MatMRI: A GPU enabled package for MRI image reconstruction and processing
Bibliographic record
Abstract
MatMRI is a package for MRI image reconstruction and processing using only core or toolbox MATLAB® functions (i.e., no mex files or pre-compiled binaries). This philosophy is used to enable easy integration with built-in MATLAB® GPU functionality, and to minimize dependencies. It currently supports: Reconstruction non-Cartesian regridding iterative SENSE (Cartesian or non-Cartesian) iterative SENSE for higher order models that may include: a B0 map time varying spherical harmonics of phase accrual compressed sensing with wavelet xform trajectory design spiral LOTUS (doi.org/10.1002/mrm.70469) diffusion MRI fitting spatially regularized diffusion kurtosis fitting with an axially symmetric model (nii2kurt.m) microscopic fractional anisotropy from LTE and STE data, using a free water elimination model (nii2uFA_fwe.m) Also see https://gitlab.com/cfmm/matlab/matmri Citations For citing MatMRI as a whole, use: Varela-Mattatall G, Dubovan PI, Santini T, Gilbert KM, Menon RS, Baron CA. Single-shot spiral diffusion-weighted imaging at 7T using expanded encoding with compressed sensing. Magn Reson Med. 2023 Apr 10. doi: 10.1002/mrm.29666 Baron CA (2021, February 2). MatMRI: A GPU enabled package for model based MRI image reconstruction. Zenodo. http://doi.org/10.5281/zenodo.4495476 Additionally, please reference the following works for usage of the below methods: nii2kurt: Hamilton, J., Xu, K., Geremia, N., Prado, V. F., Prado, M. A. M., Brown, A., & Baron, C. A. (2024). Robust frequency-dependent diffusional kurtosis computation using an efficient direction scheme, axisymmetric modelling, and spatial regularization. Imaging Neuroscience, 2, 1–22. nii2uFA_fwe: Arezza NJJ, Santini T, Omer M, Baron CA. Estimation of free water-corrected microscopic fractional anisotropy. Front Neurosci. 2023 Mar 7;17:1074730. doi: 10.3389/fnins.2023.1074730 spiralGen: Sothynathan M, Dubovan PI, Baron CA. Laterally Oscillating Trajectory for Undersampling Slices: LOTUS. Magn Reson Med. 2026 Oct;96(4):1682-1695. doi: 10.1002/mrm.70469 Pipe JG, Zwart NR. Spiral trajectory design: a flexible numerical algorithm and base analytical equations. Magn Reson Med. 2014 Jan;71(1):278-85. doi: 10.1002/mrm.24675 harmonicsFromRaw: Dubovan PI, Gilbert KM, Baron CA. A correction algorithm for improved magnetic field monitoring with distal field probes. Magn Reson Med. 2023 Dec;90(6):2242-2260. doi: 10.1002/mrm.29781 Dubovan PI, Varela-Mattatall G, Michael ES, Hennel F, Menon RS, Pruessmann KP, Kerr AB, Baron CA. Basis function compression for field probe monitoring. Magn Reson Med. 2025 Jun;93(6):2414-2433 findDelAuto: Dubovan PI, Baron CA. Model-based determination of the synchronization delay between MRI and trajectory data. Magn Reson Med. 2022 Sep 26. doi:10.1002/mrm.29460. nufftOp: Baron CA, Dwork N, Pauly JM, Nishimura DG. Rapid compressed sensing reconstruction of 3D non-Cartesian MRI. Magn. Reson. Med. 2018;79:2685–2692. sampHighOrder: Wilm BJ, Barmet C, Pruessmann KP. Fast higher-order MR image reconstruction using singular-vector separation. IEEE Trans Med Imaging. 2012 Jul;31(7):1396-403. doi: 10.1109/TMI.2012.2190991. Baron CA, Dwork N, Pauly JM, Nishimura DG. Rapid compressed sensing reconstruction of 3D non-Cartesian MRI. Magn. Reson. Med. 2018;79:2685–2692
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.221 | 0.137 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".