T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions—an MRI evaluation study

Beller, Ebba; Keeser, Daniel; Wehn, Antonia; Malchow, Berend; Karali, Temmuz; Schmitt, Aurore; Papazova, Irina; Papazov, Boris; Schoeppe, Franziska; Figueiredo, Giovanna Negrão de; Ertl‐Wagner, Birgit; Stoecklein, Sophia

doi:10.1007/s00234-018-2121-2

Cited by 8 publications

(11 citation statements)

References 24 publications

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“…T1-weighted images of the brain were not acquired in the KORA-MRI study due to time constraints arising from the whole-body MRI protocol. We have therefore validated FLAIR-based brain volumetry in a recently published study by Beller et al that assessed the feasibility of this approach by correlating the results of FLAIR-based and T1-based volumetry results of 116 atlas regions in 30 healthy individuals 13 .…”

Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

“…Processing of the FLAIR brain MRI data for the present analysis followed the steps described in the above mentioned method paper on the feasibility of FLAIR-based GM volumetry 13 : See Fig. 2 for an overview of the general approach.…”

Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

“…For the analyses of determinants of GMV in the present study, only GM structures that showed a significant correlation between the volumetric results of T1-versus FLAIR-based imaging in the abovementioned validation study 13 were included. Beller et al considered a Spearman rank correlation coefficient ≥0.597, corresponding to a p-value < 0.0005 between T1-and FLAIR-based GM volume as significant (please see Supplementary Table 1 for an overview of the structures used in the current study) 13 .…”

Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

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Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

Galiè

Rospleszcz

Keeser

et al. 2020

Sci Rep

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To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p < 0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders. A plethora of factors such as cardiovascular, metabolic and lifestyle parameters influence gray matter (GM) volume (GMV) of the human brain, with partially overlapping and intercorrelated and partially differential effects. Among all the potential determinants of GMV, age is probably the best-established factor. There is consensus that a certain decrease of GMV can be considered as a normal function of aging 1,2. As such, it is common practice

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Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

Section: Structural Brain Mri Processing and Volumetrymentioning

confidence: 99%

See 1 more Smart Citation

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

Galiè

Rospleszcz

Keeser

et al. 2020

Sci Rep

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“…Более того, в случаях хронического метаболического заболевания или воспалительного поражения центральной нервной системы новые острые очаги могут возникать на фоне хронического процесса, что дополнительно затрудняет интерпретацию нейровизуализационных находок [5]. С точки зрения морфологии, наличие отека и Т1-гадолиниевого усиления предполагает наличие воспалительного и/или инфекционного или неопластического процесса, в то время как масс-эффект является более типичным для последнего [6].…”

Section: обзорыunclassified

“…Ишемические и септические эмболы обычно проникают по артериальному кровотоку настолько далеко, насколько это допускает их размер, что часто обусловливает поражение небольших по калибру артерий ствола мозга [5]. Пограничными зонами артериальных терминалей являются покрышка мозга, продолговатый мозг и мост; у новорожденных эти части ствола считаются самыми восприимчивыми к гипоксически-ишемическому поражению [6,7]. Венозная система ствола отличается сложностью и большим количеством сосудов, но хорошо визуализируется с применением протокола SWI (Susceptibility Weighted Imaging -изображения, взвешенные по магнитной восприимчивости) [5].…”

Section: обзорыunclassified

Mri as a Tool of Differential Diagnostic in the Brainstem Damage in Children

Marchenko

Викторовна²,

Войтенков

et al. 2020

Journal of Clinical Practice

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Summary. In this review magnetic resonance tomography (MRI) is presented as the tool for differential diagnostic in the brainstem damage in children. It is shown that the isolated or connected with supratentorial changes brainstems lesion occurs within a wide range of pathological conditions. Small size, less clear distinction between gray and white matter and the limit of visualization infratentorial structures with multiparametric MRI often lead to difficulties in the differential diagnosis. Sometimes the most important in the diagnostic is the topography of lesions and their signal intensity. Clinical data, the results of epidemiological and laboratory studies are primary importance for differential diagnosis of brainstem damages.

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Hepatic fat is superior to BMI, visceral and pancreatic fat as a potential risk biomarker for neurodegenerative disease

et al. 2019

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Objectives Prior studies relating body mass index (BMI) to brain volumes suggest an overall inverse association. However, BMI might not be an ideal marker, as it disregards different fat compartments, which carry different metabolic risks. Therefore, we analyzed MR-based fat depots and their association with gray matter (GM) volumes of brain structures, which show volumetric changes in neurodegenerative diseases. Methods Warp-based automated brain segmentation of 3D FLAIR sequences was obtained in a population-based study cohort. Associations of temporal lobe, cingulate gyrus, and hippocampus GM volume with BMI and MR-based quantification of visceral adipose tissue (VAT), as well as hepatic and pancreatic proton density fat fraction (PDFF hepatic and PDFF panc , respectively), were assessed by linear regression. Results In a sample of 152 women (age 56.2 ± 9.0 years) and 199 men (age 56.1 ± 9.1 years), we observed a significant inverse association of PDFF hepatic and cingulate gyrus volume (p < 0.05) as well as of PDFF hepatic and hippocampus volume (p < 0.05), when adjusting for age and sex. This inverse association was further enhanced for cingulate gyrus volume after additionally adjusting for hypertension, smoking, BMI, LDL, and total cholesterol (p < 0.01) and also alcohol (p < 0.01). No significant association was observed between PDFF hepatic and temporal lobe and between temporal lobe, cingulate gyrus, or hippocampus volume and BMI, VAT, and PDFF panc . Conclusions We observed a significant inverse, independent association of cingulate gyrus and hippocampus GM volume with hepatic fat, but not with other obesity measures. Increased hepatic fat could therefore serve as a marker of high-risk fat distribution. Key Points• Obesity is associated with neurodegenerative processes.• In a population-based study cohort, hepatic fat was superior to BMI and visceral and pancreatic fat as a risk biomarker for decreased brain volume of cingulate gyrus and hippocampus. • Increased hepatic fat could serve as a marker of high-risk fat distribution.

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T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions—an MRI evaluation study

Cited by 8 publications

References 24 publications

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

Mri as a Tool of Differential Diagnostic in the Brainstem Damage in Children

Hepatic fat is superior to BMI, visceral and pancreatic fat as a potential risk biomarker for neurodegenerative disease

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