Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan

Siless, Viviana; Davidow, Juliet Y.; Nielsen, Jared A.; Fan, Qiuyun; Hedden, Trey; Hollinshead, Marisa O.; Beam, Elizabeth; Bustamante, Constanza M. Vidal; Garrad, Megan C.; Santillana, Rosario M.; Smith, Emily E.; Hamadeh, Aya; Snyder, Jenna; Drews, Monika; Dijk, Koene R. A. Van; Sheridan, Margaret A.; Somerville, Leah H.; Yendiki, Anastasia

doi:10.1016/j.neuroimage.2020.116703

Cited by 16 publications

(9 citation statements)

References 94 publications

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“…erefore, how to extract the contour of the moving object efficiently and reasonably and recognize the corresponding gait of the moving object completely is the key and hotspot of human motion tracking. e conventional gait recognition technology mainly depends on the effective analysis of the moving target image sequence, which can analyze and study the moving target in detail at three levels, namely, moving target segmentation technology, feature extraction technology, and corresponding recognition and classification technology [10][11][12]. In the whole operation process of the algorithm, firstly, the relevant camera will get the moving image sequence of the moving object, segment the corresponding moving sequence, extract the changing background image from the corresponding sequence image, and describe and analyze its characteristics.…”

Section: Introductionmentioning

confidence: 99%

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Feng

Gao

2021

Complexity

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With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

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Section: Introductionmentioning

confidence: 99%

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Feng

Gao

2021

Complexity

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“…While specific neural functions tend to be lateralized to one side of the brain, the structure of WM in healthy subjects is similar in both hemispheres—the two sides are enantiomorphically related. Morphometric variance between both hemispheres have been shown to be smaller than the differences between subjects [ 37 , 38 ], and dMRI tractography inter-hemispheric variance seems to be about the same as inter-subject variance [ 20 ] in healthy subjects.…”

Section: Methodsmentioning

confidence: 99%

“…As it is not possible to obtain a groundtruth connectome for lesioned brains, the changes in connectivity when including the FWM are compared to an age-and gender-matched healthy control group, as well as to the individual contralateral hemisphere. As inter-hemispheric diffusion MRI variance has been shown to be comparable to the inter-subject variance in healthy subjects [20], we assumed that an increase in the inter-hemisphere as well in the inter-subject similarity of the non-lesioned brains would be a first indicator of the plausibility of tractography findings. Comparing tumor patients to the control group, the lesions should cause stronger diversions from the mean connectome.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients

Weninger

Jütten

et al. 2020

PLoS ONE

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Diffusion-weighted MRI makes it possible to quantify subvoxel brain microstructure and to reconstruct white matter fiber trajectories with which structural connectomes can be created. However, at the border between cerebrospinal fluid and white matter, or in the presence of edema, the obtained MRI signal originates from both the cerebrospinal fluid as well as from the white matter partial volume. Diffusion tractography can be strongly influenced by these free water partial volume effects. Thus, including a free water model can improve diffusion tractography in glioma patients. Here, we analyze how including a free water model influences structural connectivity estimates in healthy subjects as well as in brain tumor patients. During a clinical study, we acquired diffusion MRI data of 35 glioma patients and 28 age-and sex-matched controls, on which we applied an open-source deep learning based free water model. We performed deterministic as well as probabilistic tractography before and after free water modeling, and utilized the tractograms to create structural connectomes. Finally, we performed a quantitative analysis of the connectivity matrices. In our experiments, the number of tracked diffusion streamlines increased by 13% for high grade glioma patients, 9.25% for low grade glioma, and 7.65% for healthy controls. Intra-subject similarity of hemispheres increased significantly for the patient as well as for the control group, with larger effects observed in the patient group. Furthermore, inter-subject differences in connectivity between brain tumor patients and healthy subjects were reduced when including free water modeling. Our results indicate that free water modeling increases the similarity of connectivity matrices in brain tumor patients, while the observed effects are less pronounced in healthy subjects. As the similarity between brain tumor patients and healthy controls also increased, connectivity changes in brain tumor patients may have been overestimated in studies that did not perform free water modeling.

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“…There are also learning-based segmentation approaches that train a model from the reference tract segmentation data and predict an anatomical label for each streamline in a new subject [179,246,178,243,508,533,269,504]. To reduce the amount of labeling for each streamline, many streamline labeling methods first group streamlines into clusters (known as fiber clustering), followed by assigning an anatomical label for each cluster, thus labeling each streamline [315,551,261,176,366,76,458,524,365,518,158,540,408,16,472,409,498].…”

Section: Anatomical Tract Identificationmentioning

confidence: 99%

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

Zhang¹,

Daducci²,

He³

et al. 2021

Preprint

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Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

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Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan

Cited by 16 publications

References 94 publications

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Analyzing the effects of free water modeling by deep learning on diffusion MRI structural connectivity estimates in glioma patients

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review

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