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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it