Published on 10 October 2024 |
Respiratory-triggered MRCP acquisition at 3T using a T2-weighted TSE (3D SPACE) sequence for Deep Learning-based reconstruction of MRCP
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DescriptionThis dataset is the sample data for MRCP_DLRecon (GitHub). Place this dataset in the "Sample_data/" directory along with the "dataset.csv" file. DataThe provided 3D MRCP data were acquired at 3T (Skyra, Siemens Healthineers AG, Erlangen) using the 3D-SPACE (3D T2w TSE) sequence for a single healthy volunteer. We provide two 2x and one 6x 3D MRCP data to ensure various training and testing scenarios. Each data in the HDF5 format contains the following structures:Datasetsgrappa: target data (y * x * slice)kdata_raw: Raw k-space data (x2 or x6) (nCoil * PE * RO * slice)kdata_fs: Fully-sampled k-space data from kdata_raw using GRAPPA (nCoil ×× PE ×× RO ×× Slice)sm_espirit: ESPIRiT-based sensitivity maps (nCoil * y * x * slice)CitationPlease cite the following paper if this dataset is helpful for your research :)Kim, J., Nickel, M. and Knoll, F. (2025), Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38: e70002. https://doi.org/10.1002/nbm.70002
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Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
44%
Source
Scholar Data Model