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Non-invasive Assessment of Hepatic Venous Pressure Gradient (HVPG) Based on MR Flow Imaging and Computational Fluid Dynamics

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arXiv 2021年

作者： Wang, Kexin Wang, Shuo Xiong, Minghua Wang, Chengyan Wang, He Department of Physics Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China Shanghai Zhiyu Software Information Co. Ltd China Human Phenome Institute Fudan University Shanghai China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China

Clinically significant portal hypertension (CSPH) is a severe complication of chronic liver disease associated with cirrhosis, which is diagnosed by the measurement of hepatic venous pressure gradient (HVPG). However, HVPG measurement is invasive and therefore difficult to be widely applied in clinical routines. There is no currently available technique to measure HVPG noninvasively. Computational fluid dynamics (CFD) has been used for noninvasive measurement of vascular pressure gradient in the intracranial and coronary arteries. However, it has been scarcely employed in the hepatic vessel system due to the difficulties in reconstructing precise vascular anatomies and setting appropriate boundary conditions. Several computer tomography and ultrasound based studies have verified the effectiveness of virtual HVPG (vHVPG) by directly connecting the portal veins and hepatic veins before CFD simulations [16,12]. We apply the latest techniques of phase-contrast magnetic resonance imaging (PC-MRI) and DIXON to obtain the velocity and vessel anatomies at the same time. Besides, we improve the CFD pipeline in regards to the construction of vessel connections and reduction of calculation time. The proposed method shows high accuracy in the CSPH diagnosis in a study containing ten healthy volunteers and five patients. The MRI-based noninvasive HVPG measurement is promising in the clinical application of CSPH diagnosis. © 2021, CC BY.

关键词： Computational fluid dynamics

Motion Classification Based on sEMG Signals Using Deep Learning 4th

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Motion Classification Based on sEMG Signals Using Deep Learn...

4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Shen, Shu Gu, Kang Chen, Xinrong Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

ISBN: (纸本)9783030323875

Nowadays, surface electromyography (sEMG) signal plays an important role in helping physically disabled people during daily life. The development of electronic information technology has also led to the emergence of low-cost, multi-channel, wearable sEMG signal acquisition devices. Therefore, this paper proposes a new motion recognition model based on deep learning to improve the accuracy of motion recognition of sEMG signals. The model uses architecture including 6 convolutional layers and 6 pooling layers which enables the Batch Normalization layer to enhance network performance and prevent overfitting. In the experiment, NinaPro DB5 data set was used for training and testing. The data set consists of sEMG signal data collected by the double Myo armbands which contains data of 52 movements for 10 subjects. The results show that the accuracy of about 90% can be achieved when using 52 sEMG signal data from every single subject or all subjects. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep learning

Bayesian data assimilation for estimating epidemic evolution: A COVID-19 study

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arXiv 2020年

作者： Yang, Xian Wang, Shuo Xing, Yuting Li, Ling Xu, Richard Yi Da Friston, Karl J. Guo, Yike Department of Computer Science Hong Kong Baptist University Hong Kong Special Administrative Region Hong Kong Data Science Institute Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China School of Computing University of Kent United Kingdom Faculty of Engineering and Information Technology University of Technology Sydney Australia Institute of Neurology University College London United Kingdom

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art 'DARt' system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt;the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes;the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data. Copyright © 2020, The Authors. All rights reserved.

关键词： Cell proliferation

Equilibrium and transient thermodynamics: A unified dissipaton–space approach

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arXiv 2020年

作者： Gong, Hong Wang, Yao Zhang, Hou-Dao Qiao, Qin Xu, Rui-Xue Zheng, Xiao Yan, YiJing Department of Chemical Physics University of Science and Technology of China HefeiAnhui230026 China Hefei National Laboratory for Physical Sciences at the Microscale and iChEM Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Digital Medical Research Center of School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

This work presents a unified dissipaton–equation–of–motion (DEOM) theory and its evaluations on the Helmholtz free energy change due to the isotherm mixing of two isolated subsystems. One is a local impurity and another is a nonlocal Gaussian bath. DEOM constitutes a fundamental theory for such open quantum mixtures. To complete the theory, we construct also the imaginary–time DEOM formalism via an analytical continuation of dissipaton algebra, which would be limited to equilibrium thermodynamics. On the other hand, the real–time DEOM deals with both equilibrium structural and nonequilibrium dynamic properties. Its combination with the thermodynamic integral formalism would be a viable and accurate means to both equilibrium and transient thermodynamics. As illustrations, we report the numerical results on a spin–boson system, with elaborations on the underlying anharmonic features, the thermodynamic entropy versus the von Neumann entropy, and an indication of "solvent–cage" formation. Beside the required asymptotic equilibrium properties, the proposed transient thermodynamics also supports the basic spontaneity criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Free energy

Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network

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Resting-state functional connectivity predicts individual la...

第二十二届全国心理学学术会议

作者： Binke Yuan Nan Zhang Jing Yan Junfeng Lu Jinsong Wu Center for Language and Brain Shenzhen Institute of Neuroscience Department of Neurosurgery The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of China Glioma Surgery Division Department of NeurosurgeryHuashan HospitalFudan University Department of MRI The First Affiliated Hospital of Zhengzhou University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

Aim: Language deficits following brain tumors should consider the dynamic interactions between different tumor growth kinetics and functional network reorganization. However, no study has attempted to investigate the language network basis at the whole brain level to determine the language network reorganization differences between low-grade glioma(LGG) and high-grade glioma(HGG) patients. Methods: Inclusion Criteria:(1) Pathologically confirmed glioma in left cerebral hemisphere and overlapped or was within the language network areas proposed by Fedorenko et al.(2010);(2) patients' age was 18–75 years old;(3) Right-handedness;(4) No symptoms of motor impairment;(5) No history of brain surgery;(6) No midline shift in structural images;(7) No history of other major neurological or psychiatric disorders. In total, 126 patients,including 77 patients with LGG and 49 patients with HGG met all inclusion criteria and completed the entire preoperative clinical, language, and imaging data acquisition. All processes strictly followed the requirements of the Declaration of Helsinki. This study was approved and supervised by the Ethics Committee of Huashan Hospital and the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, respectively. Written informed consents were obtained from the human subjects and their legal guardians. Language function: ABC(Aphasia Battery for Chinese speakers) was used and Aphasia Quotient(AQ) score(range, 0–100) was calculated for each patient. Rs-f MRI data: TR = 2,000 ms;TE = 35 ms;FA =90°;240 volumes;eyes closed. T1 and T2 images were also acquired for tumor mapping and registration. Rs-f MRI data preprocessing: slice timing, motion correction, normalization by DARTEL(MNI), smoothness(6 mm), detrend, nuisance regression, filter(0.01-0.1 Hz) and scrubbing(FD > 0.05). No patients exhibited a head motion > 3 mm maximum translation or 3° rotation. Nuisance signals included 36 head motion parameters, global signals, cerebrospinal

关键词： low-grade glioma high-grade glioma resting-state fMRI language machine learning

Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients 1

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20th International Conference on medical image computing and computer-assisted intervention, MICCAI 2017

作者： Chen, Lei Zhang, Han Thung, Kim-Han Liu, Luyan Lu, Junfeng Wu, Jinsong Wang, Qian Shen, Dinggang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Radiology and BRIC University of North Carolina Chapel Hill United States School of Biomedical Engineering Med-X Research Institute Shanghai Jiao Tong University Shanghai China Department of Neurosurgery Huashan Hospital Fudan University Shanghai China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner. © Springer International Publishing AG 2017.

关键词： Forecasting

Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

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arXiv 2017年

作者： Jiang, Dongsheng Dou, Weiqiang Vosters, Luc Xu, Xiayu Sun, Yue Tan, Tao Digital Medical Research Center School of Basic Medical Science Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer-Assisted Intervention Shanghai China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Xian Jiaotong University Xian710049 China Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xian Jiaotong Universty Xian710049 China Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands ScreenPoint Medical Nijmegen Netherlands

The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be directly used in the denosing process without optimization and even without the noise level parameter. Specifically, a ten convolutional layers neural network combined with residual learning and multi-channel strategy was proposed. Two training ways: training on a specific noise level and training on a general level were conducted to demonstrate the capability of our methods. Experimental results over synthetic and real 3D MR data demonstrate our proposed network can achieve superior performance compared with other methods in term of both of the peak signal to noise ratio and the global of structure similarity index. Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets. Furthermore, our training model show good general applicability. Copyright © 2017, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Research of an electromagnetic tracking method based on two electrically-controlled magnetic sources

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Research of an electromagnetic tracking method based on two ...

IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

作者： Sha, Min Wang, Yifeng Ding, Ning Fang, Zuxiang Wu, Xiaomei Department of Electronic Engineering Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai China

ISBN: (纸本)9781479919796

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic sources. Each source consists of three mutual orthogonal coils. The method can skillfully determine the rotation angles of the maximum magnetic flux density vector (MMFD V) generated by a magnetic source according to the Biot-Savart Law. Then the position of the sensor can be calculated with a noniterative geometrical algorithm in real time. Simulation results demonstrate a good performance of this method. © 2015 IEEE.

关键词： Finite element method

Research of an Electromagnetic Tracking Method Based on Two Electrically-controlled Magnetic Sources

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Research of an Electromagnetic Tracking Method Based on Two ...

2015 IEEE Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2015)

作者： Min Sha Yifeng Wang Ning Ding Xiaomei Wu Zuxiang Fang Department of Electronic Engineering Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (纸本)9781479919819

This paper presents an electromagnetic tracking method based on two electrically-controlled magnetic *** source consists of three mutual orthogonal *** method can skillfully determine the rotation angles of the maximum magnetic flux density vector(MMFD V) generated by a magnetic source according to the Biot-Savart *** the position of the sensor can be calculated with a noniterative geometrical algorithm in real *** results demonstrate a good performance of this method.

关键词： electromagnetic tracking MMFDV fast determination algorithm finite element analysis