Analytical corrections of banding artifacts in driven equilibrium single pulse observation of T2 (DESPOT2)
Bibliographic record
Abstract
Purpose DESPOT2 is a single‐component T2 mapping technique based on bSSFP imaging. It has seen limited application because of banding artifacts and magnetization transfer (MT) effects. In this work, acquisitions are optimized to minimize MT effects, while exact and approximate analytical equations enable automatic correction of banding artifacts within the T2 maps in mere seconds. Theory and Methods The technique was verified on an agar phantom at 3 tesla. The T2 resulting from four different data combination techniques was compared with the T2 from CPMG. Two comparable DESPOT2 scan protocols (short vs. long TR/TRF) designed to minimize MT effects, were tested both in the phantom and in vivo. A third protocol was tested in the brain of 8 volunteers and analytical correction schemes were compared with DESPOT2‐FM. Results The T2 measurements in agar agree with CPMG within ∼7% and in vivo results agree with values reported in the literature. The approximate analytical solutions provide increased robustness to hardware imperfections and higher T2‐to‐noise ratio than the exact solutions. Conclusion New analytical solutions enable fast and accurate whole‐brain T2 mapping from previously measured T1 and B1 maps, and bSSFP images with at least two phase offsets and two flip angles (=4 datasets, 8 min scan). Magn Reson Med 76:1790–1804, 2016. © 2015 International Society for Magnetic Resonance in Medicine
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".