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Record W1989245102 · doi:10.1118/1.2241502

TU‐C‐330A‐09: Performance of CT and MR‐Based Assays for In Vivo Agent Concentration Quantitation

2006· article· en· W1989245102 on OpenAlexaff
Jinzi Zheng, Michael Dunne, Christine Allen, David A. Jaffray

Bibliographic record

VenueMedical Physics · 2006
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGadoliniumIn vivoIodineSpleenNuclear medicineChemistryHigh-performance liquid chromatographyMedicineChromatographyInternal medicine

Abstract

fetched live from OpenAlex

Purpose: To investigate the feasibility of performing longitudinal image‐based measurements using CT and MR to estimate contrast agent concentrations in organs and tissues in vivo. Method and Materials: A CT and MR contrast agent (200 mg/kg of iodine and 16 mg/kg of gadolinium encapsulated in liposomes) was administered intravenously to a 2 kg New Zealand White rabbit. At 5 minutes, 24, 48, 72, 96, 120 and 168 hours following contrast injection, the rabbit was imaged in CT (120kV, 200mA) and in MR (3D FSPGR, TR/TE= 9.8/4.3). 1mL of blood was collected from the same rabbit at each of the above times. The rabbit liver and spleen were harvested at the study end point (168 hours). The blood and tissues samples were then analyzed using high performance liquid chromatography (HPLC) to measure iodine content and inductively coupled plasma atomic emission spectrometry (ICP‐AES) to measure gadolinium content. Results: The differential blood CT attenuation vs. plasma iodine concentration correlation was well approximated with a linear fit (R2=0.9), while the differential blood MR signal intensity vs. plasma gadolinium concentration correlation was found to be nonlinear. These correlations were used to estimate the iodine and gadolinium content in the liver and the spleen. Using the CT correlation, the liver and the spleen iodine content were estimated to be 70% and 60% of the extracted amounts, respectively. The MR‐based method did not yield satisfactory gadolinium content estimates. Conclusion: This study attempted to correlate CT attenuation and MR signal increases to local iodine and gadolinium concentrations, respectively. In CT, the linear correlation obtained with blood data allowed for estimation of iodine content in the liver and spleen to 60–70% accuracy. In MR, although the presence of the contrast agent could be detected visually over a 7‐day period, additional effort is required to achieve reliable agent concentration estimations.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.325
Teacher spread0.297 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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