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High-Resolution Three-Dimensional Hybrid MRI + Low Dose CT Vocal Tract Modeling: A Cadaveric Pilot Study

Journal of Voice 2022年

作者： MEYER, David RUSHO, Rushdi Zahid ALAM, Wahidul CHRISTENSEN, Gary E. HOWARD, David M. ATHA, Jarron HOFFMAN, Eric A. STORY, Brad TITZE, Ingo R. LINGALA, Sajan Goud Janette Ogg Voice Research Center Shenandoah University Winchester Virginia United States Roy J Carver Department of Biomedical Engineering University of Iowa Iowa City Iowa United States Department of Electrical and Computer Engineering University of Iowa Iowa City Iowa United States Department of Electronic Engineering Royal Holloway University of London LDN United Kingdom Departments of Radiology Medicine and Roy J Carver Department of Biomedical Engineering University of Iowa Iowa City Iowa United States College of Science University of Arizona Tucson AZ United States Utah Center for Vocology The University of Utah Salt Lake City UT United States

Objectives: MRI based vocal tract models have many applications in voice research and education. These models do not adequately capture bony structures (e.g. teeth, mandible), and spatial resolution is often relatively low in order to minimize scanning time. Most MRI sequences achieve 3D vocal tract coverage at gross resolutions of 2 mm³ within a scan time of <20 seconds. Computed tomography (CT) is well suited for vocal tract imaging, but is infrequently used due to the risk of ionizing radiation. In this cadaveric study, a single, extremely low-dose CT scan of the bony structures is blended with accelerated high-resolution (1 mm³) MRI scans of the soft tissues, creating a high-resolution hybrid CT-MRI vocal tract model. Methods: Minimum CT dosages were determined and a custom 16-channel airway receiver coil for accelerated high (1 mm³) resolution MRI was evaluated. A rigid body landmark based partial volume registration scheme was then applied to the images, creating a hybrid CT-MRI model that was segmented in Slicer. Results: Ultra-low dose CT produced images with sufficient quality to clearly visualize the bone, and exposed the cadaver to 0.06 mSv. This is comparable to atmospheric exposures during a round trip transatlantic flight. The custom 16-channel vocal tract coil produced acceptable image quality at 1 mm³ resolution when reconstructed from ∼6 fold undersampled data. High (1 mm³) resolution MR imaging of short (<10 seconds) sustained sounds was achieved. The feasibility of hybrid CT-MRI vocal tract modeling was successfully demonstrated using the rigid body landmark based partial volume registration scheme. Segmentations of CT and hybrid CT-MRI images provided more detailed 3D representations of the vocal tract than 2 mm³ MRI based segmentations. Conclusions: The method described in this study indicates that high-resolution CT and MR image sets can be combined so that structures such as tee

关键词： Airway Computed Tomography(CT) Magnetic Resonance Imaging(MRI) Modeling Segmentation Vocal tract

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A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unit

arXiv

引用

arXiv 2023年

作者： Chang, Ping Li, Huayu Quan, Stuart F. Lu, Shuyang Wung, Shu-Fen Roveda, Janet Li, Ao Department of Electrical & Computer Engineering The University of Arizona TucsonAZ United States Division of Sleep and Circadian Disorders Departments of Medicine and Neurology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Asthma and Airway Disease Research Center College of Medicine The University of Arizona TucsonAZ United States Department of Cardiovascular Surgery Zhongshan Hospital Fudan University Shanghai China The Shanghai Institute of Cardiovascular Diseases Shanghai China Bio5 Institute The University of Arizona TucsonAZ United States College of Nursing The University of Arizona TucsonAZ United States Department of Biomedical Engineering The University of Arizona TucsonAZ United States

Background and Objective: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. Methods: We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models’ ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://***/PingChang818/TDSTF. Results: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model. Conclusion: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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An LQR-assisted control algorithm for an under-actuated in-pipe robot in water distribution systems

arXiv

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arXiv 2020年

作者： Kazeminasab, Saber Jafari, Roozbeh Banks, M. Katherine Department of Electrical and Computer Engineering Texas A&M University College Station TX United States Departments of Electrical and Computer Computer Science and Biomedical Engineering Texas A&M University College Station TX United States College of Engineering Texas A&M University College Station TX United States

To address the operational challenges of in-pipe robots in large pipes of water distribution systems (WDS), in this research, a control algorithm is proposed for our previously designed robot [4]. Our size adaptable robot has an under-actuated modular design that can be used for both leak detection and quality monitoring. First, nonlinear dynamical governing equations of the robot are derived with the definition of two perpendicular planes and two sets of states are defined for the robot for stabilization and mobilization. For stabilization, we calculated the auxiliary system matrices and designed a stabilizer controller based on the linear quadratic regulator (LQR) controller, and combined it with a proportional-integral-derivative (PID) based controller for mobilization. The controller scheme is validated with simulation in MATLAB in various operation conditions in three iterations. The simulation results show that the controller can stabilize the robot inside the pipe by converging the stabilizing states to zero and keeping them in zero with initial values between -25° and +25° and tracking velocities of 10cm/s, 30cm/s, and 50cm/s which makes the robot agile and dexterous for operation in pipelines. © 2020, CC BY.

关键词： Controllers

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Using anisotropic 3D minkowski functionals for trabecular bone characterization and biomechanical strength prediction in proximal femur specimens

arXiv

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arXiv 2020年

作者： Nagarajan, Mahesh B. De, Titas Lochmüller, Eva-Maria Eckstein, Felix Wismüller, Axel Departments of Biomedical Engineering & Imaging Sciences University of Rochester United States Department of Electrical & Computer Engineering University of Rochester United States Institute of Anatomy Paracelsus Medical University Salzburg Salzburg Austria

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure;the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets;the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p Copyright © 2020, The Authors. All rights reserved.

关键词： Risk assessment

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Strategic Attention Learning for Modality Translation

Strategic Attention Learning for Modality Translation

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jonathan Martinez Ali Akbari Kaan Sel Roozbeh Jafari Departments of Computer Science and Engineering Texas A&M University College Station TX USA Biomedical Engineering Texas A&M University College Station TX USA Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Novel wearable sensor modalities, such as bio-impedance (Bio-Z), are being introduced and often provide various advantages over current state-of-the-art in terms of accuracy, sensing coverage, or convenience of wear. The principal challenge, however, lies in the ability to interpret the sensor reading by healthcare providers. In this work, we propose a two-stage deep learning framework that leverages a novel attention mechanism to translate Bio-Z signals to highly interpretable electrocardiogram (ECG) waveforms while also predicting translation uncertainty. Our experiments indicate a 66% improvement in accuracy for 1D-CNN based models to perform competitively with more sophisticated hybrid CNN-LSTM based models in a fraction of the training time while also providing a valid uncertainty measurement.

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Energy enhanced tissue texture in spectral computed tomography for lesion classification

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Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 138-149页

作者： Yongfeng Gao Yongyi Shi Weiguo Cao Shu Zhang Zhengrong Liang Department of Radiology Stony Brook UniversityStony BrookNY 11794USA Institute of Image Processing and Pattern Recognition Xi’an Jiaotong UniversityXi’an 710049ShanxiChina Departments of Radiology Biomedical EngineeringComputer Scienceand Electrical EngineeringStony Brook UniversityStony BrookNY 11794USA

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical *** computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray ***,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion *** issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian *** results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image *** issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the *** were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration *** results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data *** CM-and HF-inputs can achieve the best AUC of 0.934 and *** texture themed study shows the insight that incorporating clinical important prior information,e.g.,tiss

关键词： Tissue texture Spectral computed tomography Lesion classification Machine learning Bayesian reconstruction

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Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis In biomedical Imaging

arXiv

引用

arXiv 2020年

作者： Wismüller, Axel De, Titas Lochmüller, Eva Eckstein, Felix Nagarajan, Mahesh B. Departments of Biomedical Engineering & Imaging Sciences University of Rochester United States Department of Electrical & Computer Engineering University of Rochester United States Institute of Anatomy and Musculoskeletal Research Paracelsus Medical University Salzburg Salzburg Austria

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p -4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications. Copyright © 2020, The Authors. All rights reserved.

关键词： computerized tomography

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Invited: Drug discovery approaches using quantum machine learning

arXiv

引用

arXiv 2021年

作者： Li, Junde Alam, Mahabubul Sha, Congzhou M. Wang, Jian Dokholyan, Nikolay V. Ghosh, Swaroop Department of Computer Science and Engineering Penn State University University Park United States Department of Electrical Engineering Penn State University University Park United States Department of Engineering Science and Mechanics Penn State University University Park United States Department of Pharmacology Penn State College of Medicine Hershey United States Department of Biochemistry & Molecular Biology Penn State College of Medicine Hershey United States Departments of Chemistry and Biomedical Engineering Penn State University University Park United States

Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and discriminative models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively. © 2021, CC BY.

关键词： Molecules

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Proportional and derivative controllers for buffering noisy gene expression

Proportional and derivative controllers for buffering noisy ...

引用

IEEE Annual Conference on Decision and Control

作者： Saurabh Modi Supravat Dey Abhyudai Singh Departments of Electrical and Computer Engineering Biomedical Engineering University of Delaware Newark DE USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. Such random fluctuations in the level of a protein critically impact functioning of intracellular biological networks, and not surprisingly, cells encode diverse regulatory mechanisms to buffer noise. We investigate the effectiveness of proportional and derivative-based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as proportional and derivative controllers are discussed, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static inputoutput sensitivity of the open-loop system. As expected, the derivative controller performs poorly in terms of rejecting external disturbances. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

关键词： Proteins Protein engineering Noise level Noise measurement Stochastic processes Steady-state Gene expression

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arXiv

引用

arXiv 2023年

作者： Bilgic, Berkin Costagli, Mauro Chan, Kwok-Shing Duyn, Jeff Langkammer, Christian Lee, Jongho Li, Xu Liu, Chunlei Marques, José P. Milovic, Carlos Robinson, Simon Schweser, Ferdinand Shmueli, Karin Spincemaille, Pascal Straub, Sina van Zijl, Peter Wang, Yi Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States University of Genoa Genoa Italy Laboratory of Medical Physics and Magnetic Resonance IRCCS Stella Maris Pisa Italy Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Netherlands Advanced MRI Section NINDS National Institutes of Health BethesdaMD United States Department of Neurology Medical University of Graz Graz Austria Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States F.M. Kirby Research Center for Functional Brain Imaging Kennedy Krieger Institute BaltimoreMD United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Helen Wills Neuroscience Institute University of California BerkeleyCA United States Pontificia Universidad Catolica de Valparaiso Valparaiso Chile High Field MR Centre Department of Biomedical Imaging and Image-Guided Therapy Medical University of Vienna Austria Buffalo Neuroimaging Analysis Center Department of Neurology Jacobs School of Medicine and Biomedical Sciences the University at Buffalo BuffaloNY United States Center for Biomedical Imaging Clinical and Translational Science Institute the University at Buffalo BuffaloNY United States Department of Medical Physics and Biomedical Engineering University College London London United Kingdom MRI Research Institute Department of Radiology Weill Cornell Medicine New YorkNY United States Department of Radiology Mayo Clinic JacksonvilleFL United States MRI Research Institute Departments of Radiology and Biomedical Engineering Cornell University New YorkNY United States

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with – as a minimum – the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting. © 2023, CC BY.

关键词： Magnetic resonance imaging

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