2026-10-10T11:45:05Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/19142026-10-09T11:03:14Zradar4kit

https://dx.doi.org/10.35097/1914 Becker, Moritz Arvidsson, Filip Bertilson, Jonas Lehmkuhl, Sören Simulated 2D RASER MRI dataset for AI-driven artefact correction Karlsruhe Institute of Technology 2024 Engineering dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-sa/4.0/legalcode # Simulated 2D RASER MRI dataset for AI-driven artefact correction Data for AI-driven artefact correction in 2D RASER MRI images. Random images are generated with basic shapes and image transformations. 30 projections of each image are taken, and undergo a RASER (Radiowave amplification by the stimulated emission of radiation) [1] simulation in MATLAB. The data is divided into 3 subsets: - 10k_images.7z --> standard random images - 10k_images_WithPump.zip --> projections experience parahydrogen pumping - 1k_images_20TPI.zip --> high total population inversion (TPI) variations of +/- 20% ## File format Folder structure: ```{subset}/image{#}/{TPI value}/{filename.csv}``` Each folder contains the following files: - A(0).csv --> Signal amplitude - d(0).csv --> TPI evolution - meta.csv --> Meta information - output(Real and Imag).csv --> Simulated RASER signal - Phi(0).csv --> Signal phase ## Data loading Scripts for data loading are provided with the code at [github.com/mobecks/raser-mri-ai](https://github.com/mobecks/raser-mri-ai). ## References [1] Sören Lehmkuhl et al., RASER MRI: Magnetic resonance images formed spontaneously exploiting cooperative nonlinear interaction.Sci. Adv.8,eabp8483(2022). DOI:10.1126/sciadv.abp8483 application/x-tar