Rapid combined <i>T</i><sub>1</sub> and <i>T</i><sub>2</sub> mapping using gradient recalled acquisition in the steady state
Bibliographic record
Abstract
A novel, fully 3D, high-resolution T(1) and T(2) relaxation time mapping method is presented. The method is based on steady-state imaging with T(1) and T(2) information derived from either spoiling or fully refocusing the transverse magnetization following each excitation pulse. T(1) is extracted from a pair of spoiled gradient recalled echo (SPGR) images acquired at optimized flip angles. This T(1) information is combined with two refocused steady-state free precession (SSFP) images to determine T(2). T(1) and T(2) accuracy was evaluated against inversion recovery (IR) and spin-echo (SE) results, respectively. Error within the T(1) and T(2) maps, determined from both phantom and in vivo measurements, is approximately 7% for T(1) between 300 and 2000 ms and 7% for T(2) between 30 and 150 ms. The efficiency of the method, defined as the signal-to-noise ratio (SNR) of the final map per voxel volume per square root scan time, was evaluated against alternative mapping methods. With an efficiency of three times that of multipoint IR and three times that of multiecho SE, our combined approach represents the most efficient of those examined. Acquisition time for a whole brain T(1) map (25 x 25 x 10 cm) is less than 8 min with 1 mm(3) isotropic voxels. An additional 7 min is required for an identically sized T(2) map and postprocessing time is less than 1 min on a 1 GHz PIII PC. The method therefore permits real-time clinical acquisition and display of whole brain T(1) and T(2) maps for the first time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".