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Diffusion coefficients of articular cartilage for different CT and MRI contrast agents

2010· article· en· W1993711449 on OpenAlexaff
K.A.M. Kulmala, Rami K. Korhonen, Petro Julkunen, Jukka S. Jurvelin, Thomas Quinn, Heikki Kröger, Juha Töyräs

Bibliographic record

VenueMedical Engineering & Physics · 2010
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcGill University
FundersSigrid Juséliuksen Säätiö
KeywordsCartilageGadodiamideMagnetic resonance imagingDiffusionBiomedical engineeringEffective diffusion coefficientOsteoarthritisContrast (vision)Articular cartilageDiffusion MRIChemistryMaterials scienceNuclear magnetic resonanceAnatomyMedicinePathologyRadiology

Abstract

fetched live from OpenAlex

In contrast enhanced magnetic resonance imaging (MRI) and computed tomography (CT), the equilibrium distribution of anionic contrast agent is expected to reflect the fixed charged density (FCD) of articular cartilage. Diffusion is mainly responsible for the transport of contrast agents into cartilage. In osteoarthritis, cartilage composition changes at early stages of disease, and solute diffusion is most likely affected. Thus, investigation of contrast agent diffusion could enable new methods for imaging of cartilage composition. The aim of this study was to determine the diffusion coefficient of four contrast agents (ioxaglate, gadopentetate, iodide, gadodiamide) in bovine articular cartilage. The contrast agents were different in molecular size and charge. In peripheral quantitative CT experiments, penetration of contrast agent into the tissue was allowed either through the articular surface or through deep cartilage. To determine diffusion coefficients, a finite element model based on Fick's law was fitted to experimental data. Diffusion through articular surface was faster than through deep cartilage with every contrast agent. Iodide, being of atomic size, diffused into the cartilage significantly faster (q<0.05) than the other three contrast agents, for either transport direction. The diffusion coefficients of all clinical contrast agents (ioxaglate, gadopentetate and gadodiamide) were relatively low (142.8-253.7 μm(2)/s). In clinical diagnostics, such slow diffusion may not reach equilibrium and this jeopardizes the determination of FCD by standard methods. However, differences between diffusion through articular surface and deep cartilage, that are characterized by different tissue composition, suggest that diffusion coefficients may correlate with cartilage composition. Present method could therefore enable image-based assessment of cartilage composition by determination of diffusion coefficients within cartilage tissue.

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)0.003
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations58
Published2010
Admission routes1
Has abstractyes

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