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

Resting-state functional connectivity predicts individual la...

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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

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Organ at risk segmentation in head and neck CT images by using a two-stage segmentation framework based on 3D U-Net

arXiv

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arXiv 2018年

作者： Wang, Yueyue Zhao, Liang Song, Zhijian Wang, Manning School of Basic Medical Science Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Digital Medical Research Center Fudan University Shanghai200032 China

Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one. Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

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White Matter Deficits Underlying the Impaired Consciousness Level in Patients with Disorders of Consciousness

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Neuroscience Bulletin 2018年第4期34卷 668-678页

作者： Xuehai Wu Jiaying Zhang Zaixu Cui Weijun Tang Chunhong Shao Jin Hu Jianhong Zhu Yao Zhao Lu Lu Gang Chen Georg Northoff Gaolang Gong Ying Mao Yong He Neurosurgical Department Shanghai Huashan HospitalFudan University State Key Laboratory of Cognitive Neuroscience and Learning IDG/Mc Govern Institute for Brain ResearchBeijing Normal University Centre for Medical Image Computing Department of Computer Science University College London Radiological Department Shanghai Huashan Hospital Fudan University Psychiatry Department Shanghai Huashan Hospital Fudan University Huajia Hospital Shanghai Scientific and Statistical Computing Core National Institute of Mental Health National Institutes of Health Department of Health and Human Services Institute of Mental Health Research University of Ottawa

In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/ vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest （ROIs）, and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.

关键词： Disorder of consciousness White matter Diffusion tensor imaging Brain injury

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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

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Differential GR Expression and Translocation in the Hippocampus Mediates Susceptibility vs. Resilience to Chronic Social Defeat Stress

Differential GR Expression and Translocation in the Hippocam...

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中国神经科学学会第十二届全国学术会议

作者： Huimei Wang Qiu-Qin Han Liu Yang Hui-Jie Huang Ya-Lin Wang Rui Yu Jing Wang Xiaorong Chen Adam Pilot Gen-Cheng Wu Qiong Liu Jin Yu Department of Integrative Medicine and Neurobiology State Key Laboratory of Medical Neurobiology School of Basic Medical Sciences Institutes of Brain ScienceBrain Science Collaborative Innovation Center Shanghai Medical College Fudan University Department of Anatomy Histology and EmbryologySchool of Basic Medical Sciences Shanghai Medical College Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai

While social stress exposure is a common risk factor for affective disorders, most individuals exposed to it can maintain normal physical and psychological functioning. However, factors that determine

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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

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

arXiv

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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

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Adaptive scaled Wiener postfilter beamformer for ultrasound imaging

Adaptive scaled Wiener postfilter beamformer for ultrasound ...

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URSI Asia-Pacific Radio Science Conference (URSI AP-RASC)

作者： Saeid Aliabadi Yuanyuan Wang Jinhua Yu Department of Electronic Engineering Fudan University Shanghai China The key laboratory of medical imaging computing and computer assisted intervention of shanghai Shanghai China

In ultrasound imaging Wiener postfilter and coherent factor (CF) concepts were combined with minimum variance (MV) and eigenspace-based MV (ESBMV) beamformers to future suppress sidelobes and to improve the imaging contrast and the spatial resolution. CF, which can be considered as a scaled Wiener postfilter with high noise power weighting, provides high imaging resolution. The high resolution, however, is achieved with the contrast as a sacrifice. On other hand, Wiener postfilter, provides better signal intensity restoration and imaging contrast than CF but with lower imaging resolution. To pursue the optimal imaging contrast of Wiener postfilter and the high resolution of CF at the same time, an adaptive scaled Wiener postfilter beamformer (ASC-Wiener) is proposed. In proposed method, the noise power weighting of Wiener postfilter is adjusted adaptively so that related to the output SNR inversely needless to estimate the input SNR. Simulation and real data experiment for target point imaging show the high performance of the proposed beamformer in terms of the imaging resolution and sidelobes suppression. Cyst phantom imaging also shows that the ASC-Wiener preserves the speckle intensity and improves the imaging contrast. The contrast ratio (CR) and the contrast-to-noise ratio (CNR) are improved by 6.2/0.35 dB over ESBMV-Wiener and by 20.7/0.65 over ESBMV-CF. These results solidly prove that the imaging contrast and the imaging resolution are improved significantly and simultaneously.

关键词： Imaging Radar imaging image resolution Signal resolution Meteorology Ultrasonic imaging

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Improvement of ultrasound contrast imaging with adaptive beamformer based on pulse inversion plane wave transmission

Improvement of ultrasound contrast imaging with adaptive bea...

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URSI Asia-Pacific Radio Science Conference (URSI AP-RASC)

作者： Yurong Huang Jinhua Yu Yuanyuan Wang Shuying Li Yi Chen Department of Electronic Engineering Fudan University Shanghai China The key laboratory of medical imaging computing and computer assisted intervention of shanghai Shanghai China

Study of ultrasound contrast imaging is of great importance in recent years thanks to its wide application in ultrasonic diagnosis. Plane wave imaging has been considered as a good method with high frame rate and low mechanical index. High frame rate can improve the temporal resolution of contrast imaging. Meanwhile, low mechanical index is essential to contrast imaging since high mechanical index in traditional B-mode imaging could break micro-bubbles easily. However, plane wave contrast imaging suffers from low signal-to-noise ratio (SNR) and contrast (CR) which leads to poor image quality. In this paper, adaptive beamformer and pulse inversion (PI) technique were combined to solve the shortcomings of the plane wave contrast imaging. For the adaptive beamformer, eigenspace based minimum variance (ESBMV) with coherence factor (ESBMVCF) was proposed. Experimental results show that ESBMVCF offers an enhancement of 1.1dB, 13dB and 0.8dB in CR, contrast-to-tissue ratio (CTR) and contrast-to-noise ratio (CNR) respectively compared with minimum variance and coherence factor (MVCF). Besides, PI offers 1.5dB, 15dB and 15dB enhancement in CR, CTR and CNR compared with traditional pulse-echo imaging.

关键词： Radar imaging Meteorology Imaging Meteorological radar image resolution Radar antennas

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

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

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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

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