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Record W2794115091 · doi:10.1002/mp.12840

Effect of T1‐mapping technique and diminished image resolution on quantification of infarct mass and its ability in predicting appropriate <scp>ICD</scp> therapy

2018· article· en· W2794115091 on OpenAlexafffund
Nadia A. Farrag, Venkat Ramanan, Graham A. Wright, Eranga Ukwatta

Bibliographic record

VenueMedical Physics · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMagnetic resonance imagingMedicineGradient echoNuclear medicineImage resolutionMyocardial infarctionCardiologyRadiologyArtificial intelligenceComputer science

Abstract

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PURPOSE: Myocardial infarct (MI) may consist of an infarct core (IC) and a heterogeneous, semi-viable border zone (BZ). Patients with chronic MI in the left ventricular (LV) myocardium are at increased risk of developing ventricular arrhythmias, and may therefore qualify for implantable cardioverter defibrillator (ICD) therapy. Indices based on MI mass, as determined by cardiac magnetic resonance (CMR) imaging, are shown to be sensitive in predicting adverse ventricular arrhythmic events. However, several factors, such as imaging technique and spatial resolution affect the accuracy of MI mass quantification. The aim of this study was to compare the MI masses determined by T1-mapping CMR techniques to those of conventional late Gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) using inversion recovery fast gradient echo (IR-FGRE). We additionally aimed to investigate the effect of diminishing image resolution on quantification of the MI mass and its ability to predict appropriate ICD therapy. METHODS: Thirty-eight patients with known MI underwent acquisitions of three CMR imaging techniques: the multicontrast late enhancement (MCLE) and modified look-locker inversion recovery (MOLLI) T1-mapping techniques, and conventional inversion recovery fast gradient echo (IR-FGRE) about 20 min after double-dose injection of Gadolinium. We postprocessed images to quantify IC and BZ masses determined by each CMR technique using a full-width half-maximum (FWHM) approach in IR-FGRE images and a fuzzy c-means clustering algorithm for T1-mapping images. To determine the impact of spatial resolution in sensitivity of predicting ICD events, we artificially diminished resolution of MCLE images acquired from a separate group of 27 patients who had been followed up for ICD therapy and compared the MI masses estimated from the original and downsampled MCLE images. RESULTS: Twelve patients out of 27 (44%) received ICD therapy (i.e., one or more delivered shock) during the follow-up stage. Between each of the three imaging methods, IC masses were not significantly different. Conversely, BZ masses determined by MOLLI were larger compared to those determined by MCLE and IR-FGRE (P value = 0.0022 and 0.0003, respectively). The BZ masses determined by MCLE were not significantly different from those determined by IR-FGRE; however, BZ masses determined by the downsampled MCLE were significantly larger than those determined by IR-FGRE and original MCLE (P value = 0.0033 and 0.0003, respectively). The BZ mass estimated by original MCLE was larger in patients who had received ICD therapy compared to those who did not (P value = 0.044). However, when the spatial resolution of the MCLE images was diminished to that of MOLLI, BZ masses were not significantly different between patients with and without ICD therapy. CONCLUSIONS: While estimated IC masses were consistent among all three techniques, the estimated BZ masses were not consistent, especially when spatial resolution of images differed between the techniques. In particular, our study showed that diminished image resolution caused an increase in estimation of the BZ mass, likely due to partial volume effects, which led to a reduced sensitivity in the prediction of appropriate ICD therapy.

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)0.005
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0010.000

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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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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Citations7
Published2018
Admission routes2
Has abstractyes

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