DWI MRI - Low Rank Diffusion Paper Datasets - Rohan Senthilkumar

General InformationTitle: Rohan VDCASPR Dataset for Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRIDescription: This dataset contains raw and processed k-space data from VD-CASPR and T2-weighted diffusion MRI scans, aimed at improving fat-tumor discrimination using a novel low-rank reconstruction framework. The dataset includes raw k-space data, corrected Fourier transform data, and transformed k-space data post low-rank reconstruction. The scans mimic clinical scenarios with challenging tumor and fat signal differentiation.Authors: Rohan Senthilkumar.License: CC-BY 4.0Keywords: Diffusion-Weighted MRI, VD-CASPR, k-space, Fourier Transform, Low-Rank Reconstruction, Fat-Tumor Discrimination, Radiomics<br> Files and DescriptionsRohan_VDCASPR_DatasetDescription: Raw k-space data from the VD-CASPR scan of a phantom object mimicking a lesion in a normal environment.Format: .matMethodology: VD-CASPR scan to capture raw k-space data for subsequent processing and reconstruction.<br><br>2. T2W_WaterPhantom_DataDescription: Raw k-space data from a T2-weighted diffusion scan of a water phantom.Format: .matMethodology: T2-weighted scan to capture diffusion characteristics in a controlled phantom environment.Corrected_fft_data.matDescription: Matlab array with corrected Fourier transform data after phase correction to account for phase changes during the VD-CASPR scan.Format: .matMethodology: Phase correction applied to enhance the accuracy of k-space data.<br><br><br>4. transformed_k_space_data.matDescription: Matlab array containing corrected post-Fourier transform values after low-rank reconstruction of VD-CASPR scan data. Suitable for low k-space sampling and low-frequency reconstruction.Format: .matMethodology: Data translated to the actual end image using CAIPIRINHA and SMS techniques.<br><br>5. magnitude_spectra.matDescription: Matlab array containing the magnitudes of signals from the T2-weighted diffusion scan.Format: .matMethodology: Extracted signal magnitudes post-Fourier transform for signal intensity analysis.fourier_transforms.matDescription: Matlab array of k-space data from a T2-weighted diffusion scan post-Fourier transform.Format: .matMethodology: Fourier transform applied to diffusion-weighted scan data.Provenance and Contextual InformationProvenance: Data collected and processed as part of research aimed at improving diagnostic accuracy in diffusion-weighted MRI by mitigating fat-tumor signal ambiguity through advanced reconstruction techniques.Study Context: The dataset supports the research presented in the paper titled "Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis," which proposes a novel framework combining accelerated data acquisition, structured low-rank regularization, and deep learning-assisted radiomic analysis.Data Collection Methodology: VD-CASPR and T2-weighted diffusion MRI scans conducted on a phantom object designed to simulate clinical conditions with challenging signal differentiation.<br>Standards and InteroperabilityKnowledge Representation: Metadata and data formatted using Matlab arrays (.mat), adhering to widely accepted standards for scientific data representation.Vocabularies: Utilizes terminologies and standards compliant with FAIR principles, as recommended by FAIRsharing.org, to ensure broad applicability and reusability.Qualified References: Includes references to related k-space and Fourier transform data to provide comprehensive context and facilitate integration with other datasets.<br><br><br><br>