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11 results for “MRF”
Atomic energy densities from the multiple radii functional (MRF)
<p>This dataset contains exchange-correlation energy densities in the gauge of the electrostatic potential for various atomic systems, all in atomic units. The first column represents the distance from the nucleus in bohr, while the remaining columns represent the energy densities. The headings for the columns are specified in the first row of the dataset. The dataset includes the exact energy densities "w_0 [exact]" and "w_1 [exact]" which are taken from reference [Phys.Chem.Chem.Phys., 2017, 19, 6169]. These densities are calculated at the zero and full coupling strengths, respectively. The densities used to calculate the MRF functional are also taken from the same reference, where the computational details can be found. "w_1 [mrf original]" represents the MRF energy densities calculated using the original fluctuation function developed in reference [J. Phys. Chem. Lett. 2017, 8, 2799−2805]. "w_1 [mrf new]" represents the MRF energy densities calculated using a newly developed fluctuation function, specifically designed to satisfy the high-density limit, non-negativity of the correlation part of the energy densities, and the uniform electron gas limit. Files are given in the XLSX format and different file names represent different atoms (ions). Wolfram Mathematica 13.1.0.0. has been used for data curation. </p> <p> </p>
Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3.0T
<p>3D MR Fingerprinting T1/T2/M0 maps of twelve healthy volunteers obtained in eight different sites (1.5T and 3.0T scanners, single vendor). Each subject/site dataset includes two acquisitions (test-retest) to assess repeatability of the measurement.</p>
DeliCS Training+Validation Data - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 12 healthy volunteers. The data were acquired on a 3T Premier MRI scanner (GE Healthcare, Waukesha, WI) with a 48-channel head receiver-coil. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <p>To run the processing pipeline presented in [1], please follow the instructions on <a href="http://github.com/SetsompopLab/deli-cs">https://github.com/SetsompopLab/deli-cs</a> and download Zenodo datasets <a href="https://doi.org/10.5281/zenodo.7734431">10.5281/zenodo.7734431</a> and <a href="http://doi.org/10.5281/zenodo.7703200">10.5281/zenodo.7703200</a> with testing data and meta data needed for the reconstruction pipeline.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
data set related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach
<p>This record contains raw data related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach</p>
DeliCS Testing Data + DL Checkpoints - SPI-TGAS-MRF+GRE
<p>This data set consists of raw MRI k-space data from 3 healthy volunteers (train_case000, test_case000, and test_case001) and 3 patients (test_case002, test_case003, and test_case004). The data were acquired on a 3T Premier MRI scanners (GE Healthcare, Waukesha, WI) with 48-channel head receiver-coils. The raw data was saved as numpy-arrays to remove any potentially identifying meta-data, and to work in the reconstruction pipeline presented in [1]. </p> <p>Each case tarball contains three files: <strong>raw_mrf.npy, gre_mrf.npy, noise.npy</strong></p> <p>SPI-TGAS-MRF (files named <strong>raw_mrf.npy</strong>):</p> <p>The acquisition consists of an initial adiabatic inversion pulse followed by a 500 TR long readout train (TI/TE/TR = 20/0.7/12ms) with varying flip angles (10 to 75 degrees) and a rotating 3D center-out spiral trajectory. 48 repeats of the TR train are used for a 6 min acquisition. Details available in [2]. The data shape is: (2000, 48, 24000) = (data along spiral readout, number of receive channels, number of spirals across 500 TR's and 48 repeats)</p> <p>GRE (files named <strong>raw_gre.npy</strong>):</p> <p>A 20 second, low resolution (6.9 mm isotropic) gradient echo (GRE) pre-scan with a large FOV of 440x440x440mm^3. The data shape is: (64, 48, 4096) = (data along readout, number of receive channels, number of phase encode lines (64x64))</p> <p>Noise estimation (files named <strong>noise.npy</strong>):</p> <p>Data from a noise scan acquired using all receive channels to calculate the noise coherence matrix. The data shape is: (48, 4096) = (number of receive channels, noise measurement points)</p> <ul> </ul> <p>Finally, <strong>checkpoints.tar.gz</strong> contains the pre-trained weights used for the deliCS network.</p> <p> </p> <p>[1] Iyer S, Schauman S, Sandino C, et al. Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction. <em>BioRxiv: </em><a href="https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1">https://www.biorxiv.org/content/10.1101/2023.03.28.534431v1</a></p> <p>[2] Cao, X, Liao, C, Iyer, SS, et al. Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imaging. <em>Magn Reson Med</em>. 2022; 88: 133- 150. doi:<a href="https://doi.org/10.1002/mrm.29194">10.1002/mrm.29194</a></p>
Whole-Brain 3D MRF and MRI Multi-Scanner, Scan-Rescan Dataset at 3T
<p>This repository contains a multi-scanner, scan-rescan dataset comprising thirty (30) whole-brain 3D MRI acquisitions. Each scan acquisition (n = 30) includes whole-brain quantitative MRI maps (MRF T1, MRF T2, and ADC) and weighted MR images (T1w MPRAGE, T2w SPACE, and T2w FLAIR). Five healthy subjects (deidentified) were scanned over three scanners (all 3T field strength). Each subject was scanned twice per scanner, for a total of 30 scans (5 subjects × 3 scanners × 2 scans). All data was acquired at University Hospitals Cleveland Medical Center and Case Western Reserve University.</p> <p> </p> <p>This dataset was used in the analysis for the following research article published in Investigative Radiology: "<strong><a href="https://journals.lww.com/investigativeradiology/fulltext/9900/physics_informed_discretization_for_reproducible.159.aspx">Physics-Informed Discretization for Reproducible and Robust Radiomic Feature Extraction Using Quantitative MRI</a></strong>" and request this article be referenced in all research works that use this dataset.</p>
Development of MRF for Characterization of Brain Tumors After Radiotherapy
ClinicalTrials.gov study NCT06101069. IPD Sharing: NO. Countries: 1. Publications: 0.
Brain MRF in Children, Adolescents and Young Adults With Acute Leukemia
ClinicalTrials.gov study NCT06421155. IPD Sharing: YES. Countries: 1. Publications: 0.
AI-Driven MRF Type 2 Diabetes Integrating DHT IF and Resistance Training to OM and Functional Outcomes
ClinicalTrials.gov study NCT07336459. IPD Sharing: NO. Countries: 1. Publications: 0.
Induction Chemotherapy for MRF-negative, Moderate-risk, Resectable Middle and Low Rectal Cancer
ClinicalTrials.gov study NCT04296240. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of knockdown of lncRNA MRF on gene expression during osteogenic induction of human BMSCs
GEO Series GSE272041. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
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