Find research datasets worth reusing

closedIPD-NOFeb 2026View details →

Diffusion MRI in Heart Failure

ClinicalTrials.gov study NCT02973633. IPD Sharing: NO. Countries: 1. Publications: 1.

Predictive Value of Diffusion-weighted MRI Performed in Early Post-treatment in the Occurrence of Tumor Recurrence or Progression in Head and Neck Squamous Cell Carcinoma Treated With Chemoradiotherap

ClinicalTrials.gov study NCT02862678. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Whole Body MRI Imaging in Multiple Myeloma at 3 Tesla MRI : Added Value of Diffusion Weighted Imaging

ClinicalTrials.gov study NCT01780766. IPD Sharing: Not stated. Countries: 1. Publications: 1.

USPIO-enhanced and Diffusion-weighted MRI for the Detection of Pelvic Lymph Node Metastases

ClinicalTrials.gov study NCT00622973. IPD Sharing: Not stated. Countries: 1. Publications: 3.

Longitudinal (Weekly) Follow-up of Active Plaques in Multiple Sclerosis With 3 Teslas Multi-modality MRI Using Diffusion, Perfusion, Venography and Proton Spectroscopy

ClinicalTrials.gov study NCT00861172. IPD Sharing: Not stated. Countries: 1. Publications: 1.

ART-Pro: Clinical Trial Evaluating Biparametric MRI and Advanced, Quantitative Diffusion MRI for Detection of Prostate Cancer

ClinicalTrials.gov study NCT06579417. IPD Sharing: Not stated. Countries: 2. Publications: 1.

Diffusion-weighted Cerebral MRI and Intra Uterine Growth Restriction.

ClinicalTrials.gov study NCT02229630. IPD Sharing: Not stated. Countries: 1. Publications: 5.

opencc-by-nc-sa-4.0Sep 2019View details →

zenodo28/100

Macaca mulatta structural and diffusion weighted MRI data

The AMU dataset includes structural and diffusion weighted MRI data from 4 Macaca mulatta monkeys. The data is provided both as a directory structure compressed as <code>all_the_data.zip</code>, and as individual files. The correspondence between the directory structure and the individual files is contained in the file <code>tree.json</code>. The bash command <code>source unflatten.sh</code> can be used to convert the individual files into the original directory structure. Sample Description <ul> <li>Sample size: 4</li> <li>Age distribution: 7-8 years</li> <li>Weight distribution: 7.5-12.5 kgs</li> <li>Sex distribution: 3 male, 1 female</li> </ul> Click <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/files/amu.csv">here</a> for the full sample description (.csv download) Scan Procedures and Parameters Ethics approval: obtained at local Ethics Committee Animal care and housing: At Institut de Neurosciences de La Timone Any applicable training: N/A Scanning preparations Anesthesia procedures: Isoflurane Time between anesthesia and scanning: None- anesthesia was performed and monitored during scanning. Head fixation: Kopf frame and ear bars Position in scanner and procedure used: Sphinx position. Fiducial marker placed on right side. Contrast agent: None During scanning Physiological monitoring: Heart rate, respiration Additional procedures: Ventilation Scan sequences <ul> <li>Scanner type: Siemens Prisma 3T</li> <li>Head coil: Body transmit array, 11cm loop receiving coil</li> <li>Optimization of the magnetic field prior to data acquisition: Automatic B0 shimming procedures from Siemens</li> <li> </li> <li>Diffusion-weighted: <ul> <li>Diffusion SE-EPI sequence</li> <li>Voxel resolution: 1 x 1 x 1 mm</li> <li>TE: 87.6ms</li> <li>TR: 7520ms</li> <li>64b1000</li> <li>6b300</li> <li>5b0</li> <li>Two repetitions with reversed phase encoding direction</li> </ul> </li> <li>Structural: <ul> <li>T1 <ul> <li>MPRAGE sequence</li> <li>Voxel resolution: 0.8 x 0.8 x 0.8 mm</li> <li>TE: 2.04ms</li> <li>TR: 2900ms</li> <li>TI: 1000ms</li> </ul> </li> <li>T2 <ul> <li>3D SPACE sequence</li> <li>Voxel resolution: 0.8 x 0.8 x 0.8mm</li> <li>TE: 561ms</li> <li>TR: 3200ms</li> </ul> </li> <li>QSM <ul> <li>3D GRE sequence</li> <li>Voxel resolution: 0.93 x 0.93 x 1mm</li> <li>TE: 2.7-42ms</li> <li>TR: 45ms</li> </ul> </li> </ul> </li> </ul> Personnel <ul> <li>Thomas Brochier1</li> <li>Pascal Belin1</li> <li>Frédéric Chavanne1</li> <li>Lionel Velly1</li> <li>Clémentine Bodin1</li> <li>Luc Renaud1,2</li> <li>Marc Martin1,2</li> <li>Laurence Boes1,2</li> <li>Julien Sein1</li> <li>Bruno Nazarian1</li> <li>Jean-Luc Anton1</li> </ul> 1Institut de Neurosciences de la Timone (INT), UMR7289 CNRS & Aix-Marseille Université, Marseille, France 2Centre d’Exploration Fonctionnelle et de Formation en Primatologie (CE2F-PRIM), UMS5737 CNRS & Aix-Marseille Université, Marseille, France Acknowledgements We gratefully acknowledge the support of French France-Life Imaging (FLI) and Infrastructures en Biologie Santé et Agronomie (IBISA) Funding Project PRIMAVOICE, PI Belin, French Agence Nationale de la Recherche Detailed information can be found at <a href="http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html">http://fcon_1000.projects.nitrc.org/indi/PRIME/amu.html</a>. Citation <ul> <li>Brochier, T., Belin, P., Chavanne, F., Velly, L., Bodin, C., Renaud, L., Martin, M., Boes, L., Sein, J., Nazarian, B., & Anton, J.-L. (2019). Macaca mulatta structural and diffusion weighted MRI data [Data set]. Zenodo. <a href="https://doi.org/10.5281/ZENODO.3402456">https://doi.org/10.5281/ZENODO.3402456</a>.</li> <li>Milham, M. P., Ai, L., Koo, B., Xu, T., Amiez, C., Balezeau, F., … Schroeder, C. E. (2018). An Open Resource for Non-human Primate Imaging. Neuron, 100(1), 61–74.e2. <a href="https://doi.org/10.1016/j.neuron.2018.08.039">https://doi.org/10.1016/j.neuron.2018.08.039</a>.</li> </ul>

opencc-zeroJun 2020View details →

Data from: Longitudinal diffusion MRI as surrogate outcome measure for myelopathy in adrenoleukodystrophy

Objective: To prospectively determine the potential of diffusion MRI (dMRI) of the cervical spinal cord and the corticospinal tracts in brain as surrogate outcome measure for progression of myelopathy in men with adrenoleukodystrophy, as better outcome measures to quantify progression of myelopathy would enable clinical trials with less patients and shorter follow-up. Methods: Clinical assessment of myelopathy included Expanded Disability Status Scale (EDSS), Severity Scoring system for Progressive Myelopathy (SSPROM), timed up-and-go and 6-minute walk test. Applied dMRI metrics included fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity. Results: Data was available for 33 controls and 52 patients. First, cross-sectionally, differences between groups (controls vs. patients; controls vs. asymptomatic patients vs. symptomatic patients) were statistically significant for fractional anisotropy, mean diffusivity and radial diffusivity in spinal cord and brain corticospinal tracts (effect size 0.31-0.68). Correlations between dMRI metrics and clinical measures were moderate to strong (correlation coefficient 0.35-0.60). Second, longitudinally (n=36), change on clinical measures was significant after 2-year follow-up for EDSS, SSPROM and timed up-and-go (p≤0.021, effect size ≤0.14). Change on brain fractional anisotropy and radial diffusivity was slightly larger (p≤0.002, effect sizes 0.16-0.28). In addition, a statistically significant change was detectable in asymptomatic patients using brain dMRI and not using the clinical measures. Change on clinical measures did not correlate to change on dMRI metrics. Conclusions: Although effect sizes were small, our prospective data illustrate the potential of dMRI as surrogate outcome measures for progression of myelopathy in men with adrenoleukodystrophy.

opencc-zeroJul 2020View details →

Data from: Alkaline phosphatase, lactic dehidrogenase, inflammatory variables and apparent diffusion coefficients from MRI for prediction of chemotherapy response in osteosarcoma

Background. This present study aimed to assess if clinical, laboratory and MRI were an accurate benchmark in assessing the effectiveness of neoadjuvant chemotherapy in osteosarcoma patients. Methods. This was an observational analytic study with a cross-sectional design. Research subjects were selected using the total sampling method from osteosarcoma patients who underwent neoadjuvant chemotherapy during the period between January 2017– July 2019. Results.Of the 58 patients included in this study, 38 were male and 20 were female aged 5 - 67 years (mean,16-year-old. 37(63.8%) patients underwent neoadjuvant chemotherapy with CAI regimens and 13 (36.2%) with CA. The tumors were classified as stage IIB in 43 (74.1%) patients and stage III in15 (25.9%) patients. After undergoing neoadjuvant chemotherapy, 4 patients had poor MSTS, 30 patients had fair MSTS, 17 and 7 patients had good and excellent MSTS score, respectively. Spearman's test revealed no correlation between tumor necrosis after neoadjuvant chemotherapy with a reduction in tumor size and MSTS score. Wilcoxon test showed significant differences between ALP, ESR, and NLR before and after neoadjuvant chemotherapy in the poor-response group. We found no significant difference between LDH and LMR before and after neoadjuvant chemotherapy in the good-response group. We had 9 patients for ADC value only. No significant statistical differences were found in tumor volumes after chemotherapy in both groups. Conclusion. We demonstrated that ALP level after neoadjuvant chemotherapy was markedly decreased, and was statistically significant in the poor-response group. We also demonstrated that LDH value before neoadjuvant chemotherapy had a strong correlation with degree of necrosis and could be used as a predictive indicator. NLR and LMR cannot be independent prognostic factors. MRI plays an important role in evaluating tumor volumes and preoperative radiological changes, using DWI and water diffusion to predict histological necrosis.

opencc-zeroDec 2015View details →

Data from: Data for evaluation of fast kurtosis imaging, b-value optimization and exploration of diffusion MRI contrast

Here we describe and provide diffusion magnetic resonance imaging (dMRI) data that was acquired in neural tissue and a physical phantom. Data acquired in biological tissue includes: fixed rat brain (acquired at 9.4T) and spinal cord (acquired at 16.4T) and in normal human brain (acquired at 3T). This data was recently used for evaluation of diffusion kurtosis imaging (DKI) contrasts and for comparison to diffusion tensor imaging (DTI) parameter contrast. The data has also been used to optimize b-values for ex vivo and in vivo fast kurtosis imaging. The remaining data was obtained in a physical phantom with three orthogonal fiber orientations (fresh asparagus stems) for exploration of the kurtosis fractional anisotropy. However, the data may have broader interest and, collectively, may form the basis for image contrast exploration and simulations based on a wide range of dMRI analysis strategies.

opencc-zeroDec 2015View details →

Data from: Diffantom: whole-brain diffusion MRI phantoms derived from real datasets of the Human Connectome Project

Diffantom is a whole-brain diffusion MRI (dMRI) phantom publicly available through the Dryad Digital Repository (doi:10.5061/dryad.4p080). The dataset contains two single-shell dMRI images, along with the corresponding gradient information, packed following the BIDS standard (Brain Imaging Data Structure, Gorgolewski et al., 2015). The released dataset is designed for the evaluation of the impact of susceptibility distortions and benchmarking existing correction methods. In this Data Report we also release the software instruments involved in generating diffantoms, so that researchers are able to generate new phantoms derived from different subjects, and apply these data in other applications like investigating diffusion sampling schemes, the assessment of dMRI processing methods, the simulation of pathologies and imaging artifacts, etc. In summary, Diffantom is intended for unit testing of novel methods, cross-comparison of established methods, and integration testing of partial or complete processing flows to extract connectivity networks from dMRI.

opencc-zeroSep 2021View details →

Longitudinal white-matter abnormalities in sports-related concussion: a diffusion MRI study of the NCAA-DoD CARE Consortium

Objective To study longitudinal recovery trajectories of white-matter after sports-related concussion (SRC), we performed diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n=219, 82 concussed athletes, 68 contact-sport controls, and 69 non-contact-sport controls) were included from the Concussion Assessment, Research and Education (CARE) Consortium. The participants completed clinical assessments and DTI at four time points: 24-48-hours post-injury, asymptomatic state, seven days following return-to-play, and six-months post-injury. Tract-based spatial statistics were used to investigate group differences in DTI metrics and identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than the controls at 24-48 hours post-injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group-differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief-Symptom-Inventory scores and symptom-severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non-contact-sport controls. Conclusions Changes in white matter were evident after SRC at six-months post-injury, but were not observed in contact-sport exposure. Further, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery time.

closedIPD-NOFeb 2026View details →

Diffusion Weighted MRI Enables Differential Diagnosis Between Pyocele and Mucocele

ClinicalTrials.gov study NCT06323798. IPD Sharing: NO. Countries: 0. Publications: 1.

Diffusion Tensor MRI to Distinguish Brain Tumor Recurrence From Radiation Necrosis

ClinicalTrials.gov study NCT00285324. IPD Sharing: Not stated. Countries: 1. Publications: 3.

Added Value of Diffusion Weighted MRI in Evaluation of Sacroiliitis in Newly Diagnosed Patients of Spondyloarthropathy.

ClinicalTrials.gov study NCT05655533. IPD Sharing: Not stated. Countries: 0. Publications: 10.

B-mode Ultrasound, Sono-Elastography, and Diffusion-weighted Imaging MRI in Thyroid Nodules

ClinicalTrials.gov study NCT06029946. IPD Sharing: Not stated. Countries: 0. Publications: 10.

MRI Volumetry and Diffusion Tensor Imaging in Temporal Lobe Epilepsy

ClinicalTrials.gov study NCT07384351. IPD Sharing: Not stated. Countries: 0. Publications: 3.

Role of Diffusion MRI in Differentiation of Various Bone Marrow Lesions

ClinicalTrials.gov study NCT06703138. IPD Sharing: Not stated. Countries: 0. Publications: 5.

The Predictive Value of MRI for Adult-type Diffuse Gliomas

ClinicalTrials.gov study NCT06572592. IPD Sharing: Not stated. Countries: 0. Publications: 1.