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73 results for “diffusion weighted images”
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
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Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Diffusion weighted MR imaging of post-mortem rat brain to allow reconstruction of the cortical connectome
<h2>Brief description</h2> <p> </p> <p>These data accompany the article by Sinke et al. (Sinke et al., 2018). It contains the dMRI image volumes of 10 rats, a subset of these data was used for the tractography procedures described in the article. In addition high-resolution 3D balanced SSFP data are provided with high contrast between grey and white matter and CBF. The data are also accompanied by T<sub>1</sub> weighted 3D spoiled gradient echo volumes at three different echo times (5,10 and 15 ms) which can be used for T<sub>2</sub>* measurements.</p> <h2>Animals</h2> <p> </p> <p>All animal procedures were approved by the Animal Experiments Committee of the University Medical Center Utrecht and Utrecht University. Experiments were performed in accordance with the guidelines of the European Communities Council Directive. Ten healthy adult (12–13 weeks old) male Wistar rats have been used and are described in the RCR_table.csv file. Animals were sacrificed and their brains were fixed with transcardial perfusion-fixation. Brains were extracted scanned.</p> <p> </p> <h2>MR acquisition</h2> <p> </p> <p>MRI was performed on a 9.4 T horizontal bore MR system (Varian, Palo Alto, CA, USA) equipped with a 6 cm ID gradient insert with gradients up to 1 T/m. A custom made solenoid coil with an internal diameter of 2.6 cm was used for excitation and reception of the MR signal. The perfusion-fixed brains were inserted with the skulls intact in a custom-made holder and immersed in non-magnetic oil (Fomblin, Solvay Solexis). Diffusion MR used a 3D diffusion-weighted spin-echo sequence with an isotropic spatial resolution of 150 mm, where the read- and phase- encode direction were acquired using 8-shot EPI encoding and the second phase direction was linearly phase-encoded (TR/TE 500/32.4 ms, 220*128*108 matrix, FOV 33*19.2*16 mm<sup>3</sup>, D/d 15/4 ms, b 1031,2078,3994,6038,7756 s/mm<sup>2</sup>, 60 diffusion-weighted images in non-collinear directions and 24 images without diffusion weighting (b=0), number of averages 1, total number of images 325). Four 3D BSSFP images were acquired with an isotropic spatial resolution of 100 mm (TR/TE 15.4/7.7 ms, flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 6 averages, pulse angle shift 0°, 90°, 180° and 270°). The four images were added as complex images to obtain a single BSSFP image with reduced banding artifacts in the brain. If scanning time allowed, three spoiled gradient-echo acquisitions were also performed with varying echotimes of 15, 10 and 5 ms respectively and TR 20 ms (flip angle 40°, 320*160*190 matrix, FOV 32*16*19 mm<sup>3</sup>, 24 averages, pulse angle shift 117°).</p> <h2>Data structure</h2> <p> </p> <p>The repository contains the following data:</p> <p>- READ_ME.txt: this file</p> <p>- RCR_table.csv : Table containing acquisition dates and numbers for the scanned animals.</p> <p>- rawdata.zip : Zipped data directory ‘rawdata’ containing acquired images in NIfTI data format per animal. Data can be unzipped using the ‘unzip’ command. Directory rawdata contains subdirectories RCR01 to RCR10 (individual rat directories). Each rat directory contains the following NIfTI files:</p> <p>o bal.nii.gz and balsumcom.nii.gz : The separate acquisitions of the BSSFP experiment and the complex summation of the data respectively.</p> <p>o dtitot.nii.gz : The diffusion weighted volumes in the order that they were acquired.</p> <p>o bvals and bvecs : Text files containing the b-values and b-vectors in the order that they were acquired, so this corresponds with the dtitot.nii.gz file.</p> <p>o zerob: Text file containing the image numbers where images with no diffusion weighting were acquired.</p> <p>o ubal1.nii.gz, ubal2.nii.gz and ubal3.nii.gz : The three 3D spoiled gradient acquisitions with TE 15,10, and 5 ms respectively.</p> <p>- derivatives.zip : Zipped data directory ‘derivatives’ containing calculated images of the diffusion parameters after application of FMRIB’s diffusion toolbox DTIfit. In addition it contains a file dti3D_b0.nii.gz which is a summation of all the b0-images and a file mask.nii.gz containing the ‘brain’ mask used for application of DTIfit.</p> <p>- Sinke_BrainStructureFunction2018.pdf : The article based on (part) of these data.</p>
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis
<p>We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609</p>
Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging
<p>We uploaded IVIM and DKI parameters values of included patients in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis
<p>We uploaded dataset including apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP), tissue diffusivity (DT) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)) of 52 patients of the manuscript: Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Granata V, Fusco R, Risi C, Ottaiano A, Avallone A, De Stefano A, Grimm R, Grassi R, Brunese L, Izzo F, Petrillo A. Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis. Cancers (Basel). 2020 Aug 26;12(9):2420. doi: 10.3390/cancers12092420. PMID: 32858990; PMCID: PMC7565693.</p>
Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination
<p>We uploaded the daset releatet t the manuscript: Fusco R, Granata V, Pariante P, Cerciello V, Siani C, Di Bonito M, Valentino M, Sansone M, Botti G, Petrillo A. Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination. Magn Reson Imaging. 2021 Jan;75:51-59. doi: 10.1016/j.mri.2020.10.008. Epub 2020 Oct 17. PMID: 33080334.</p>
Diffusion-weighted Imaging Magnetic Resonance for Assessing Liver Fibrosis
ClinicalTrials.gov study NCT02682108. IPD Sharing: NO. Countries: 1. Publications: 9.
Diffusion-weighted Imaging Study in Cancer of the Ovary
ClinicalTrials.gov study NCT01505829. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Functional Imaging in Multiple Myeloma -PET/CT and Diffusion Weighted Imaging in Multiple Myeloma
ClinicalTrials.gov study NCT02187731. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Whole-body Diffusion-weighted Magnetic Resonance Imaging for Staging and Treatment Prediction of Lymphoma
ClinicalTrials.gov study NCT01231269. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Identification of Stroke Patients ≤ 3 and ≤ 4.5 Hours of Symptom Onset by Fluid Attenuated Inversion Recovery (FLAIR) Imaging and Diffusion Weighted Imaging (DWI)
ClinicalTrials.gov study NCT01021319. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2)
ClinicalTrials.gov study NCT01349946. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Prediction of Surgical Resectability After FOLFIRINOX Chemotherapy for Borderline Resectable and Locally Advanced Pancreatic Cancer: the Role of Diffusion Weighted Magnetic Resonance Imaging, Radiomic
ClinicalTrials.gov study NCT05298722. IPD Sharing: UNDECIDED. Countries: 2. Publications: 13.
Diffusion Weighted Magnetic Resonance Imaging for the Characterization of Solitary Pulmonary Lesions
ClinicalTrials.gov study NCT02482181. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Monitoring of Breast Cancers Treated by Neoadjuvant Therapy Via Diffusion-weighted Magnetic Resonance Imaging
ClinicalTrials.gov study NCT02798484. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Whole Body Magnetic Resonance Imaging With Diffusion Weighted Imaging : Potential Role in Neurofibromatosis
ClinicalTrials.gov study NCT01777451. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.
Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
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