A comparison of magnetic resonance methods for spatially resolved<i>T</i><sub>2</sub>distribution measurements in porous media
Bibliographic record
Abstract
Naturally occurring porous media are usually characterized by a distribution of pore sizes. If the material is fluid saturated, the 1 H magnetic resonance (MR) signal depends on the pore size, the surface relaxivity and the fluid itself. Measurement of the transverse relaxation time T 2 is a well-established technique to characterize material samples by means of MR. T 2 distribution measurements, including T 2 distribution mapping, are widely employed in clinical applications and in petroleum engineering. T 2 distribution measurements are the most basic measurement employed to determine the fluid-matrix properties in MR core analysis. Three methods for T 2 distribution mapping, namely spin-echo single point imaging (SE-SPI), DANTE-Z Carr–Purcell–Meiboom–Gill (CPMG) and adiabatic inversion CPMG are compared in terms of spatial resolution, minimum observable T 2 and sensitivity. Bulk CPMG measurement is considered to be the gold standard for T 2 determination. Bulk measurement of uniform samples is compared to the three spatially resolved measurements. SE-SPI is an imaging method, which measures spatially resolved T 2 s in samples of interest. A variant is introduced in this work that employs pre-equalized magnetic field gradient waveforms and is therefore able to measure shorter T 2 s than previously reported. DANTE-Z CPMG and adiabatic inversion CPMG are faster, non-imaging, local T 2 distribution measurements. The DANTE-Z pulse train and adiabatic inversion pulse are compared in terms of T 1 or T 2 relaxation time effects during the RF pulse application, minimum pulse duration, requisite RF pulse power, and inversion profile quality. In addition to experimental comparisons, simulation results are presented.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".