A quantitative study of susceptibility and additional frequency shift of three common materials in MRI
Bibliographic record
Abstract
PURPOSE: This work quantifies magnetic susceptibilities and additional frequency shifts derived from different samples. METHODS: Twenty samples inside long straws were imaged with a multiecho susceptibility weighted imaging and analyzed with two approaches for comparisons. One approach applied our complex image summation around a spherical or cylindrical object method to phase distributions outside straws. The other approach utilized phase values inside each straw from two orientations. Both methods quantified susceptibilities of each sample at each echo time. The R2* value of each sample was measured too. Uncertainty of each measurement was also estimated. RESULTS: Quantified susceptibilities from complex image summation around a spherical or cylindrical object are consistent within uncertainties between different echo times. However, this is not the case for the other method. Nonetheless, most quantified susceptibilities are consistent between these two methods. Phase values due to additional frequency shifts in some of ferritin and nanoparticle samples have been identified. Only R2* values quantified from low concentration nanoparticle samples agree with the predictions from the static dephasing theory. CONCLUSION: This work suggests that using the sample sizes and phase values only outside samples can correctly quantify the susceptibilities of those samples. With the presence of a possible additional frequency shift inside a material, it will not be suitable to obtain susceptibility maps without taking that into account. Magn Reson Med 76:1263-1269, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".