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Modeling tissue contrast agent concentration: A solution to the tissue homogeneity model using a simulated arterial input function

2001· article· en· W2057504810 on OpenAlexafffund
Gerald Moran, Frank S. Prato

Bibliographic record

VenueMagnetic Resonance in Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSt Joseph's Health CareLawson Health Research Institute
FundersMedical Research CouncilMedical Research Council Canada
KeywordsHomogeneity (statistics)Laplace transformMathematical analysisTRACERLaplace's equationBoundary value problemFunction (biology)Capillary actionMathematicsPhysicsMechanicsThermodynamics

Abstract

fetched live from OpenAlex

The tissue homogeneity model, which describes tissue in terms of two compartments, one intravascular (iv) and one extravascular (ev), is solved by Laplace transformation of two coupled differential equations. By assuming a functional form for the arterial input function (AIF), or by fitting to an experimentally determined AIF, this function is introduced into the solution as a boundary condition describing the time dependent input to the capillary. The solution to the tissue homogeneity model equations in Laplace space are numerically inverted to obtain the concentration of tracer in the ev space as a function of time and in the iv space as a function of both position and time. Magn Reson Med 45:42-45, 2001.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.342
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 designSimulation or modeling
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

Citations20
Published2001
Admission routes2
Has abstractyes

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