MétaCan
Menu
Back to cohort
Record W2156346018 · doi:10.1109/tmi.2004.827969

Time Delay for Arrival of MR Contrast Agent in Collateral-Dependent Myocardium

2004· article· en· W2156346018 on OpenAlexaff
M. Jerosch-Herold, Xiaoping Hu, Naveen S. Murthy, Ravi T. Seethamraju

Bibliographic record

VenueIEEE Transactions on Medical Imaging · 2004
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood Institute
KeywordsDeconvolutionImpulse responsePerfusionCollateral circulationImpulse (physics)Blood flowBiomedical engineeringCardiologyMedicineComputer sciencePhysicsMathematicsAlgorithm

Abstract

fetched live from OpenAlex

An analysis of the kinetics of myocardial contrast enhancement is an important component of myocardial perfusion studies. The contrast enhancement can be modeled by a linear time-invariant system, and the myocardial impulse response, calculated by deconvolution of the measured tissue response with an arterial input, gives a direct estimate of myocardial blood flow. In this paper, we analyze the effects of delays in the contrast enhancement, that occur in collateral-dependent myocardium, where the tracer reaches the tissue region only through branches from other coronary arteries that form natural bypass vessels. We investigate how the delayed arrival of tracer alters the myocardial impulse response. Model-independent deconvolution is applied to determine the lag between arterial input and tissue enhancement. Experimental data in a porcine model of collateral development indicate that the delayed arrival of an injected tracer, measured at rest, is a useful marker to identify collateral-dependent myocardium, and predict its flow capacitance.

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)0.000
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: none
Teacher disagreement score0.958
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.305
Teacher spread0.294 · 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

Citations35
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Medical ImagingSame topicAdvanced MRI Techniques and ApplicationsFrench-language works237,207