Quantitative <i>T</i><sub>2</sub> in the occipital lobe: The role of the CPMG refocusing rate
Bibliographic record
Abstract
PURPOSE: To investigate the dependence of occipital gray and white matter T(2) on the Carr-Purcell-Meiboom-Gill (CPMG) refocusing interval, thereby testing the basis of a novel functional magnetic resonance imaging (fMRI) method for blood volume quantification, and addressing recent questions surrounding T(2) contrast in the occipital lobe. MATERIALS AND METHODS: A CPMG sequence with 1 x 1 x 5 mm(3) resolution was used to quantify T(2) in a single axial slice at the midlevel of the occipital lobe in 23 healthy adult volunteers. Refocusing intervals of 8, 11, and 22 msec were compared. A Bayesian classifier was used to classify a 1 x 1 x 1 mm(3) T(1)-weighted three-dimensional data set into gray matter, white matter, and cerebrospinal fluid, with an average 95% a posteriori probability used as the threshold for inclusion into a tissue-specific region of interest (ROI). RESULTS: The usual T(2) contrast between the gray and white matter (i.e., T(2GM) > T(2WM)) was observed, with a highly significant effect of tissue type on the estimated T(2) (P < 10(-5)). The observed T(2) gradually decreased with increasing refocusing interval, for a decrease of 3.3 +/- 1.5 msec in gray matter and 3.0 +/- 1.5 msec in white matter between the 8 and 22 msec refocusing interval acquisitions. CONCLUSION: The observed T(2) shortening is consistent with the effect of the dramatic decrease in T(2) of partly deoxygenated blood on this range of refocusing rates.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".