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Record W2800740397 · doi:10.1002/jmri.26052

Limitations of skipping echoes for exponential T<sub>2</sub> fitting

2018· article· en· W2800740397 on OpenAlexafffund
Kelly C. McPhee, Alan H. Wilman

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2018
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsFlip angleEcho (communications protocol)Imaging phantomExponential functionSpin echoNuclear magnetic resonanceMathematicsEcho timeWilcoxon signed-rank testNuclear medicinePhysicsMagnetic resonance imagingStatisticsMedicineMathematical analysisComputer scienceMann–Whitney U testRadiology

Abstract

fetched live from OpenAlex

Background Exponential fitting of multiecho spin echo sequences with skipped echoes is still commonly used for quantification of transverse relaxation (T 2 ). Purpose To examine the efficacy of skipped echo methods for T 2 quantification against computational modeling of the exact signal decay. Study Type Prospective comparison of methods. Subjects/Phantom Eight volunteers were imaged at 4.7T, six volunteers at 1.5T, and phantoms ([MnCl 2 ] = 68–270 mM). Field Strength/Sequence 1.5T and 4.7T; multiple‐echo spin echo. Assessment Exponential fitting for T 2 using all echoes, skipping the first echo or skipping all odd echoes, compared with Bloch simulations. Resulting T 2 values were examined over a range of T 2 (10–150 msec), refocusing flip angles (90–270°), and echo train lengths (ETL = 6–32). Statistical Tests Shapiro–Wilk tests and Q‐Q plots were used to check for normality of data. Paired sample t ‐tests and Wilcoxon rank tests were used to compare fitting models using α = 0.05. Multiple comparisons were accounted for with Bonferroni correction. Results In examined regions of interest, typical incorrect estimation of T 2 ranged from 23–39% for exponential fitting of all echoes, or 15–32% for skipped echo methods. In vivo, T 2 estimation error was reduced to as little as 10% with skipped echo methods using 180° refocusing and ETL = 8, although error varied due to refocusing angle, T 2 , and ETL. In vivo, skipped echo T 2 values were significantly different than all echo exponential fitting ( P &lt; 0.004), but also were significantly different from reference values ( P &lt; 0.002, except frontal white matter). Simulations showed skipping the first echo was the most effective form of exponential fitting, in particular for T 2 &lt;50 msec and ETL = 8, with potential to reduce T 2 errors to 10%, depending on refocusing angle and T 2 . Data Conclusion Skipping echoes is insufficient for avoiding stimulated echo contamination. Resulting T 2 errors depend on a complicated interplay of T 2 , refocusing angle, and ETL. Modeling of the multiecho sequence is recommended. Level of Evidence : 2 Technical Efficacy : Stage 1 J. Magn. Reson. Imaging 2018;47:1432–1440.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.310
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2018
Admission routes2
Has abstractyes

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