Effects of diffusion on high‐resolution quantitative <i>T</i><sub>2</sub> MRI
Bibliographic record
Abstract
Carr-Purcell-Meiboom-Gill-based sequences are often assumed to be insensitive to diffusion. However, imaging gradients always contribute some degree of diffusion weighting which increases with resolution. This may cause an apparent decrease in T2 when using a multi-echo sequence, such as quantitative T2 (qT2) at high resolution. This study investigated the impact of diffusion on the qT2 sequence. An equation was developed relating the diffusion factor associated with each echo (bqT2 ) to the underestimation of T2 , which was strongly dependent on both the actual T2 and the apparent diffusion coefficient of the tissue. The diffusion dependence of the measured T2 was demonstrated in rat spinal cord. The measured T2 was independent of the imaging plane in gray matter, where diffusion was isotropic, and orientation dependent in white matter, where diffusion was strongly anisotropic. The dependence of the measured T2 on the actual T2 value was also demonstrated in MnCl2 phantoms. The relationship between the resolution and underestimation of T2 was investigated both theoretically and experimentally for the original readout and a fully refocused readout. The fully refocused readout increased the resolution at which diffusion effects could be neglected whilst measuring T2 . To avoid the misidentification of cerebrospinal fluid when applying qT2 in the brain or spinal cord, a minimum in-plane voxel dimension of 0.2 mm was suggested for the standard qT2 sequence and 0.1 mm for the refocused readout. Simulations of myelin water fraction measurement indicated that signal-to-noise ratio requirements were increased in the presence of diffusion. Finally, the use of decreasing spoiler gradients to attenuate stimulated echoes should be avoided, as it was found to distort the T2 distribution when the slice thickness was less than 1 mm.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".