Podcasts about t1w

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.30.534950v1?rss=1 Authors: Saleem, K. S., Avram, A. V., Yen, C. C.-C., Magdoom, K. N., Schram, V., Basser, P. J. Abstract: Subcortical nuclei and other deep brain structures play essential roles in regulating the central and peripheral nervous systems. However, many of these nuclei and their subregions are challenging to identify and delineate in conventional MRI due to their small size, hidden location, and often subtle contrasts compared to neighboring regions. To address these limitations, we scanned the whole brain of the marmoset monkeys in ex vivo using a clinically feasible diffusion MRI method, called the mean apparent propagator (MAP)-MRI, along with T2W and MTR (T1-like contrast) images acquired at 7 Tesla. Additionally, we registered these multimodal MRI volumes to the high-resolution images of matched whole-brain histology sections with seven different stains obtained from the same brain specimens. At high spatial resolution, the microstructural parameters and fiber orientation distribution functions derived with MAP-MRI can distinguish the subregions of many subcortical and deep brain structures, including fiber tracts of different sizes and orientations. The good correlation with multiple but distinct histological stains from the same brain serves as a thorough validation of the structures identified with MAP-MRI and other MRI parameters. Moreover, the anatomical details of deep brain structures found in the volumes of MAP-MRI parameters are not visible in conventional T1W or T2W images. The high-resolution mapping using novel MRI contrasts, combined and correlated with histology, can elucidate structures that were previously invisible radiologically. Thus, this multimodal approach offers a roadmap toward identifying salient brain areas in vivo in future neuroradiological studies. It also provides a useful anatomical standard reference for the region definition of subcortical targets and the generation of a 3D digital template atlas for the marmoset brain research (Saleem et al., 2023). Additionally, we conducted a cross-species comparison between marmoset and macaque monkeys using results from our previous studies (Saleem et al., 2021). We found that the two species had distinct patterns of iron distribution in subregions of the basal ganglia, red nucleus, and deep cerebellar nuclei, confirmed with T2W MRI and histology. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.14.532686v1?rss=1 Authors: Kim, Y., Joshi, A. A., Choi, S., Joshi, S. H., Bhushan, C., Varadarajan, D., Haldar, J. P., Leahy, R. M., Shattuck, D. W. Abstract: There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.15.528705v1?rss=1 Authors: Chattopadhyay, T., Ozarkar, S. S., Buwa, K., Thomopoulos, S. I., Thompson, P. M. Abstract: Abnormal {beta}-amyloid accumulation in the brain is an early indicator of Alzheimer's disease and practical tests could help identify patients who could respond to treatment, now that promising anti-amyloid drugs are available. Even so, amyloid positivity (A{beta}+) is assessed using PET or CSF assays, both highly invasive procedures. Here, we investigate how well A{beta}+ can be predicted from T1 weighted brain MRI and gray matter, white matter and cerebrospinal fluid segmentations from T1-weighted brain MRI (T1w), a less invasive alternative. We used 3D convolutional neural networks to predict A{beta}+ based on 3D brain MRI data, from 762 elderly subjects (mean age: 75.1 yrs. {+/-} 7.6SD; 394F/368M; 459 healthy controls, 67 with MCI and 236 with dementia) scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We also tested whether the accuracy increases when using transfer learning from the larger UK Biobank dataset. Overall, the 3D CNN predicted A{beta}+ with 76% balanced accuracy from T1w scans. The closest performance to this was using white matter maps alone when the model was pre-trained on an age prediction in the UK Biobank. The performance of individual tissue maps was less than the T1w, but transfer learning helped increase the accuracy. Although tests on more diverse data are warranted, deep learned models from standard MRI show initial promise for A{beta}+ estimation, before considering more invasive procedures. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.02.522457v1?rss=1 Authors: Baratz, Z., Assaf, Y. Abstract: Magnetic resonance imaging (MRI) is a powerful tool for non-invasive imaging of the human body. However, the quality and reliability of MRI data can be influenced by various factors, such as hardware and software configurations, image acquisition protocols, and preprocessing techniques. In recent years, the introduction of large-scale neuroimaging datasets has taken an increasingly prominent role in neuroscientific research. The advent of publicly available and standardized repositories has enabled researchers to combine data from multiple sources to explore a wide range of scientific inquiries. This increase in scale allows the study of phenomena with smaller effect sizes over a more diverse sample and with greater statistical power. Other than the variability inherent to the acquisition of the data across sites, preprocessing and feature generation steps implemented in different labs introduce an additional layer of variability which may influence consecutive statistical procedures. In this study, we show that differences in the configuration of surface reconstruction from anatomical MRI using FreeSurfer results in considerable changes to the estimated anatomical features. In addition, we demonstrate the effect these differences have on within-subject similarity and the performance of basic prediction tasks based on the derived anatomical features. Our results show that although FreeSurfer may be provided with either a T2w or a FLAIR scan for the same purpose of improving pial surface estimation (relative to based on the mandatory T1w scan alone), the two configurations have a distinctly different effect. In addition, our findings indicate that the similarity of within-subject scans and performance of a range of models for the prediction of sex and age are significantly effected, they are not significantly improved by either of the enhanced configurations. These results demonstrate the large extent to which elementary and sparsely reported differences in preprocessing workflow configurations influence the derived brain features. The results of this study are meant to underline the importance of optimizing preprocessing procedures based on experimental results prior to their distribution and consecutive standardization and harmonization efforts across public datasets. In addition, preprocessing configurations should be carefully reported and included in any following analytical workflows, to account for any variation originating from such differences. Finally, other representations of the raw data should be explored and studied to provide a more robust framework for data aggregation and sharing. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.18.520922v1?rss=1 Authors: Orooji, F., Butler, R. Abstract: We apply deep learning to the problem of segmenting the arterial system from T1w and T2w images. We use the freely available 7-Tesla 'forrest' dataset from OpenNeuro, (which contains TOF, T1w, and T2w) and use supervised learning with T1w or T2w as input, and TOF segmentation as ground truth, to train a Unet architecture capable of segmenting arteries and quantifying arterial diameters from T1w or T2w images alone. We demonstrate arterial segmentations from both T1w and T2w images, and show that T2w images have sufficient vessel contrast to estimate arterial diameters comparable to those estimated from TOF. We then apply our Unet to T2w images from a separate dataset (IXI) and show our model generalizes to images acquired at different field strength. We consider this work proof-of concept that arterial segmentations can be derived from MRI sequences with poor contrast between arteries and surrounding tissue (T1w and T2w), due to the ability of deep convolutional networks to extract complex features based on local image intensity. Future work will focus on improving the generalizability of the network to non forrest datasets, with the eventual goal of leveraging the entire pre-existing corpus of neuroimaging data for study of human cerebrovasculature. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.16.511844v1?rss=1 Authors: Schira, M. M., Isherwood, Z. J., Kassem, M. S., Barth, M., Shaw, T. B., Roberts, M. M., Paxinos, G. Abstract: We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm3 isotropic resolution for T1w, T2w and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus - difficult or often impossible to identify using standard MRI protocols, can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (www.hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high quality individual brain, this serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical and education settings. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.19.304758v1?rss=1 Authors: He, J., Zhang, F., Xie, G., Yao, S., Feng, Y., Bastos, D. C. A., Rathi, Y., Makris, N., Kikinis, R., Golby, A. J., O'Donnell, L. J. Abstract: The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.28.121343v1?rss=1 Authors: Bussy, A., Plitman, E., Patel, R., Tullo, S., Salaciak, A., Bedford, S., Farzin, S., Beland, M.-L., Valiquette, V., Kazazian, C., Tardif, C., Devenyi, G., Chakravarty, M. Abstract: The hippocampus has been extensively studied in various neuropsychiatric disorders throughout the lifespan. However, inconsistent results have been reported with respect to which subfield volumes are most related to age. Here, we investigate whether these discrepancies may be explained by experimental design differences that exist between studies. Multiple datasets were used to collect 1690 magnetic resonance scans from healthy individuals aged 18-95 years old. Standard T1-weighted (T1w; MPRAGE sequence, 1 mm3 voxels), high-resolution T2-weighted (T2w; SPACE sequence, 0.64 mm3 voxels) and slab T2-weighted (Slab; 2D turbo spin echo, 0.4 x 0.4 x 2 mm3 voxels) images were acquired. The MAGeT Brain algorithm was used for segmentation of the hippocampal grey matter (GM) subfields and peri-hippocampal white matter (WM) subregions. Linear mixed-effect models and Akaike information criterion were used to examine linear, second or third order natural splines relationship between hippocampal volumes and age. We demonstrated that stratum radiatum/lacunosum/moleculare and fornix subregions expressed the highest relative volumetric decrease, while the cornus ammonis 1 presented a relative volumetric preservation of its volume with age. We also found that volumes extracted from slab images were often underestimated and demonstrated different age-related relationships compared to volumes extracted from T1w and T2w images. The current work suggests that although T1w, T2w and slab derived subfield volumetric outputs are largely homologous, modality choice plays a meaningful role in the volumetric estimation of the hippocampal subfields. Copy rights belong to original authors. Visit the link for more info

On Episode 113 Totemlydrunk, Anura, and Booger break down the opening week of the 2020 season. Join us on Discord: https://discord.me/owlnshow    Around The League Overwatch League Stats Lab Charge cut ties with T1w   Fact or Fiction Xzi will be in contention for rookie of the year Valiant actually a good team and Dallas dropped the ball  After a record setting performance against the Fuel on Ana, Architect will continue to get the starting nod over Viol2t   Main Discussion Opening Week Stories Rein/D.Va dominates the frontline; Rein/Orisa aids more defensive approach Valiant cash in their scrimbucks to topple Dallas A battle of two cities - Fuel drop two at home; NYXL win out Gladiators push Titans to the brink; the king is back Philadelphia Homestand Preview SAT 1PM PT Florida Mayhem vs Houston Outlaws SAT 3PM PT Washington Justice vs Philadelphia Fusion SUN 1PM PT Washington Justice vs Houston Outlaws SUN 3PM PT Florida Mayhem vs Philadelphia Fusion   Closing Notes Reviews Show some support for the show by giving us a review over on iTunes or your podcasting app of choice. Any feedback or constructive criticism is always welcomed. It’ll offer us insight and how we can make for a better listening experience for you.   OVERWATCH RECALL You can find a listing of several Overwatch, Overwatch League, and Path to Pro podcasts over on our website at http://owlnshow.com/owrecall. Episodic listings are released every Sunday. Be sure to follow Overwatch Recall on twitter @owrecall    SUPPORT US VIA PATREON  We have created a Patreon page for everyone out there who would like to support our Overwatch podcast network and help it grow and get better. We're offering 4 different tiers starting at $1. Learn more by visiting http://patreon.com/owlnetwork   Where can you find us?   Email the show at contact@owlnshow.com Twitter/Instagram: @owlnshow Website: www.owlnshow.com YouTube:  https://www.youtube.com/c/OverwatchLeagueNetwork Discord: https://discord.me/owlnshow  Patreon:  https://www.patreon.com/owlnetwork Twitch: https://twitch.tv/owlnshow  - We are a Twitch affiliate now. You can show your support for our network by subscribing to the channel! Overwatch League Network Mondays 7PM PT OWL By The Numbers (on hiatus til 2020 season) Tuesdays 6PM PT  Heroes Never Die (Variety Overwatch) Wednesdays 5PM PT Host streams Flex   Totemlydrunk Twitter @totemlydrunkctr Twitch https://www.twitch.tv/totemlydrunk Anura Twitter @AnuraOW Booger Twitter @BoogerBrainzz Twitch https://www.twitch.tv/BoogerBrainzz 

Join Totemlydrunk and Edanar on Episode 167 as we discuss the latest in Overwatch, Overwatch League, hot community topics, and more! On the show we discuss the new anti-cheat notifications, the new LEGO sets due out in October, 1v1 battles for the Atlanta Homestand, daily fantasy Overwatch League, OWL post game discussion, and more! As always we would love to hear your feedback. Feel free to shoot us a message via email, discord, or twitter. Thanks again for tuning in! Join us on Discord: https://discord.me/owlnshow  Daily fantasy: https://www.fantasyowl.com (Every Sunday we have a featured league for our network show OWL By The Numbers)   The News Twitch Rivals workshop tournament New anti-cheat system LEGO unveils new Overwatch sets   Hot Community Topics Bren vs Babybay; on-air trash talk Dafran vs Mangachu Torb hammer 1v1 this Sunday Daily fantasy OWL is here!   Inside The Path to Pro & OWL Fissure retires Toronto Defiant sign Logix / Toronto Defiant sign Mangachu MVP JJONAK skin now available Guangzhou Charge acquire T1w for academy Hangzhou Spark send 2 to academy New hometown merchandise hits store   Main Discussion Stage 3 Week 4 discussion Stage 3 Week 5 (Atlanta Homestand) preview   Closing Notes Reviews Show some support for the show by giving us a review over on iTunes or your podcasting app of choice. Any feedback or constructive criticism is always welcomed. It’ll offer us insight and how we can make for a better listening experience for you.    OVERWATCH RECALL You can find a listing of several Overwatch, Overwatch League, and Path to Pro podcasts over on our website at http://owlnshow.com/owrecall. Episodic listings are released every Sunday. Be sure to follow Overwatch Recall on twitter @owrecall -    SUPPORT US ON PATREON! We have created a Patreon page for everyone out there who would like to support our Overwatch podcast network and help it grow and get better. We're offering 4 different tiers starting at $1. Learn more by visiting http://patreon.com/owlnetwork There's no pressure to pledge and we thank you for your continued support!   Where can they find us? Email the show at hndoverwatch@gmail.com Website: owlnshow.com Twitter: @hndoverwatch Twitch: https://twitch.tv/owlnshow -  We’re a Twitch affiliate! You can support the show by subscribing to our twitch channel and unlock our network emoticon. Thanks again to everyone who has been watching our shows live and interacting with the chat. If you’d like to catch us live we’re broadcasting 3 nights a week. OWLN Mon 7PM PT, OWL By The Numbers Tues 6PM PT, HND Wed 5PM PT. Discord: https://discord.me/owlnshow YouTube: https://www.youtube.com/c/OverwatchLeagueNetwork  Totem  Twitter @totemlydrunkctr Twitch https://www.twitch.tv/totemlydrunk Edanar Twitter @edanaroverwatch Twitch https://www.twitch.tv/edanarow   

Isaac and Mark review one of New York's rising promotions Tier 1 Wrestling on their second show ever "Rumble in the Concrete Jungle" and see if T1W lives up to the hype.

Isaac and Mark review one of New York's rising promotions Tier 1 Wrestling on their second show ever "Rumble in the Concrete Jungle" and see if T1W lives up to the hype.

Background: To determine if black-blood 3 T cardiovascular magnetic resonance (bb-CMR) can depict differences between symptomatic and asymptomatic carotid atherosclerotic plaques in acute ischemic stroke patients. Methods: In this prospective monocentric observational study 34 patients (24 males; 70 +/- 9.3 years) with symptomatic carotid disease defined as ischemic brain lesions in one internal carotid artery territory on diffusion weighted images underwent a carotid bb-CMR at 3 T with fat-saturated pre- and post-contrast T1w-, PDw-, T2w- and TOF images using surface coils and Parallel Imaging techniques (PAT factor = 2) within 10 days after symptom onset. All patients underwent extensive clinical workup (lab, brain MR, duplex sonography, 24-hour ECG, transesophageal echocardiography) to exclude other causes of ischemic stroke. Prevalence of American Heart Association lesion type VI (AHA-LT6), status of the fibrous cap, presence of hemorrhage/thrombus and area measurements of calcification, necrotic core and hemorrhage were determined in both carotid arteries in consensus by two reviewers who were blinded to clinical information. McNemar and Wilcoxon's signed rank tests were use for statistical comparison. A p-value

Over the course of our “Theology On Tap” series we heard talks from Fr. Matt Spahr, Cy Kellett and Dr. Karen Saroki. This Thursday the series was brought to a close by the ever-popular Jackie Francois. Actually, Jackie wasn’t the only one who spoke that night, being supported by her boyfriend Bobby Angel: Jackie Francois is a full-time, travelling worship leader and speaker from Orange County. Bobby Angel, her

Background: Previously proposed classifications for carotid plaque and cerebral parenchymal hemorrhages are used to estimate the age of hematoma according to its signal intensities on T1w and T2w MR images. Using these classifications, we systematically investigated the value of cardiovascular magnetic resonance (CMR) in determining the age of vessel wall hematoma (VWH) in patients with spontaneous cervical artery dissection (sCAD). Methods: 35 consecutive patients (mean age 43.6 +/- 9.8 years) with sCAD received a cervical multi-sequence 3T CMR with fat-saturated black-blood T1w-, T2w- and TOF images. Age of sCAD was defined as time between onset of symptoms (stroke, TIA or Horner's syndrome) and the CMR scan. VWH were categorized into hyperacute, acute, early subacute, late subacute and chronic based on their signal intensities on T1w- and T2w images. Results: The mean age of sCAD was 2.0, 5.8, 15.7 and 58.7 days in patients with acute, early subacute, late subacute and chronic VWH as classified by CMR (p < 0.001 for trend). Agreement was moderate between VWH types in our study and the previously proposed time scheme of signal evolution for cerebral hemorrhage, Cohen's kappa 0.43 (p < 0.001). There was a strong agreement of CMR VWH classification compared to the time scheme which was proposed for carotid intraplaque hematomas with Cohen's kappa of 0.74 (p < 0.001). Conclusions: Signal intensities of VWH in sCAD vary over time and multi-sequence CMR can help to determine the age of an arterial dissection. Furthermore, findings of this study suggest that the time course of carotid hematomas differs from that of cerebral hematomas.

Background: Most of the carotid plaque MR studies have been performed using black-blood protocols at 1.5 T without parallel imaging techniques. The purpose of this study was to evaluate a multi-sequence, black-blood MR protocol using parallel imaging and a dedicated 4-channel surface coil for vessel wall imaging of the carotid arteries at 3 T. Materials and methods: 14 healthy volunteers and 14 patients with intimal thickening as proven by duplex ultrasound had their carotid arteries imaged at 3 T using a multi-sequence protocol (time-of-flight MR angiography, pre-contrast T1w-, PDw- and T2w sequences in the volunteers, additional post-contrast T1w- and dynamic contrast enhanced sequences in patients). To assess intrascan reproducibility, 10 volunteers were scanned twice within 2 weeks. Results: Intrascan reproducibility for quantitative measurements of lumen, wall and outer wall areas was excellent with Intraclass Correlation Coefficients >0.98 and measurement errors of 1.5%, 4.5% and 1.9%, respectively. Patients had larger wall areas than volunteers in both common carotid and internal carotid arteries and smaller lumen areas in internal carotid arteries (p < 0.001). Positive correlations were found between wall area and cardiovascular risk factors such as age, hypertension, coronary heart disease and hypercholesterolemia (Spearman's r = 0.45-0.76, p < 0.05). No significant correlations were found between wall area and body mass index, gender, diabetes or a family history of cardiovascular disease. Conclusion: The findings of this study indicate that high resolution carotid black-blood 3 T MR with parallel imaging is a fast, reproducible and robust method to assess carotid atherosclerotic plaque in vivo and this method is ready to be used in clinical practice.