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Three-Dimensional Refractive Index Estimation Based on Non-interferometric Optical Diffraction Tomography with Angular and Axial Illumination Scanning

Three-Dimensional Refractive Index Estimation Based on Non-i...

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Digital Holography and Three-Dimensional Imaging, DH 2024

作者： Bazow, Brad Raub, Christopher B. Nehmetallah, George Departments of Electrical Engineering and Computer Science The Catholic University of America 620 Michigan Avenue N.E. WashingtonDC20064 United States The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road LaurelMD20723 United States Departments of Biomedical Engineering The Catholic University of America 620 Michigan Avenue N.E. WashingtonDC20064 United States

We present computational inverse scattering to achieve three-dimensional refractive index reconstructions of biological specimens with sub-micron resolution in an optical diffraction tomography system with angular and...

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A predictive model of death from cerebrovascular diseases in intensive care units

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Intelligent Medicine 2023年第4期3卷 267-279页

作者： Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah Department of Biomedical Engineering Faculty of HealthTehran Medical SciencesIslamic Azad UniversityTehranIran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer EngineeringCollege of EngineeringUniversity of TehranTehranIran Departments of Biomedical Engineering Shahed UniversityTehranIran Loghman Medical Center Shahid Beheshti University of Medical SciencesTehranIran

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical *** approaches have been proposed forprediction in this *** of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood *** The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical *** proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU *** electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 *** time of admission and time ofdeath are labeled in all *** this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood *** the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been ***,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were *** sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than *** The results indicate that the new approach presented in this paper can be compared with other methodsor l

关键词： Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map

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Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning

arXiv

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arXiv 2025年

作者： Qi, Xiaoming Zhang, Jingyang Fu, Huazhu Yang, Guanyu Li, Shuo Jin, Yueming Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Ministry of Education Nanjing China Institute of High Performance Computing A*STAR Singapore Departments Biomedical Engineering and Computer and Data Science Case Western Reserve University Cleveland United States

Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, where tasks evolve dynamically and asynchronously across clients, especially in medical scenario. Existing server-side FCL methods in nature domain construct a continually learnable server model by client aggregation on all-involved tasks. However, they are challenged by: (1) Catastrophic forgetting for previously learned tasks, leading to error accumulation in server model, making it difficult to sustain comprehensive knowledge across all tasks. (2) Biased optimization due to asynchronous tasks handled across different clients, leading to the collision of optimization targets of different clients at the same time steps. In this work, we take the first step to propose a novel server-side FCL pattern in medical domain, Dynamic Allocation Hypernetwork with adaptive model recalibration (FedDAH). It is to facilitate collaborative learning under the distinct and dynamic task streams across clients. To alleviate the catastrophic forgetting, we propose a dynamic allocation hypernetwork (DAHyper) where a continually updated hypernetwork is designed to manage the mapping between task identities and their associated model parameters, enabling the dynamic allocation of the model across clients. For the biased optimization, we introduce a novel adaptive model recalibration (AMR) to incorporate the candidate changes of historical models into current server updates, and assign weights to identical tasks across different time steps based on the similarity for continual optimization. Extensive experiments on the AMOS dataset demonstrate the superiority of our FedDAH to other FCL methods on sites with different task streams. The code is available:https://***/jinlab-imvr/FedDAH. © 2025, CC BY-SA.

关键词： Federated learning

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Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual Learning

arXiv

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arXiv 2025年

作者： Qi, Xiaoming Zhang, Jingyang Fu, Huazhu Yang, Guanyu Li, Shuo Jin, Yueming Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Ministry of Education Nanjing China Institute of High Performance Computing A*STAR Singapore Departments Biomedical Engineering Computer and Data Science Case Western Reserve University Cleveland United States

关键词： Federated learning

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A Novel Voltage Controlled Decoupling Method for Transmit Coils in MRI

A Novel Voltage Controlled Decoupling Method for Transmit Co...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Benjamin Malone Jacob Ruff Jue Hou Courtney Bauer Steven M. Wright Texas A&M University Electrical and Computer Engineering College Station TX USA Texas A&M University Departments Electrical and Computer Engineering and Biomedical Engineering College Station TX USA

Array coils are ubiquitous in MRI and are becoming more widely used in MR spectroscopy. Conventional PIN diode decoupling circuits require significant currents to forward bias the diodes. The approach proposed here does not require significant current and thus reduces concerns for contaminating the B 0 homogeneity with the detune current. Additionally, the proposed approach will facilitate the construction of array coils for MRI due to its *** Relevance— Decoupling is critical for constructing RF coil arrays and enables rapid MR imaging.

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Predicting Thresholds in an Auditory Overshoot Paradigm Using a Computational Subcortical Model with Efferent Feedback

Predicting Thresholds in an Auditory Overshoot Paradigm Usin...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Afagh Farhadi Laurel H. Carney Department of Electrical and Computer Engineering University of Rochester Rochester NY USA Departments of Biomedical Engineering and Neuroscience University of Rochester Rochester NY USA

The threshold for detecting a brief tone in a long-duration Gaussian-noise masker is elevated for a tone presented near masker onset compared to a tone later in the masker. This phenomenon, referred to as overshoot, has been hypothesized to reflect the dynamic control of cochlear gain by the medial olivocochlear (MOC) component of the efferent system. In this study, we used a subcortical computational model of the auditory system that included the MOC efferent system to predict listener thresholds in the overshoot paradigm. Our simulation results matched listener thresholds, including the trend of decreasing thresholds as the delay of the tone with respect to noise onset increased from 2 to 200 ms. A model without MOC efferents had the opposite trend, with thresholds increasing in longer delay conditions. These findings support the hypothesis that the MOC efferents are involved in the overshoot effect.

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An Artificial Intelligence Representation of Human Knowledge for Lung Nodule Classification

An Artificial Intelligence Representation of Human Knowledge...

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Gao, Yongfeng Pomeroy, Marc Cao, Weiguo Han, Fangfang Liang, Zhengrong Stony Brook University Department of Radiology Stony BrookNY11794 United States Stony Brook University Departments of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony BrookNY11794 United States

ISBN: (纸本)9781665488723

Low-dose computed tomography (LdCT), a recommended screening method for detection of early lung cancer, has high false positive (FP) rate, and lung nodule biopsy is a follow-up option to eliminate the FPs. It is challenging and meaningful to differentiate the nodule pathology by the LdCT screening data to avoid the costly interventional biopsy procedure. In this paper, we propose an artificial intelligence (AI) model to represent the human knowledge about lesion properties to differentiate the malignant nodules from benign ones. Three lesion properties in terms of heterogeneity, elasticity and growth are quantitatively represented by the proposed AI model. An augmented feature selection strategy was developed to integrate all lesion properties for the lesion classification. Experimental results show that the proposed AI model can achieve an AUC (area under the curve of receiver operating characteristics) score of 0.78 in the cases where physicians cannot determine the lesion type with AUC score around 0.5. © 2022 IEEE.

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Evaluation of Low-Loss Polymer Switches for Multinuclear MRI/S*

Evaluation of Low-Loss Polymer Switches for Multinuclear MRI...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Edith Touchet-Valle Seelay Tasmim Taylor H. Ware Mary P. McDougall Department of Electrical and Computer Engineering Texas A&M University College Station TX USA Department of Biomedical Engineering Texas A&M University College Station TX USA Departments of Biomedical Engineering and Materials Science & Engineering Texas A&M University College Station TX USA Departments of Biomedical Engineering and Electrical and Computer Engineering Texas A&M University College Station TX USA

Implementation of multinuclear MRI/S as a diagnostic tool in clinical settings faces many challenges. One of those challenges is the development of highly sensitive multinuclear RF coils. Current multi-tuning techniques incorporate lossy components that impact the highest achievable SNR for at least one of the coil frequencies. As a result, optimization of multinuclear coil designs continues to be a priority for RF hardware engineers. To address this challenge, a new frequency switching technology that incorporates stimuli-responsive polymer materials was explored. Q measurements were used as a comparison metric between single-tuned, a standard switching network, and the proposed switching technology. The Q losses measured in the new switching method remained below 38% when compared to single-tuned coils. These results are consistent with low loss values reported using traditional switching networks. Furthermore, preliminary testing indicates that there is potential for improvement. These results establish the new technology as a promising alternative to traditional switching *** Relevance— A low loss multi-tuning technique for MRI radiofrequency coils has the potential of improving the study and diagnosis of disease.

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Wrist EMG Improves Gesture Classification for Stroke Patients

Wrist EMG Improves Gesture Classification for Stroke Patient...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Connor D. Olsen W. Caden Hamrick Samuel R. Lewis Marta M. Iverson Jacob A. George The Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical and Computer Engineering Mercer University Macon GA USA The Department of Biomedical Engineering University of Utah Salt Lake City UT USA The Departments of Electrical and Computer Engineering Physical Medicine and Rehabilitation Biomedical Engineering and Mechanical Engineering University of Utah Salt Lake City UT USA

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

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Plasma-Treated Polymeric Biomaterials for Improved Surface and Cell Adhesion

arXiv

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arXiv 2025年

作者： Rondón, Jairo Gonzalez-Lizardo, Angel Biomedical & Chemical Engineering Departments Polytechnic University of Puerto Rico Puerto Rico San Juan United States Department of Electrical and Computer Engineering and Computer Science Polytechnic University of Puerto Rico Puerto Rico San Juan United States

Surface modification of polymeric biomaterials using plasma has emerged as an effective strategy to optimize the cell-material interface without compromising the structural properties of the material. This work presents a critical review of the impact of low-temperature plasma treatment on enhancing cell adhesion, with emphasis on the physicochemical changes induced on the surface of polymers commonly used in biomedical applications. The mechanisms of interaction between reactive plasma species and the polymer surface are analyzed, along with techniques used to introduce hydrophilic functional groups that improve wettability and biocompatibility. Scientific evidence demonstrates that this type of surface modification promotes greater cell spreading, anchorage, and proliferation, making it particularly useful in the design of tissue engineering scaffolds, implantable devices, and vascular prostheses. Finally, current and future trends in the development of smart plasma-functionalized biomaterials are discussed, highlighting their role in regenerative medicine. © 2025, CC BY-NC-ND.

关键词： Medical applications

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