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7 results for “Kurtosis”
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>
Evidence for microscopic kurtosis in neural tissue revealed by correlation tensor MRI
<p>Three sample double diffusion encoding (DDE) datasets for three female rat brains acquired using a 9.4T Bruker Biospec scanner equipped with an 86 mm quadrature transmission coil and four-element array reception cryocoil (Rat1_invivo_cti_data.nii, Rat2_invivo_cti_data.nii, Rat3_invivo_cti_data.nii).</p> <p>All animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.</p> <p>DDE data were acquired for 636 pairs of DDE b-values (bvals1.bval and bvals2.bval) and gradient directions (bvecs1.bvec and bvecs2.bvec) according to the DDE protocol for Correlation Tensor MRI (CTI) fitting described by Henriques et al. (Magn Reson Med 2021, doi:10.1002/mrm.28938).</p> <p>For each rat brain dataset, thermal noise from each cryocoil channel was suppressed using the threshold-based PCA denoising algorithm (Henriques et al., bioRxiv 2023, doi:10.1101/2023.03.29.534707). The denoised data was subsequently corrected for Gibbs ringing using the sub-voxel shift algorithm (Kellner et al. Magn Reson Med 2016, doi: 10.1002/mrm.26054) which is implemented in DIPY (Garyfallidis et al., Frontiers in Neuroinformatics 2014, doi: 10.3389/fninf.2014.00008). The processed diffusion-weighted signals from the four channels were then combined using sum-of-squares. Combined data for different gradient direction pairs were then aligned along the different b-values and directions using a sub-pixel registration technique (Guizar-Sicairos et al., Opt Lett. 2008, doi: 10.1364/ol.33.000156).</p> <p>Full description of these datasets acquisition and preprocessing can be found in its original manuscript (Henriques et al., Magn Reson Med 2021, doi:10.1002/mrm.28938).</p>
Data from: Characterization of breast tumors using diffusion kurtosis imaging (DKI)
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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.
Data from: Data for evaluation of fast kurtosis imaging, b-value optimization and exploration of diffusion MRI contrast
Open the record for dataset details and reuse information.
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