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Clinical Cancer Bulletin 2022年第3期1卷 167-171页

作者： Ye GU Kai XUE Quan LIU Huankang ZHANG Hongmeng YU Department of Neurosurgery Eye&ENT HospitalFudan UniversityShanghaiChina Department of Otolaryngology Eye&ENT HospitalFudan UniversityShanghaiChina Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Fudan UniversityShanghaiChina Research Units of New Technologies of Endoscopic Surgery in Skull Base Tumor Chinese Academy of Medical SciencesShanghaiChina

Objective Although intracranial and extracranial communicating meningiomas are uncommon in clinical settings,they pose a considerable challenge for ***,one-stage surgery should be more comprehensively explained as it is not frequently reported as a first-line *** report A 27-year-old man with a massive intracranial and extracranial communicating meningioma was admitted with nasal congestion and mild numbness on the left side of his face.A combined transcranial and endoscopic endonasal approach was performed in a single day aided by preoperative tumor *** tumor was nearly completely removed without any serious ***-up revealed the disappearance of diplopia,with the patient having a normal ***,no tumor progression was *** The combined transcranial and endoscopic endonasal approach is feasible for removing certain massive intracranial and extracranial communicating *** techniques,such as embolization,microscopic,and endoscopic manipulation,are indispensable tools for treating such cases.

关键词： meningioma transcranial approach endoscopic endonasal approach

Estimating mean trabecular bone spacing based on the combination of Hilbert transform and fundamental frequency estimation

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Science Foundation in China 2017年第3期25卷 57-71页

作者：李颖徐峰刘成成许凯亮他得安王威琪 Department of Electronic Engineering Fudan UniversityShanghai 200433China Institute of Acoustics Tongji UniversityShanghai 200092China Sorbonne Université UPMC Univ Paris 06INSERM UMR-S 1146CNRS UMR 7371Laboratoired'ImagerieBiomédicaleF-75006ParisFrance State Key Laboratory of ASIC and System Fudan UniversityShanghai 200433China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention(MICCAI)of Shanghai Shanghai 200032China

Ultrasonic backscatter signals from cancellous bone are sensitive to the microstructure of trabecular bone,and thus enable the feasibility to extract microstructural information of trabecular *** mean trabecular bone spacing(MTBS)is an important parameter for characterizing bone *** paper proposes an MTBS estimation method based on the combination of Hilbert transform and fundamental frequency estimation(CHF). The CHF was verified with ultrasonic backscatter signals from simulations and in vitro measurements at a central frequency of *** CHF method was compared with the simplified inverse filter tracking(SIFT)method,Simons' Quadratic Transformation(QT)method,Singular Spectrum Analysis(SSA)method,and Spectral Autocorrelation(SAC)***-Carlo simulations were performed by varying the MTBS,signal-to-noise ratio(SNR),standard deviation of regular spacing(SDRS),amplitude ratio of diffuse scattering to regular scattering(Ad)and frequency dependent attenuation(nBUA).The simulation results showed that the CHF method had a better performance in MTBS estimation under almost all the examination conditions except for *** estimation percentage correct(EPC)was greater than 90% when the MTBS was in the range of 0.4to *** the in vitro measurements,the estimated EPC by the CHF method was91.25±7.81%(mean±standard deviation).A significant correlation was observed for the CHF-estimated MTBS and micro-computed tomography(μ-CT)-measured values(R^2=0.75,p<0.01).These results demonstrate that the CHF method is anti-interference for MTBS estimation and can be used to estimate trabecular bone spacing.

关键词： Ultrasonic backscatter Mean trabecular bone spacing estimation Hilbert transform and fundamental frequency estimation Cancellous bone microstructure

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

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

超声背散射松质骨诊断仪及其在新生儿骨质评价中的应用

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Engineering 2015年第3期 123-137页

作者：刘成成张蓉李颖徐峰他得安王威琪 Department of Electronic Engineering Fudan University Department of Neonatology Children’s Hospital of Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松... 详细信息

超声背散射技术在松质骨骨质无损评价中极具前景。本文介绍了一种新型超声背散射骨质诊断仪(UBBD)及其在新生儿骨质状况评价中的应用。超声背散射骨质诊断仪具有无损、无电离辐射、便携及操作简便等优点,可以在短时间内(5 s)获得人体松质骨背散射信号。笔者共采集了467例新生儿(268男/199女)左侧跟骨处的背散射信号,所用探头中心频率为3.5 MHz。逐步调节背散射有效信号(SOI)的延迟(T1)和长度(T2),计算表观积分背散射(AIB)、表观背散射频率斜率(FSAB)及零频率截距(FIAB)和频谱质心偏移量(SCS)等参数。结果表明,背散射有效信号的选取对背散射测量有直接的影响。当T1较短时(<8μs),AIB和FIAB与新生儿胎龄有显著的正相关性(|R|=0.45,P<0.001);而当T1较长时(>10μs),AIB和FIAB负相关于新生儿胎龄(|R|=0.56,P<0.001),还可以观察到FSAB和SCS与新生儿胎龄有中度的正相关性(|R|=0.45,P<0.001)。T2对背散射测量的影响较小,引起相关系数的波动较小。本文通过自制的松质骨超声诊断仪证明超声背散射信号用于评价新生儿骨质状况的可行性。本文还提出了明确的背散射有效信号选取规则及新生儿骨质评价标准。

关键词：超声背散射松质骨评价有效信号背散射仪器新生儿骨质

USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient image Analysis

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arXiv 2023年

作者： Jiao, Jing Zhou, Jin Li, Xiaokang Xia, Menghua Huang, Yi Huang, Lihong Wang, Na Zhang, Xiaofan Zhou, Shichong Wang, Yuanyuan Guo, Yi Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai China Fudan University Shanghai Cancer Center Shanghai China Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States SenseTime Research Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

Clomipramine inhibits microglial NLRP3 inflammasome in the hippocampus of depressive rats

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Clomipramine inhibits microglial NLRP3 inflammasome in the h...

中国解剖学会2021年年会

作者： Wang Yalin Zhang Shanshan Yue Na Liu Guixue Huang Huijie Han Qiuqin Gong Wenqing Chen Xiaorong Zhang Yaodong Yu Jin Xiao Honglei Qin Song Li Wensheng Liu Qiong Department of Integrative Medicine and Neurobiology School of Basic Medical Sciences Fudan University Department of Anatomy Histology and EmbryologySchool of Basic Medical SciencesFudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Children's Hospital Affiliated to Zhengzhou University Henan Neural Development Engineering Research Center

Neuroinflammation is implicated in the pathophysiology of *** reduction of hippocampal volume in depression remains controversial because of interindividual variability in clinical ***,we studied the effects of clomipramine,a tricyclic antidepressant (TCA) that modulates serotonin and norepinephrine uptake,on chronic mild unpredictable stress (CMS)-induced depressive-like behaviors,hippocampal neuroinflammation, and hippocampal volume.

关键词： CMS Clomipramine inhibits microglial NLRP3 inflammasome in the hippocampus of depressive rats

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

DEEP RECURSIVE EMBEDDING FOR HIGH-DIMENSIONAL DATA

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arXiv 2021年

作者： Zhou, Zixia Zu, Xinrui Wang, Yuanyuan Lelieveldt, Boudewijn P.F. Tao, Qian The Department of Electronic Engineering Fudan University Shanghai200433 China University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China The Division of Image Processing Department of Radiology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands The Department of Imaging Physics Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical and practical value. In this paper, we propose to combine deep neural networks (DNN) with mathematics-guided embedding rules for high-dimensional data embedding. We introduce a generic deep embedding network (DEN) framework, which is able to learn a parametric mapping from high-dimensional space to low-dimensional space, guided by well-established objectives such as Kullback-Leibler (KL) divergence minimization. We further propose a recursive strategy, called deep recursive embedding (DRE), to make use of the latent data representations for boosted embedding performance. We exemplify the flexibility of DRE by different architectures and loss functions, and benchmarked our method against the two most popular embedding methods, namely, t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). The proposed DRE method can map out-of-sample data and scale to extremely large datasets. Experiments on a range of public datasets demonstrated improved embedding performance in terms of local and global structure preservation, compared with other state-of-the-art embedding methods. © 2021, CC BY-NC-SA.

关键词： Deep neural networks

Intracardiac Electrogram Signals key Feature Points Recognition Using Dense Convolutional Network

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Intracardiac Electrogram Signals Key Feature Points Recognit...

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data medical services Myocardium Feature extraction Pattern recognition Testing