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Diffusion Tensor Imaging and Its Application to traumatic brain injury: Basic Principles and Recent Advances

Open Journal of Medical Imaging 2012年第4期2卷 137-161页

作者： Ping-Hong Yeh Terrence R. Oakes Gerard Riedy National Capital Neuroimaging Consortium Walter Reed National Military Medical Center Bethesda USA National Intrepid Center of Excellence Bethesda USA traumatic brain injury image analysis lab Henry Jackson Foundation for the Advancement of Military Medicine Rockville USA

traumatic axonal injury is a progressive process evoked by shear forces on the brain, gradually evolving from focal axonal alteration and cumulating in neural disconnection. Clinical classifiers and conventional neuroimaging are limited in traumatic axonal injury detection, outcome prediction, and treatment guidance. Diffusion weighted imaging is an advanced magnetic resonance imaging (MRI) technique that is sensitive to the movement of water molecules, providing additional information on the micro-structural arrangement of tissue. Quantitative analysis of diffusion metrics can aid in the localization of axonal injury and/or de(dys)myelination caused by trauma. Diffusion MRI tractography is an extension of diffusion weighted imaging, and can provide additional information about white matter pathways and the integrity of brain neural networks. Both techniques are able to detect the early micro-structural changes caused by traumatic brain injury (TBI), and can be used to increase understanding of the mechanisms of brain plasticity in recovery after brain injury and possibly optimize treatment planning of patients with traumatic brain injury. This review focuses on the theoretical basis and applied advanced techniques of diffusion weighted imaging, their limitations and applications, and future directions in the application to TBI.

关键词： brain Network Connectivity High Angular Resolution Diffusion Imaging (HARDI) Diffusion Tensor Imaging (DTI) Neurocircuitry Neuroimaging traumatic Axonal injury (TAI) traumatic brain injury (TBI) Tractography White Matter

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Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

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Neural Regeneration Research 2015年第10期10卷 1622-1627页

作者： Bing-bing Guo Xiao-lin Zheng Zhen-gang Lu Xing Wang Zheng-qin Yin Wen-sheng Hou Ming Meng Department of Biomedical Engineering Chongqing University Department of Psychological and brain Sciences Dartmouth College Key lab of Visual Damage and Regeneration & Restoration Third Military Medical University

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

关键词： nerve regeneration primary visual cortex electrical stimulation visual cortical prosthesis low resolution vision pixelized image functional magnetic resonance imaging voxel size neural regeneration brain activation pattern

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一种新的多光谱MRI数据融合技术用于分析脑白质病变分段:方法及与FLAIR影像上阈值分段的比较

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国际医学放射学杂志 2010年第5期 498-498页

作者： M.d.C.Valdés Hernández K.J.Ferguson F.M.Chappell J.M.Wardlaw 廖海燕 SFC brain Imaging Research Centre Image Analysis LabDivision of Clinical NewrosciencesWestern General HospitalUniversity of Edinburgh

关键词：脑白质病变脑白质缺血老化组织分段 MRI 多光谱分段阈值分段液体衰减反转恢复序列(FLAIR)

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COVID-19-associated monocytic encephalitis(CAME):histological and proteomic evidence from autopsy

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Signal Transduction and Targeted Therapy 2023年第2期8卷 705-715页

作者： Pei-Pei Zhang Zhi-Cheng He Xiao-Hong Yao Rui Tang Jie Ma Tao Luo Chuhong Zhu Tian-Ran Li Xindong Liu Dingyu Zhang Shuyang Zhang Yi-Fang Ping Ling Leng Xiu-Wu Bian Department of Pathology the First Affiliated Hospital of University of Science&Technology of China230036 HefeiAnhuiP.R.China Institute of Pathology&Southwest Cancer Center Southwest HospitalThird Military Medical University(Army Medical University)and Key Laboratory of Tumor ImmunopathologyMinistry of Education of China400038 ChongqingP.R.China Department of Pathology Ruijin HospitalSchool of MedicineShanghai Jiaotong University200025 ShanghaiP.R.China Stem Cell and Regenerative Medicine lab Department of Medical Science Research CenterState Key Laboratory of Complex Severe and Rare DiseasesTranslational Medicine CenterPeking Union Medical College HospitalPeking Union Medical College and Chinese Academy of Medical Sciences100730 BeijingP.R.China Department of Anatomy Key Laboratory for Biomechanics and Tissue Engineering of ChongqingState Key Laboratory of TraumaBurn and Combined injuryArmy Medical University(Third Military Medical University)400038 ChongqingP.R.China Wuhan Jinyintan Hospital(Wuhan Hospital for Infectious Diseases) 430015 WuhanHubeiP.R.China Department of Cardiology Peking Union Medical College HospitalPeking Union Medical College and Chinese Academy of Medical Sciences100730 BeijingP.R.China

Severe neurological symptoms are associated with Coronavirus disease 2019(COVID-19).However,the morphologic features,pathological nature and their potential mechanisms in patient brains have not been revealed despite evidence of neurotropic *** this study,neuropathological damages and infiltrating inflammatory cells were quantitatively evaluated by immunohistochemical staining,ultrastructural examination under electron microscopy,and an image threshold method,in postmortem brains from nine critically ill COVID-19 patients and nine age-matched cadavers of healthy *** expressed proteins were identified by quantitative proteomic *** findings included neurophagocytosis,microglia nodules,satellite phenomena,extensive edema,focal hemorrhage,and infarction,as well as infiltrating mononuclear *** of COVID-19 brains revealed extensive activation of both microglia and astrocytes,severe damage of the blood-brain barrier(BBB)and various degrees of perivascular infiltration by predominantly CD14+/CD16+/CD141+/CCR7+/CD11c+monocytes and occasionally CD4+/CD8+T *** proteomic assays combined with bioinformatics analysis identified upregulated proteins predominantly involved in immune responses,autophagy and cellular metabolism in COVID-19 patient brains compared with control *** involved in brain development,neuroprotection,and extracellular matrix proteins of the basement membrane were downregulated,potentially caused by the activation of transforming growth factorβreceptor and vascular endothelial growth factor signaling ***,our results define histopathological and molecular profiles of COVID-19-associated monocytic encephalitis(CAME)and suggest potential therapeutic targets.

关键词： encephalitis metabolism histological

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Toward a Neurophysiological Measure of image Quality Perception Based on Algebraic Topology analysis

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China Communications 2022年第2期19卷 31-38页

作者： Chang Liu Xiaoyu Ma Yijie Zhou Jiaojiao Wang Dingguo Yu Institute of Intelligent Media Technology Communication University of ZhejiangZhejiang 310018China Key lab of Film and TV Media Technology of Zhejiang Province Communication University of ZhejiangZhejiang 310018China College of Media Engineering Communication University of ZhejiangZhejiang 310018China

The bandwidth of internet connections is still a bottleneck when transmitting large amounts of images,making the image quality assessment *** assessment of image quality has highlight advantages for it does not interfere with natural viewing ***,in JPEG compression,the previous study is hard to tell the difference between the electroencephalogram(EEG)evoked by different quality *** this paper,we propose an EEG analysis approach based on algebraic topology analysis,and the result shows that the difference between Euler characteristics of EEG evoked by different distortion images is striking both in the alpha and beta ***,we further discuss the relationship between the images and the EEG signals,and the results implied that the algebraic topological properties of images are consistent with that of brain perception,which is possible to give birth to braininspired image compression based on algebraic topological *** general,an algebraic topologybased approach was proposed in this paper to analyze the perceptual characteristics of image quality,which will be beneficial to provide a reliable score for data compression in the network and improve the network transmission capacity.

关键词： image quality assessment electroencephalogram algebraic topology analysis Euler characteristic

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The application of artificial intelligence to chest medical image analysis

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Intelligent Medicine 2021年第3期1卷 104-117页

作者： Feng Liu Jie Tang Jiechao Ma Cheng Wang Qing Ha Yizhou Yu Zhen Zhou Deepwise AI lab Deepwise Inc.No.8 Haidian AvenueBeijing 100080China Department of Computer Science The University of Hong KongPok Fu LamHong KongChina

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image *** lungs,bone,and mediastinum were included in terms of anatomy,while X-ray and computed tomography(CT),with and without contrast enhancement,were considered regarding imaging *** key components of deep learning were summarized,namely,network architectures,learning strategies,optimiza-tion methods,and vision ***-specific applications were discussed in detail with respect to the dimen-sion of the data input,network architecture,and modality:lung cancer,pneumonia,tuberculosis,pulmonary embolism,chronic obstructive pulmonary disease,and interstitial lung disease for lung;traumatic fractures,os-teoporosis,osteoporotic fractures,and bone metastases for bone;and coronary artery calcification and aortic dissection for vascular ***,five promising research directions and possible solutions were presented for future work.

关键词： Artificial intelligence Deep learning Medical images Chest Lung Fracture Mediastinum

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Improved block matching approach to fast disparity estimation

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1278-1285页

作者： Tao Tangfei Ja Choon Koo Hyouk Ryeol Choi School of Mechanical Engineering Xi'an Jiaotong Univ. Xi'an 710049 P. R. China State Key lab. for Manufacturing Systems Engineering Xi'an 710049 P. R. China School of Mechanical Engineering Sungkyunkwan Univ. Suwon Republic of Korea

An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another *** also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each ***, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each *** performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block *** results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.

关键词： machine vision block matching disparity estimation sum of the absolute difference.

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Automated Segmentation and Connectivity analysis for Normal Pressure Hydrocephalus

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Biomedical Engineering Frontiers 2022年第1期3卷 1-13页

作者： Angela Zhang Amil Khan Saisidharth Majeti Judy Pham Christopher Nguyen Peter Tran Vikram Iyer Ashutosh Shelat Jefferson Chen B.S.Manjunath Vision Research laboratory Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraSanta BarbaraCAUSA Chen lab Department of NeurosurgeryUniversity of CaliforniaIrvine Medical CenterOrangeCAUSA Santa Barbara Cottage Hospital Santa BarbaraCAUSA

Objective and Impact *** propose an automated method of predicting Normal Pressure Hydrocephalus(NPH)from CT scans.A deep convolutional network segments regions of interest from the *** regions are then combined with MRI information to predict *** our knowledge,this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for *** to their low cost and high versatility,CT scans are often used in NPH *** well-defined and effective protocol currently exists for analysis of CT scans for ***’index,an approximation of the ventricle to brain volume using one 2D image slice,has been proposed but is not *** proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting *** propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these *** segmentation and network features are used to train a model for NPH *** method outperforms the current state-of-the-art by 9 precision points and 29 recall *** segmentation model outperforms the current state-of-the-art in segmenting the ventricle,gray-white matter,and subarachnoid space in CT *** experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process,and network properties can increase NPH prediction accuracy.

关键词： diagnosis network Connectivity

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Artificial intelligence applications in psychoradiology

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Psychoradiology 2021年第2期1卷 94-107页

作者： Fei Li Huaiqiang Sun Bharat B.Biswal John A.Sweeney Qiyong Gong Huaxi MR Research Center(HMRRC) Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Research Unit of Psychoradiology Chinese Academy of Medical SciencesChengdu 610041SichuanP.R.China Functional and Molecular Imaging Key laboratory of Sichuan Provience Department of RadiologyWest China Hospital of Sichuan UniversityChengdu 610041SichuanP.R.China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA The Clinical Hospital of Chengdu brain Science Institute MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 610054SichuanP.R.China Department of Psychiatry and Behavioral Neuroscience University of CincinnatiCincinnatiOH 45219USA

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness,pre-diction of prognosis before treatment,and guidance for selection of effective treatments that target patient-relevant pathophysiological *** is the primary aim of the field of *** databases collected from large samples at multiple centers,sophisticated artificial intelligence(AI)algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose,predict,and make treatment *** this review,we selectively summarize psychoradiological research using magnetic reso-nance imaging of the brain to explore the neural mechanism of psychiatric disorders,and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders,as well as limitations in the application of AI that should be considered in future translational research.

关键词： Psychoradiology magnetice resonance imaging brain artificial intelligence machine learning deep learning graph neural network

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Increased representation of the non-dominant hand in pianists demonstrated by measurement of 3D morphology of the central sulcus

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Psychoradiology 2021年第2期1卷 66-72页

作者： Adam Harvey Lewis Hou Kirsteen Davidson-Kelly Rebecca S.Schaefer Sujin Hong Jean-Francois Mangin Katie Overy Neil Roberts Reid School of Music Alison House12 Nicolson SquareUniversity of EdinburghEH89DFUK School of Clinical Sciences The Queen’s Medical Research Institute(QMRI)University of EdinburghEH164TJUK Scottish Chamber Orchestra 4 Royal TerraceEdinburghEH75ABUK Health Medical and Neuropsychology UnitInstitute for PsychologyLeiden UniversityLeidenThe Netherlands Academy of Creative and Performing Arts Leiden UniversityLeidenThe Netherlands Neuropolitics Research lab and Edinburgh Imaging School of Social and Political ScienceUniversity of EdinburghEH89LNUK Universit′e Paris-Saclay CEACentre National de la Recherche Scientifique(CNRS)NeurospinBaobabGif-sur-YvetteFrance

Background:Post-mortem and magnetic resonance imaging(MRI)studies of the central sulcus,as an indicator of motor cortex,have shown that in the general population there is greater representation of the dominant compared to the non-dominant *** of musicians,who are highly skilled in performing complex finger movements,have suggested this dominance is affected by musical training,but methods and findings have been ***:In the present study,an automated image analysis pipeline using a 3D mesh approach was applied to measure central sulcus(CS)asymmetry on MR images obtained for a cohort of right-handed pianists and matched ***:The depth,length,and surface area(SA)of the CS and thickness of the cortical mantle adjacent to the CS were measured in each cerebral hemisphere by applying the brainVISA Morphologist 2012 software pipeline to 3D T1-weighted MR images of the brain obtained for 15 right-handed pianists and 14 controls,matched with respect to age,sex,and *** indices(AIs)were calculated for each parameter and multivariate analysis of covariance(MANCOVA),and post hoc tests were performed to compare differences between the pianist and control ***:A one-way MANCOVA across the four AIs,controlling for age and sex,revealed a significant main effect of group(P=0.04),and post hoc analysis revealed that while SA was significantly greater in the left than the right cerebral hemisphere in controls(Pimage analysis pipeline to 3D MR images has provided robust evidence of sig-nificantly increased representation of the non-dominant hand in the brain of pianists compared to age-,sex-,and handedness-matched *** finding su

关键词： asymmetry brain plasticity central sulcus(CS) magnetic resonance imaging(MRI) motor cortex musicians pianists surface area(SA)

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