MétaCan
Menu
Back to cohort

T2-mapping and T2*-mapping for detection of intramyocardial haemorrhage: a head-to-head comparison with T2-weighted imaging

2015· article· en· W25791627 on OpenAlexaboutno aff
Pankaj Garg, Ananth Kidambi, David P Ripley, Adam K McDiarmid, Peter Swoboda, Tarique A Musa, Bara Erhayiem, Laura E Dobson, John P. Greenwood, Sven Plein

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
FundersBritish Heart Foundation
KeywordsMedicineNuclear medicineAngiologyInternal medicine

Abstract

fetched live from OpenAlex

A variety of CMR methods for detecting intramyocardial haemorrhage (IMH) has been proposed, including T2-weighted imaging (T2w), T2-mapping and T2* mapping. IMH detected by T2w imaging is associated with adverse LV remodelling and adverse outcome post acute myocardial infarction (MI). We compare the sensitivity, specificity, CNR and SNR of the three IMH imaging techniques. Twenty patients underwent CMR at 3T (Achieva TX system, Philips Healthcare, Best, The Netherlands) within 3 days following reperfused ST-elevation MI. Black blood, cine, T2w, T2-mapping, T2*-mapping and LGE imaging (0.1mmol/kg gadolinium DTPA) were performed in identical short axis locations using the ‘3 of 5' approach. Data were evaluated offline using commercial software (cvi42 v4.1.5, Circle Cardiovascular Imaging Inc., Calgary, Canada). On the LGE images showing the largest infarct volume, infarct size was determined by using a semi-automated histogram-based thresholding method. This slice was evaluated for visual presence of IMH by the three methods. Signal intensity (SI) and respective standard deviation of SI (SD) were measured for the infarcted myocardium, remote myocardium and any IMH (if present). SNR was computed for each using the formula=0.655((SI)/(SD)). CNR was determined comparing contrast-to-noise of infarcted myocardium to IMH (SNR i -SNR IMH ). Of the twenty patients, 55% (n=11) had IMH on T2w-imaging. The mean (±standard deviation) SNR and CNR values are listed in Table 1 . The visual assessment of T2w imaging correlated strongly to T2-maps (r=0.69;p=0.001) and to the T2*-maps (r=0.60; p=0.005). The SNR for IMH and infarct zone were significantly different for only T2w imaging (Figure 1 ). Quantitative CNR for T2w imaging correlated strongly to visual assessment of all three imaging modalities (T2w- r=0.650; p=0.002, T2-map- r=0.454; p=0.04, T2*-map- r=0.603;p=0.005). The CNR for T2-maps and T2*-maps did not show similar correlation to the visual assessment. Box-plot of mean ± standard deviation (SD) of Signal-to-Noise-Ratio (SNR) for IMH and Infarct using the three imaging techniques. Quantitative and qualitative T2w-imaging assessment for IMH is superior to T2-mapping and T2*mapping.

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)0.003
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cardiovascular Magnetic ResonanceSame topicCardiac Imaging and DiagnosticsFrench-language works237,207