MatMRI: A GPU enabled package for model based MRI image reconstruction
Bibliographic record
Abstract
MatMRI is a package for MRI image reconstruction 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: non-Cartesian regridding iterative SENSE (Cartesian or non-Cartesian) iterative SENSE for higher order models that may include: an off-resonance map time varying spherical harmonics of phase accrual compressed sensing for higher order models spherical harmonic estimation using raw field probe data Also see https://gitlab.com/cfmm/matlab/matmri Related publications: 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. PMID: 36161333. Baron CA, Dwork N, Pauly JM, Nishimura DG. Rapid compressed sensing reconstruction of 3D non-Cartesian MRI. Magn. Reson. Med. 2018;79:2685–2692. 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. Epub 2012 Mar 14. Erratum in: IEEE Trans Med Imaging. 2012 Sep;31(9):1833.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.067 |
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".