QUIQI II Analysis Script - Statistical analyses of motion-corrupted MRI relaxometry data
Authors/Creators
- 1. Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- 2. Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France
- 3. Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
Description
QUIQI accounts for the degradation of data quality due to motion in the analysis of MRI data by assigning weights to each dataset, based on a value of image quality, the Motion Degradation Index (MDI). In the context of quantitative relaxometry data, some maps can be computed from multiple raw image volumes, such as R1, R2*, and MTsat. In QUIQI II, we account for the motion degradation when images are obtained from multiple acquisitions.
QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.
The complete support package for the QUIQI method includes:
- A copy of the original analysis code used to compile the results presented in the original scientific publication
- A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data (doi: 10.5281/zenodo.7692074)
The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.
The material provided here only concerns part 1. of the QUIQI II support package described above – original analysis code.
Files
LREN-physics/QUIQIAnalysisScript-v2.0.zip
Files
(609.5 kB)
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Additional details
Related works
- Is supplement to
- https://github.com/LREN-physics/QUIQIAnalysisScript/tree/v2.0 (URL)
Funding
- Swiss National Science Foundation
- Advanced quantitative MRI biomarkers of Parkinson's Disease - towards in-vivo histology 320030_184784