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Record W2008463999 · doi:10.1186/1532-429x-14-s1-p221

Age and gender dependence of pre-contrast T1-relaxation times in normal human myocardium at 1.5T using ShMOLLI

2012· article· en· W2008463999 on OpenAlexfundno aff
Stefan K. Piechnik, Vanessa M. Ferreira, Adam J. Lewandowski, Ntobeko Ntusi, Daniel Sado, Viviana Maestrini, Steven K White, Merzaka Lazdam, Rajarshi Banerjee, Mark B.M. Hofman, James Moon, Stefan Neubauer, Paul Leeson, Matthew D. Robson

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2012
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersClarendon FundNIHR Oxford Biomedical Research CentreFondation pour la Recherche MédicaleUniversity of OxfordAlberta Heritage Foundation for Medical ResearchNational Institute for Health and Care Research
KeywordsMedicineAngiologyNormal valuesContrast (vision)Magnetic resonance imagingHuman heartReference valuesCardiologyInternal medicineRadiologyArtificial intelligence

Abstract

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Quantitative T1-mapping is rapidly becoming a clinical Cardiovascular Magnetic Resonance (CMR) imaging tool that can distinguish normal from diseased myocardium. The usefulness of any quantitative measurement to identify disease lies in its ability to detect significant differences from an established normal range of values. In this study we aim to establish a large database for the normal range of T1 values in healthy human myocardium and to examine any differences based on age and gender. 231 healthy volunteers underwent CMR with at least one ShMOLLI (Shortened Modified Look-Locker Inversion recovery) T1-map in one of three CMR centres. All data were acquired in 1.5T MR systems (Siemens, Avanto) using the ShMOLLI sequence for T1 maps as previously described [Piechnik at al. JCMR, 2010, 12:69] with 16 or 32 channel coil arrays. Each subject yielded a single average T1 value based on semi-automatically drawn myocardial contours in 3 short axis slices (typically, range 1-7, median=3, mean=3.3±1.1 slices). The heart rate (HR) was calculated from individual ShMOLLI image times. The age range was 11-85 years with similar averages between females (37±14, 19-68 years old, n=123) and males (35±17, 11-85, n=108). The average T1 was 961±26ms amongst all subjects. Males had an average T1=947±20ms and females 974±25ms (p<0.0001). The gender differences in T1 are most prominent between the second and fifth decades of life (Fig. 1 ). T1 decreased with age at -0.5ms/year (females:-0.9ms/y; males: -0.3ms/y). There was only a very weak T1 dependence on HR (+0.45ms/(beat/minute), R=0.15, p=0.03), which was much smaller then previously reported +2.7ms/(b/m) for the MOLLI technique [Messroghli et al. Radiology 2006, 238:1004-1012]. Furthermore, there was no HR dependence for either gender separately and the overall relationship can be attributed to the concurrent differences in T1 and HR between groups in our material. Gender and age dependence of T1±SD relaxation times in human myocardium at 1.5 Tesla. Note: Case numbers of females ♀ and males ♂ in the overlapping age groups are shown above the respective bars. (*)Unpaired Student T-test p-values for gender differences are marked when Bonferroni-significant. Normal human myocardial T1 relaxation times can be measured precisely and show a narrow range of variation of about ±2% of the average in relation to age and gender but are not dependent on heart rate using ShMOLLI. T1 variability due to age and gender is small compared to the effect of major cardiac injuries, such as myocardial infarction which is characterised by 10-20% increase in T1. While normal variation will not impact on the sensitivity of T1-mapping to detect acute changes, for the detection of smaller T1 differences, age and gender matching between patients and controls may be desired.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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 designObservational
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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Citations18
Published2012
Admission routes1
Has abstractyes

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