mathematical Sciences New Jersey Institute of Technology USA Faculty of Electrical and Computer Engineering Biomedical Engineering and Mathematical Sciences University of Delaware Newark DE

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

We study stochasticity in gene transcription oscillations. The oscillations in the protein concentration occur due to stochastic bursting of gene transcription being modulated by a delayed negative feedback. The oscillator dynamics is such that the mean dynamics (which the system converges towards in the large size limit) supports an attracting limit cycle. However, since there are only a finite-number of particles in biologically-realistic systems, this results in stochastic effects which perturb the system away from the limit cycle. We define an isochronal phase for the oscillation, and argue that it allows a much more powerful analysis than the Linear Noise Approximation. We demonstrate that the isochronal phase is accurate for very long periods of time: this results from the attracting dynamics of the limit cycle damping down stochasticity from the finite-size effects. Furthermore, because the system stays close to the oscillator for very long periods of time, we can obtain an estimate for the increase in errors over time due to the stochastic nature of the chemical reactions. We investigate numerically how various parametric regimes affect the resulting limit cycle dynamics.

关键词： Oscillators Limit-cycles Chemicals Stochastic processes Mathematical model Proteins Differential equations

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MR slice profile estimation by learning to match internal patch distributions

arXiv

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arXiv 2021年

作者： Han, Shuo Remedios, Samuel Carass, Aaron Schär, Michael Prince, Jerry L. Department of Biomedical Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Radiology Johns Hopkins University BaltimoreMD21205 United States

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selection profile given a specific image by learning to match its internal patch distributions. Specifically, we assume that after applying the correct slice selection profile, the image patch distribution along HR in-plane directions should match the distribution along the LR through-plane direction. Therefore, we incorporate the estimation of a slice selection profile as part of learning a generator in a generative adversarial network (GAN). In this way, the slice selection profile can be learned without any external data. Our algorithm was tested using simulations from isotropic MR images, incorporated in a through-plane super-resolution algorithm to demonstrate its benefits, and also used as a tool to measure image resolution. Our code is at https://***/shuohan/espreso2. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

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Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems

arXiv

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arXiv 2022年

作者： Wang, Yangyang Gill, Jeffrey P. Chiel, Hillel J. Thomas, Peter J. Department of Mathematics The University of Iowa Iowa CityIA52242 United States Department of Biology Case Western Reserve University ClevelandOH44106 United States Departments of Biology Neurosciencesand Biomedical Engineering Case Western Reserve University ClevelandOH44106 United States Departments of Mathematics Applied Mathematics and StatisticsBiologyCognitive ScienceData and Computer Scienceand Electrical Control and Systems Engineering Case Western Reserve University ClevelandOH44106 United States

Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al., 2015;Lyttle et al., 2017). We established in (Wang et al., 2021) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates’ hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing *** Codes 34C07, 34C15, 58E25, 49J52, 92B20, 93C73 Copyright © 2022, The Authors. All rights reserved.

关键词： Activation analysis

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Analysis of growing tumor on the flow velocity of cerebrospinal fluid in human brain using computational modeling and fluid-structure interaction

arXiv

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arXiv 2021年

作者： Uzair-Ul-Haq, Muhammad Ahmed, Ali Mustansar, Zartasha Shaukat, Arslan Margetts, Lee Waris, Asim Nadeem, Faizan Department of Biomedical Engineering & Sciences School of Mechanical & Manufacturing Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Computational Engineering Research center of Modelling and Simulation National University of Sciences and Technology Islamabad44000 Pakistan Department of Computer & Software Engineering College of Electrical & Mechanical Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Mechanical Aerospace and Civil Engineering University of Manchester United Kingdom Nuclear Medicine specialist associated with INMOL cancer hospital Lahore Pakistan

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles. © 2021, CC BY-SA.

关键词： Brain

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Retraction Note: An adaptive QoS computation for medical data processing in intelligent healthcare applications

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Neural Computing and Applications 2024年第17期36卷 10381-10381页

作者： Sodhro, Ali Hassan Malokani, Abdul Sattar Sodhro, Gul Hassan Muzammall, Muhammad Zongwei, Luo Electrical Engineering Department Sukkur IBA University Sukkur Pakistan IDA-Computer and Information Science Department Linkoping University Linköping Sweden Department of Biomedical Engineering Kyunghee University South Korea Suwon Korea Department of Physics Shah Abdul Latif University Khairpur Mirs Pakistan Department of Computer Science Bahria University Islamabad Pakistan Shenzhen Key Laboratory of Computational Intelligence Department of the Computer Science and Engineering Southern University of Science and Technology Shenzhen China

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Estimating Potential Outcome Distributions with Collaborating Causal Networks

arXiv

引用

arXiv 2021年

作者： Zhou, Tianhui Carson, William E. Carlson, David Department of Biostatistics and Bioinformatics Duke University DurhamNC27705 United States Department of Biomedical Engineering Duke University DurhamNC27705 United States Department of Civil and Environmental Engineering Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27705 United States Department of Electrical and Computer Engineering Duke University DurhamNC27705 United States

Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process (e.g. Gaussian errors). Additionally, our proposed method facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions (e.g. risk tolerance variability). CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision making process through definition of flexible comparisons. Under assumptions commonly made in the causal inference literature, we show that CCN learns distributions that asymptotically capture the correct potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. Finally, we evaluate the performance of CCN in multiple experiments on both synthetic and semi-synthetic data. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved de

关键词： Risk perception

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

arXiv

引用

arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Speech Motion Anomaly Detection via Cross-Modal Translation of 4D Motion Fields from Tagged MRI

arXiv

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arXiv 2024年

作者： Liu, Xiaofeng Xing, Fangxu Zhuo, Jiachen Stone, Maureen Prince, Jerry L. El Fakhri, Georges Woo, Jonghye Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Dept. of Radiology University of Maryland School of Medicine BaltimoreMD21201 United States Dept. of Neural and Pain Sciences University of Maryland School of Dentistry BaltimoreMD21201 United States Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Radiology and Biomedical Imaging Yale University New HeavenCT06519 United States

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes—i.e., articulatory-acoustic relation—is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies. This is especially important when evaluating and detecting abnormal articulatory features in patients with speech-related disorders. In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics. This is achieved through the use of a deep cross-modal translator trained on data from healthy individuals only, which bridges the gap between 4D motion fields obtained from tagged MRI and 2D spectrograms derived from speech acoustic data. The trained translator is used as an anomaly detector, by measuring the spectrogram reconstruction quality on healthy individuals or patients. In particular, the cross-modal translator is likely to yield limited generalization capabilities on patient data, which includes unseen out-of-distribution patterns and demonstrates subpar performance, when compared with healthy individuals. A one-class SVM is then used to distinguish the spectrograms of healthy individuals from those of patients. To validate our framework, we collected a total of 39 paired tagged MRI and speech waveforms, consisting of data from 36 healthy individuals and 3 tongue cancer patients. We used both 3D convolutional and transformer-based deep translation models, training them on the healthy training set and then applying them to both the healthy and patient testing sets. Our framework demonstrates a capability to detect abnormal patient data, thereby illustrating its potential in enhancing the understanding of the articulatory-acoustic relation for both healthy individuals and patients. © 2024, CC BY.

关键词： Anomaly detection

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