Quantitative magnetization transfer imaging <i>made</i> easy with <i>q</i><scp>MTL</scp><i>ab</i>: Software for data simulation, analysis, and visualization
Bibliographic record
Abstract
Quantitative magnetization transfer imaging (qMTI) increases specificity to macromolecular content in tissue by modeling the exchange process between the liquid and the macromolecular pool. However, its use has been mostly restricted to researchers that have developed these methods, in part due to the need to write complicated in‐house software for modeling and data analysis. We have developed a software package (qMTLab) with a simple and easy to use graphical user interface that unifies three of the most widely used qMTI methods: MT spoiled gradient echo (MT‐SPGR), MT balanced steady‐state free precession (MT‐bSSFP), and selective inversion recovery with fast spin echo (SIR‐FSE). qMTLab is free open‐source software that allows anyone interested in using these methods to easily simulate qMTI data, compare the performance of the methods under various experimental conditions, define new acquisition protocols, fit acquired data, and visualize the fitted parameters maps. By providing free software that gives end users a simple and easy to use graphical interface, we hope to make qMTI accessible to a greater number of investigators and facilitate the development, evaluation, and optimization of acquisition protocols and models. © 2016 Wiley Periodicals, Inc. Concepts Magn Reson Part A, 2016.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.038 |
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".