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Record W3047513581 · doi:10.1002/mrm.28434

Simultaneous <b>pro</b>ton density <b>f</b>at‐fraction and <b>i</b>maging with water‐specific <b>T<sub>1</sub></b> mapping (PROFIT<sub>1</sub>): application in liver

2020· article· en· W3047513581 on OpenAlexafffund
Richard B. Thompson, Kelvin Chow, Diana R. Mager, Joseph J. Pagano, Justin Grenier

Bibliographic record

VenueMagnetic Resonance in Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsFlip angleImaging phantomNonalcoholic fatty liver diseaseNuclear magnetic resonanceIn vivoMathematicsNuclear medicineChemistryAnalytical Chemistry (journal)Magnetic resonance imagingPhysicsFatty liverMedicineChromatographyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Purpose To describe and validate a simultaneous proton density fat‐fraction (PDFF) imaging and water‐specific T1 mapping (T1(Water)) approach for the liver (PROFIT1) with mapping and low sensitivity to calibration or inhomogeneity. Methods A multiecho gradient‐echo sequence, with and without saturation preparation, was designed for simultaneous imaging of liver PDFF, , and T1(Water) (three slices in ~13 seconds). Chemical‐shift‐encoded MRI processing yielded fat‐water separated images and maps. T1(Water) calculation utilized saturation and nonsaturation‐recovery water‐separated images. Several variable flip angle schemes across k‐space (increasing flip angles in sequential RF pulses) were evaluated for minimization of T1 weighting, to reduce the dependence of T1(Water) and PDFF (reduced flip angle dependence). T1(Water) accuracy was validated in mixed fat‐water phantoms, with various PDFF and T1 values (3T). In vivo application was illustrated in five volunteers and five patients with nonalcoholic fatty liver disease (PDFF, T1(Water), ). Results A sin3(θ) flip angle pattern (0 < θ < π/2 over k‐space) yielded the largest PROFIT1 signal yield with negligible dependence for both T1(Water) and PDFF. Mixed fat‐water phantom experiments illustrated excellent agreement between PROFIT1 and gold‐standard spectroscopic evaluation of PDFF and T1(Water) (<1% T1 error). In vivo PDFF, T1(Water), and maps illustrated independence of the PROFIT1 values from inhomogeneity and significant differences between volunteers and patients with nonalcoholic fatty liver disease for T1(Water) (927 ± 56 ms vs. 1033 ± 23 ms; P < .05) and PDFF (2.0% ± 0.8% vs. 13.4% ± 5.0%, P < .05). was similar between groups. Conclusion The PROFIT1 pulse sequence provides fast simultaneous quantification of PDFF, T1(Water), and with minimal sensitivity to miscalibration or inhomogeneity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.226
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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".

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Citations32
Published2020
Admission routes2
Has abstractyes

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