A Statistical Cerebroarterial Atlas Derived from 700 MRA Datasets

Forkert, Nils D.; Fiehler, Jens; Suniaga, Santiago; Wersching, H.; Knecht, Stefan; Kemmling, André

doi:10.3414/me13-02-0001

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…Therefore, the Montreal Neurological Institute (MNI) brain atlas is registered to the average baseline perfusion image using an affine registration. After this, a segmentation in MNI reference space that contains typical locations of the MCA and ICA including a safety margin, which were determined using a previously generated statistical cerebrovascular atlas [12], is transformed to the average baseline perfusion image. The corresponding concentration time curves within this segmentation are separated into arterial and non-arterial signals using a k-means clustering approach, whereas the concentration time curves from the cluster with the earlier time-to-peak and higher peak are averaged to a final arterial input function using a geometrically correct method.…”

Section: Image Processingmentioning

confidence: 99%

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

et al. 2020

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IntroductionIn recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction. Materials and methodsAcute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric. OPEN ACCESS Citation: Grosser M, Gellißen S, Borchert P, Sedlacik J, Nawabi J, Fiehler J, et al. (2020) Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features. PLoS ONE 15(1): e0228113.

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Section: Image Processingmentioning

confidence: 99%

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

et al. 2020

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“…[20][21][22] On the basis of the resulting vessel segmentation, the 3D vessel centerline representation and the corresponding vessel radius for each voxel of the vessel centerline were then calculated by using the method described by Forkert et al (Figure). 23 Due to different FOVs of the TOF-MRA acquisition and to enable an analysis of the regional vascular alterations, parcellation of each brain into anatomic substructures was required. First, the well-established Montreal Neurological Institute (MNI) adult brain atlas 24 was registered to the MNI pediatric atlas (7-11 years of age) 25 by optimizing the nonlinear transformation A .…”

Section: Data Analysesmentioning

confidence: 99%

Brain Structural and Vascular Anatomy Is Altered in Offspring of Pre-Eclamptic Pregnancies: A Pilot Study

Rätsep¹,

Paolozza

Hickman³

et al. 2015

American Journal of Neuroradiology

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BACKGROUND AND PURPOSE:Pre-eclampsia is a serious clinical gestational disorder occurring in 3%-5% of all human pregnancies and characterized by endothelial dysfunction and vascular complications. Offspring born of pre-eclamptic pregnancies are reported to exhibit deficits in cognitive function, higher incidence of depression, and increased susceptibility to stroke. However, no brain imaging reports exist on these offspring. We aimed to assess brain structural and vascular anatomy in 7-to 10-year-old offspring of pre-eclamptic pregnancies compared with matched controls.

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“…and (2) that registration often fails to properly align the vessel trees at smaller branches and thus necessitates many subjects (or samples) to minimize misregistration steps. This could, at least in part, explain the low number of probabilistic atlases of brain arteries (Dufour et al, 2011;Nils Daniel Forkert et al, 2012) and veins (N. D. Forkert et al, 2013;Ward et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The morphology of the human cerebrovascular system

Bernier

Cunnane

Whittingstall

2018

Human Brain Mapping

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While several methodologies exist for quantifying gray and white matter properties in humans, relatively little is known regarding the spatial organization and the intersubject variability of cerebral vessels. To resolve this, we developed a fast, open‐source processing algorithm using advanced vessel segmentation schemes and iterative nonlinear registration to isolate, extract, and quantify cerebral vessels in susceptibility weighting imaging (SWI) and time‐of‐flight angiography (TOF‐MRA) datasets acquired in a large cohort (n = 42) of healthy individuals. From this, whole‐brain venous and arterial probabilistic maps were generated along with the computation of regional densities and diameters within regions based on popular anatomical and functional atlases. The results show that cerebral vasculature is highly heterogeneous, displaying disproportionally large vessel densities in brain areas such as the anterior and posterior cingulate, cuneus, precuneus, parahippocampus, insula, and temporal gyri. On average, venous densities were slightly higher and less variable across subjects than arterial. Moreover, regional variations in both venous and arterial density were significantly correlated to cortical thickness (R = 0.42). This publicly available new atlas of the human cerebrovascular system provides a first step toward quantifying morphological changes in the diseased brain and serving as a potential regression tool in fMRI analysis.

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A Statistical Cerebroarterial Atlas Derived from 700 MRA Datasets

Abstract: The presented cerebroarterial atlas seems useful for improving the understanding about normal variations of cerebral arteries, initialization of cerebrovascular segmentation methods and may even lay the foundation for a reliable quantification of subtle morphological vascular changes.

Cited by 17 publications

References 21 publications

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

Brain Structural and Vascular Anatomy Is Altered in Offspring of Pre-Eclamptic Pregnancies: A Pilot Study

The morphology of the human cerebrovascular system

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