scholarly journals Whole brain and deep gray matter structure segmentation: Quantitative comparison between MPRAGE and MP2RAGE sequences

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254597
Author(s):  
Amgad Droby ◽  
Avner Thaler ◽  
Nir Giladi ◽  
R. Matthew Hutchison ◽  
Anat Mirelman ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Neurological Diseases ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Imaging Biomarkers ◽  
Precentral Gyrus ◽  
Tissue Segmentation ◽  
Whole Brain ◽  
Volumetric Assessment ◽  
Total Brain

Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; p<0.0001). Significantly higher total brain GM, and lower cerebrospinal fluid‏ volumes were obtained from MP2RAGE vs. MP-RAGE based on all segmentation methods (p<0.05 in all cases). Whole-brain voxel-wise comparisons revealed higher GM tissue probability in the thalamus, putamen, caudate, lingual gyrus, and precentral gyrus based on MP2RAGE compared with MP-RAGE. Moreover, significantly higher WM probability was observed in the cerebellum, corpus callosum, and frontal-and-temporal regions in MP2RAGE vs. MP-RAGE. Finally, MP2RAGE showed a higher mean percentage of change in total brain GM compared to MP-RAGE. On the other hand, MP-RAGE demonstrated a higher overtime percentage of change in WM and DGM volumes compared to MP2RAGE. Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

scholarly journals Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

2021 ◽  
Author(s):  
Rakshit Dadarwal ◽  
Michael Ortiz-Rios ◽  
Susann Boretius
Keyword(s):  
White Matter ◽  
Brain Tissue ◽  
Brain Structures ◽  
Macaque Monkey ◽  
List Type ◽  
Subcortical Structures ◽  
Tissue Segmentation ◽  
Tissue Contrast ◽  
T1 Contrast ◽  
The Brain

AbstractRecent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter scale subcortical brain structures in humans. QSM reflects the magnetic susceptibility arising from the spatial distribution of iron, myelin, and calcium in the brain. The simultaneous visualization of cortical, subcortical, and white matter structure remains, however, challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortical and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first established QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analyses methods allowed a similar accurate delineation of subcortical structures in humans. Moreover, applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of the brain tissue types as compared to the single T1 contrast. Furthermore, we validate our dual-contrast fusion approach in humans and similarly demonstrate improvements in automated segmentation of cortical and subcortical structures. We believe the proposed contrast will facilitate translational studies in non-human primates to investigate the pathophysiology of neurodegenerative diseases that affect the subcortical structures of the basal ganglia in humans.HighlightsThe subcortical gray matter areas of macaque monkeys are reliably mapped by QSM, much as they are in humans.Combining T1w and QSM images improves the visualization and segmentation of white matter, cortical and subcortical structures in the macaque monkey.The proposed dual contrast TQ-SILiCON provides a similar image quality also in humans.TQ-SILiCON facilitates comparative and translational neuroscience studies investigating subcortical structures.

Gadolinium effect on thalamus and whole brain tissue segmentation

2018 ◽  
Vol 60 (11) ◽  
pp. 1167-1173 ◽  
Author(s):  
Salem Hannoun ◽  
Marwa Baalbaki ◽  
Ribal Haddad ◽  
Stephanie Saaybi ◽  
Nabil K. El Ayoubi ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Tissue Segmentation ◽  
Whole Brain ◽  
Brain Tissue Segmentation

scholarly journals Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3363
Author(s):  
Chaitra Dayananda ◽  
Jae-Young Choi ◽  
Bumshik Lee
Keyword(s):  
Feature Extraction ◽  
Brain Tissue ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Similarity Index ◽  
Medical Image Segmentation ◽  
Model Parameters ◽  
Tissue Segmentation ◽  
Semantic Class ◽  
Multi Scale

In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.

scholarly journals Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

2022 ◽  
Author(s):  
Enrica Cavedo ◽  
Philippe Tran ◽  
Urielle Thoprakarn ◽  
Jean-Baptiste Martini ◽  
Antoine Movschin ◽  
...  
Keyword(s):  
Clinical Trials ◽  
White Matter ◽  
Neurological Diseases ◽  
Automatic Segmentation ◽  
Brain Structures ◽  
Relative Volume ◽  
White Matter Hyperintensity ◽  
Dice Similarity Coefficient ◽  
Analysis Tool ◽  
Clinical Routine

Abstract Objectives QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. Methods Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland–Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. Results The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. Conclusions QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. Key Points • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

scholarly journals Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma

2021 ◽  
Author(s):  
Thomaz R. Mostardeiro ◽  
Ananya Panda ◽  
Robert J. Witte ◽  
Norbert G. Campeau ◽  
Kiaran P. McGee ◽  
...  
Keyword(s):  
White Matter ◽  
Statistical Significance ◽  
Relaxation Times ◽  
Brain Structures ◽  
Centrum Semiovale ◽  
In Vivo Evaluation ◽  
Whole Brain ◽  
Mean Values ◽  
Caudate Head

Abstract Purpose MR fingerprinting (MRF) is a MR technique that allows assessment of tissue relaxation times. The purpose of this study is to evaluate the clinical application of this technique in patients with meningioma. Materials and methods A whole-brain 3D isotropic 1mm3 acquisition under a 3.0T field strength was used to obtain MRF T1 and T2-based relaxometry values in 4:38 s. The accuracy of values was quantified by scanning a quantitative MR relaxometry phantom. In vivo evaluation was performed by applying the sequence to 20 subjects with 25 meningiomas. Regions of interest included the meningioma, caudate head, centrum semiovale, contralateral white matter and thalamus. For both phantom and subjects, mean values of both T1 and T2 estimates were obtained. Statistical significance of differences in mean values between the meningioma and other brain structures was tested using a Friedman’s ANOVA test. Results MR fingerprinting phantom data demonstrated a linear relationship between measured and reference relaxometry estimates for both T1 (r2 = 0.99) and T2 (r2 = 0.97). MRF T1 relaxation times were longer in meningioma (mean ± SD 1429 ± 202 ms) compared to thalamus (mean ± SD 1054 ± 58 ms; p = 0.004), centrum semiovale (mean ± SD 825 ± 42 ms; p < 0.001) and contralateral white matter (mean ± SD 799 ± 40 ms; p < 0.001). MRF T2 relaxation times were longer for meningioma (mean ± SD 69 ± 27 ms) as compared to thalamus (mean ± SD 27 ± 3 ms; p < 0.001), caudate head (mean ± SD 39 ± 5 ms; p < 0.001) and contralateral white matter (mean ± SD 35 ± 4 ms; p < 0.001) Conclusions Phantom measurements indicate that the proposed 3D-MRF sequence relaxometry estimations are valid and reproducible. For in vivo, entire brain coverage was obtained in clinically feasible time and allows quantitative assessment of meningioma in clinical practice.

GSCFN: A Graph Self-Construction and Fusion Network for Semi-supervised Brain Tissue Segmentation in MRI

2021 ◽  
Author(s):  
Yan Zhang ◽  
Yifei Li ◽  
Youyong Kong ◽  
Jiasong Wu ◽  
Jian Yang ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Tissue Segmentation ◽  
Brain Tissue Segmentation

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Visualisation of GABAergic neurons and synapses in the rat brain using immunohistochemistry for two forms of glutamate decarboxylase

2021 ◽  
Vol 21 (2) ◽  
pp. 63-73
Author(s):  
Valeria A. Razenkova ◽  
Dmitrii E. Korzhevskii
Keyword(s):  
Brain Tissue ◽  
Glutamate Decarboxylase ◽  
Brain Structures ◽  
Gabaergic Neurons ◽  
Laboratory Animals ◽  
Tissue Samples ◽  
Tissue Sections ◽  
Background Staining ◽  
The Central Nervous System ◽  
Rat Forebrain

BACKGROUND: Taking into account the importance of GABAergic brain system research and also the opportunity to achieve specific and accurate results in laboratory studies using immunohistochemical approaches, it seems important to have a reliable method of visualization GABA-synthesizing cells, their projections and synapses, for the morphofunctional analysis of GABAergic system both in normal conditions and in the experimental pathology. AIM: The aim of the study was to visualize analyze GABAergic neurons and synapses within rats brain using three different antibody types against glutamate decarboxylase and to identify the optimal conditions for reaction performing. MATERIALS AND METHODS: The study was performed on paraffin brain tissue sections of 5 adult Wistar rats. Immunohistochemical reactions using three antibody types against glutamate decarboxylase isoform 67 (GAD67) and glutamate decarboxylase isoform 65 (GAD65) were performed. Additional controls on C57/Bl6 mice and Chinchilla rabbits brain samples were also carried out. RESULTS: Antibodies used in the research made it possible to achieve high quality of GABAergic structures visualizing without increasing background staining. At the same time different antibody types are distinct in their efficacy to perform immunohistochemistry reaction on laboratory animal brain tissue samples. By performing additional controls, we discovered that there is necessary to adsorb secondary reagents immunoglobulins in order to eliminate nonspecific staining. It was found that GAD67 and GAD65 distribution in rat forebrain structures is different. It was stated that GAD67 immunohistochemistry most completely reveals GABAergic brain structures compared to GAD65 immunhistochemistry. The possibility of determining morphological features of GABAergic neurons and synaptic terminals, as well as performing quantitative analysis, was demonstrated. CONCLUSIONS: The approach proposed makes it possible to specifically visualize GABAergic structures of the central nervous system of different laboratory animals. This could be useful both in fundamental studies and in pathology research.

Sign in / Sign up

Export Citation Format

Share Document