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1,152
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Dataset results
1,152 results for “Magnetic resonance imaging”
Magnetic Resonance Imaging in Patients with Trigeminal Neuralgia
ClinicalTrials.gov study NCT06853119. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Upright Magnetic Resonance Imaging (MRI) Study - Breast Implant Shell Geometry and Edge Scalloping
ClinicalTrials.gov study NCT01238601. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Functional Magnetic Resonance Imaging (fMRI) Study in Overweight and Obese Men (0000-103)
ClinicalTrials.gov study NCT00914212. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Whole Body Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in Multiple Myeloma
ClinicalTrials.gov study NCT01829412. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Magnetic Resonance Imaging for Better Selection of Pancreatic Cancer Patients for Surgery: A Randomized Clinical Trial
ClinicalTrials.gov study NCT05253313. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Characterize Tumor Hypoxia by Magnetic Resonance Imaging
ClinicalTrials.gov study NCT03987568. IPD Sharing: NO. Countries: 0. Publications: 0.
Advanced Cardiac Magnetic Resonance Imaging for Assessment of Obstructive Coronary Artery Disease: ADVOCATE-CMR
ClinicalTrials.gov study NCT06419894. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Functional Magnetic Resonance Imaging in Infants
ClinicalTrials.gov study NCT00008814. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Magnetic Resonance Imaging and Thermal Imaging of Adiposity in Neonates of Women With Metabolic Diseases
ClinicalTrials.gov study NCT04890886. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Evaluating the Use of Magnetic Resonance Imaging and Contrast Enhanced Mammography After MagTrace® Use
ClinicalTrials.gov study NCT05637528. IPD Sharing: NO. Countries: 0. Publications: 0.
Diffusion Magnetic Resonance Imaging in Mamographically Detected Asymmetric Densities
ClinicalTrials.gov study NCT03696147. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Functional Magnetic Resonance Imaging - Synthetic Aperture Magnetometry (fMRI-SAM) and Alzheimer's Disease
ClinicalTrials.gov study NCT00412048. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Cellulite and Magnetic Resonance Imaging
ClinicalTrials.gov study NCT00636025. IPD Sharing: Not stated. Countries: 0. Publications: 0.
BOLD Functional Magnetic Resonance Imaging (fMRI) and Cerebral Blood Flow Measurements as Biomarkers for Cognition Enhancing Drugs (3134-006)
ClinicalTrials.gov study NCT00887601. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Development Of New Techniques For Functional Magnetic Resonance Imaging Of The Brain
ClinicalTrials.gov study NCT00593931. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Performance Evaluation by Magnetic Resonance Imaging (MRI) of Intramuscular Thigh Injections With 3 Configurations of Needle-free Injector (ZENEO®)
ClinicalTrials.gov study NCT03225638. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
A comprehensive dataset of magnetic resonance enterography images with bowel segment annotations
<p>Inflammatory bowel disease (IBD) is a kind of recurrent bowel disease and usually requires magnetic resonance enterography (MRE) examinations for diagnosis and monitoring. However, radiologists’ recognition of bowel segments from MRE images is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual efforts and provide automated tools to assist in the management of disease, but it requires a large-scale fine<span>-</span>annotated dataset for training. To address this gap, we collected MRE data from 114 IBD patients. The bowel images per patient were contoured and annotated as ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Further, we <span>validated</span> the efficiency of several state-of-the-art segmentation methods on this dataset. This work established a high quality, publicly available whole bowel segment MR dataset with benchmark results and laid a groundwork for IBD’s AI research.</p>
Dataset related to article: "68Ga-PSMA Positron Emission Tomography/Computerized Tomography for Primary Diagnosis of Prostate Cancer in Men with Contraindications to or Negative Multiparametric Magnetic Resonance Imaging: A Prospective Observational Study"
<p>This record contains raw data related to article 68Ga-PSMA Positron Emission Tomography/Computerized Tomography for Primary Diagnosis of Prostate Cancer in Men with Contraindications to or Negative Multiparametric Magnetic Resonance Imaging: A Prospective Observational Study</p> <p>PURPOSE:</p> <p><sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography may represent the most promising imaging modality to identify and risk stratify prostate cancer in patients with contraindications to or negative multiparametric magnetic resonance imaging.</p> <p>MATERIALS AND METHODS:</p> <p>In this prospective observational study we analyzed <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography in a select group of patients with persistently elevated prostate specific antigen and/or Prostate Health Index suspicious for prostate cancer, negative digital rectal examination and at least 1 negative biopsy. The cohort comprised men with equivocal multiparametric magnetic resonance imaging (Prostate Imaging-Reporting and Data System, version 2 score of 2 or less), or an absolute or relative contraindication to multiparametric magnetic resonance imaging. Sensitivity, specificity and CIs were calculated compared to histopathology findings. ROC analysis was applied to determine the optimal cutoff values of <sup>68</sup>Ga labeled prostate specific membrane antigen uptake to identify clinically significant prostate cancer (Gleason score 7 or greater).</p> <p>RESULTS:</p> <p>A total of 45 patients with a median age of 64 years were referred for <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography between January and August 2017. The 25 patients (55.5%) considered to have positive positron emission tomography results underwent software assisted fusion biopsy. We determined the uptake values of regions of interest, including a median maximum standardized uptake value of 5.34 (range 2.25 to 30.41) and a maximum-to-background standardized uptake value ratio of 1.99 (range 1.06 to 14.42). Mean and median uptake values on <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography (ie the maximum standardized uptake value or the maximum-to-background standardized uptake value ratio) were significantly higher for Gleason score 7 lesions than for Gleason score 6 or benign lesions (p <0.001). On ROC analysis a maximum standardized uptake value of 5.4 and a maximum-to-background standardized uptake value ratio of 2 discriminated clinically relevant prostate cancer with 100% overall sensitivity in each case, and 76% and 88% specificity, respectively.</p> <p>CONCLUSIONS:</p> <p>Our findings support the use of <sup>68</sup>Ga labeled prostate specific membrane antigen positron emission tomography/computerized tomography for primary detection of prostate cancer in a specific subset of men.</p>
Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.
<p>Data set and 3d model from Emendi M, Sturla F, Ghosh RP, Bianchi M, Piatti F, Pluchinotta FR, Giese D, Lombardi M, Redaelli A, Bluestein D. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid-Structure Interaction Approach. Ann Biomed Eng. 2020 Aug 17. doi: 10.1007/s10439-020-02571-4. Epub ahead of print. PMID: 32804291.</p> <p> </p> <p>This is the abstract:</p> <p>Congenital bicuspid aortic valve (BAV) consists of two fused cusps and represents a major risk factor for calcific valvular stenosis. Herein, a fully coupled fluid-structure interaction (FSI) BAV model was developed from patient-specific magnetic resonance imaging (MRI) and compared against in vivo 4-dimensional flow MRI (4D Flow). FSI simulation compared well with 4D Flow, confirming direction and magnitude of the flow jet impinging onto the aortic wall as well as location and extension of secondary flows and vortices developing at systole: the systolic flow jet originating from an elliptical 1.6 cm<sup>2</sup> orifice reached a peak velocity of 252.2 cm/s, 0.6% lower than 4D Flow, progressively impinging on the ascending aorta convexity. The FSI model predicted a peak flow rate of 22.4 L/min, 6.7% higher than 4D Flow, and provided BAV leaflets mechanical and flow-induced shear stresses, not directly attainable from MRI. At systole, the ventricular side of the non-fused leaflet revealed the highest wall shear stress (WSS) average magnitude, up to 14.6 Pa along the free margin, with WSS progressively decreasing towards the belly. During diastole, the aortic side of the fused leaflet exhibited the highest diastolic maximum principal stress, up to 322 kPa within the attachment region. Systematic comparison with ground-truth non-invasive MRI can improve the computational model ability to reproduce native BAV hemodynamics and biomechanical response more realistically, and shed light on their role in BAV patients' risk for developing complications; this approach may further contribute to the validation of advanced FSI simulations designed to assess BAV biomechanics.</p> <p> </p>
Dataset related to the article " Automated Left and Right Ventricular Chamber Segmentation in Cardiac Magnetic Resonance Images Using Dense Fully Convolutional Neural Network"
<p>This record contains raw data related to the article " Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network"</p> <p><br> Background and objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiacfunction evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.<br> Method: A retrospectively selected database (DB1) of 210 cine sequences (3 pathology groups) was considered: images (GE, 1.5 T) were acquired at Centro Cardiologico Monzino (Milan, Italy), and end-diastolic (ED) and end-systolic frames (ES) were manually segmented (gold standard, GS). Automatic ED and ES RV and LV segmentation were performed with a U-Net inspired architecture, where skip connections were redesigned introducing dense blocks to alleviate the semantic gap between the U-Net encoder and decoder. The proposed architecture was trained including: A) the basal slices where the Myo surrounded<br> the LV for at least the 50% and all the other slice; B) all the slices where the Myo completely surrounded the LV. To evaluate the clinical relevance of the proposed architecture in a practical use case scenario, a graphical user interface was developed to allow clinicians to revise, and correct when needed, the automatic segmentation. Additionally, to assess generalizability, analysis of CMR images obtained in 12 healthy volunteers (DB2) with different equipment (Siemens, 3T) and settings was performed.<br> Results: The proposed architecture outperformed the original U-Net. Comparing the performance on DB1 between the two criteria, no significant differences were measured when considering all slices together, but were present when only basal slices were examined. Automatic and manually-adjusted segmentation<br> performed similarly compared to the GS (bias±95%LoA): LVEDV -1±12 ml, LVESV -1±14 ml, RVEDV 6±12 ml, RVESV 6±14 ml, ED LV mass 6±26 g, ES LV mass 5±26 g). Also, generalizability showed very similar performance, with Dice scores of 0.944 (LV), 0.908 (RV) and 0.852 (Myo) on DB1, and 0.940 (LV), 0.880 (RV), and 0.856 (Myo) on DB2.<br> Conclusions: Our results support the potential of DL methods for accurate LV and RV contours segmentation and the advantages of dense skip connections in alleviating the semantic gap generated when high level features are concatenated with lower level feature. The evaluation on our dataset, considering separately the performance on basal and apical slices, reveals the potential of DL approaches for fast, accurate and reliable automated cardiac segmentation in a real clinical setting.<br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.