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Better Fibre Orientation Estimation with Single-Shell diffusion mri Using Spherical U-Net

国际计算机前沿大会会议论文集 2023年第2期 3-12页

作者： Hang Zhao Chengdong Deng Yu Wang Jiquan Ma Department of Computer Science and Technology Heilongjiang UniversityHarbinChina

diffusion mri is an important technology for detecting human brain nerve pathways,aiding in neuroscience and clinical diagnosis.However,the Multi-ShellMulti-TissueConstrainedSphericalDeconvolution(M-CSD)method,which is a signiﬁcant technique for reconstructing theﬁbre orientation distribution func-tion(fODF),requires multishell data with a considerable number of gradient direc-tions to achieve high accuracy.As multishell data are not easy to acquire,the Single-Shell Single-Tissue CSD(S-CSD)suffers from the Partial Volume Effect(PVE).It would be more convenient if we could use single-shell data to reconstruct better fODFs.We propose a novel method that utilizes the spatial structure and anisotropy of dmri data through a spherical convolution network.We reduce the need for high-quality data by utilizing b=1000 s/mm2 with 60 gradient directions or even less.Our results show that our method outperforms the traditional S-CSD when compared to the M-CSD results as our gold standard.

关键词： Deep learning Fibre orientation estimation diffusion mri Spherical U-Net

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On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

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Magnetic Resonance Letters 2023年第3期3卷 232-247,I0003页

作者： Alfredo Ordinola Shan Cai Peter Lundberg Ruiliang Bai Evren Ozarslan Department of Biomedical Engineering(IMT) Link oping UniversityLink opingS-58185Sweden Department of Radiation Physics Department of HealthMedicine and Caring Sciences(HMV)Link oping UniversityLink opingS-58183Sweden Center for Medical Image Science and Visualization(CMIV) Link oping UniversityLink opingS-58185Sweden School of Medicine Zhejiang University866 Yuhangtang RdHangzhou310058China

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dmri).Many analysis frameworks using dmri data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.

关键词： diffusion mri Water exchange Sampling schemes Double diffusion encoding

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Diffusivity-limited q-space trajectory imaging

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Magnetic Resonance Letters 2023年第2期3卷 187-196,I0005页

作者： Deneb Boito Magnus Herberthson Tom Dela Haije Ida Blystad Evren Ozarslan Department of Biomedical Engineering Campus USLinkoping UniversityLinkopingSE-58183.Sweden Center for Medical Image Science and Visualization Linkoping UniversityLinkopingSE-58183Sweden Department of Mathematics Linkoping UniversityLinkopingSE-58183Sweden Department of Computer Science University of CopenhagenCopenhagenDK-2300Denmark Department of Radiology in Linkoping and Department of Health Medicine and Caring SciencesLinkoping UniversityLinkopingSE-58183Sweden

Q-space trajectory imaging(QTI)allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms.A recently proposed constrained estimation framework,called QTI+,improved QTI's resilience to noise and data sparsity,thus increasing the reliability of the method by enforcing relevant positivity constraints.In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model.We show that the additional conditions,which introduce an upper bound on the diffusivity values,further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.

关键词： diffusion diffusion mri q-space trajectory imaging QTI Microstructure Microscopic anisotropy QTI+ Constrained

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The structural connectome in ADHD

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Psychoradiology 2021年第4期1卷 257-271页

作者： Xuan Bu Miao Cao Xiaoqi Huang Yong He State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal UniversityBeijing 100875China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghai 200433China Huaxi MR Research Center West China Hospital of Sichuan UniversityChengdu 610041China Chinese Institute for Brain Research Beijing 102206China

Attention-deficit/hyperactivity disorder(ADHD)has been conceptualized as a brain dysconnectivity disorder.In the past decade,noninvasive diffusion magnetic resonance imaging(dmri)studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome,and that these alterations are associated with core symptoms and cognitive deficits in patients.This review aims to summarize recent dmri-based structural connectome studies in ADHD from voxel-,tractography-,and network-based perspectives.Voxel-and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts,the corpus callosum,the corticospinal tracts,and the cingulum bundle in patients with ADHD.Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes.The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification.These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation.However,given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations,these ADHD-related white matter alterations are still far from informing clinical practice.Future studies with larger and more homogeneous samples are needed to validate the consistency of current results;advanced dmri techniques can help to generatemuchmore precise estimation of whitematter pathways and assure specific fiber configurations;and finally,dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.

关键词： attention-deficit/hyperactivity disorder white matter diffusion mri connectomics

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功能磁共振成像/弥散张量成像图像融合:脑肿瘤治疗术前计划新方法(英文)

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中华临床医师杂志（电子版） 2011年第20期5卷 6021-6026页

作者：王志郭悦陈敏崔壮高鸿周诚 Peking Union Medical College Department of Radiology Beijing Hospital Ministry of Health Department of Nuclear Medicine Beijing Hospital Ministry of Health Department of Neurosurgery Beijing Hospital Ministry of Health Department of Radiotherapy Beijing Hospital Ministry of Health

Objective To develop and test an image fusion software(fDf,fmri/DTI fusion) which can overlap the anatomical(T1WI images),functional mri(fmri) and diffusion tensor imaging(DTI) simultaneously for preoperational evaluation of patients′ tumor.Methods fmri with bilateral hands grasp movement and DTI were performed using GE 1.5 T magnetic resonance system on 10 subjects(5 healthy volunteers and 5 patients with brain tumor,of which 3 were metastases,1 was cavernous hemangioma and 1 was glioma).All data were input to the personal computer and offline postprocessing of fmri and DTI data was performed using SPM5 and VolumeOne software package to visualize the activated functional cortex areas and corticospinal tracts.fDf was used to import the fmri and structural images to VolumeOne to show them simultaneously.Results Brain functional activation maps and diffusion tensor fiber tracking images were obtained in all five healthy volunteers and four patients except one who suffered from left hemiplegia.The functional activation maps and the fiber tracking images were successfully fused by the fDf software,where the activations areas and the white matter fiber were displayed together.The fusion images of healthy volunteers showed the hand motion areas and corresponding corticospinal tracts,while that of the patients display the relationship of the eloquent cortex and peritumoral fiber tracts,which were useful in guiding the treatments for the surgeons and radiotherapists.It only took hours to present the fused images to the neurosurgeons or the radiotherapists.Conclusions All the results confirm that the image fusion software work well for all the data.It is an easy,free and fast way to realize the image fusion.The neurosurgeon and radiotherapist consider the software were very helpful for preoperative planning.

关键词： Functional mri diffusion mri Brain neoplasms

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