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Record W2035109599 · doi:10.1002/mrm.22253

Determination of the venous output function from MR signal phase: Feasibility for quantitative DCE-MRI in human brain

2010· article· en· W2035109599 on OpenAlexaff
Claire Foottit, Greg O. Cron, Matthew J. Hogan, Thanh Nguyen, I Cameron

Bibliographic record

VenueMagnetic Resonance in Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity of OttawaOttawa HospitalCarleton University
Fundersnot available
KeywordsSuperior sagittal sinusImaging phantomSagittal planeMagnetic resonance imagingPartial volumeBiomedical engineeringNuclear medicinePhysicsMedicineRadiologySurgery

Abstract

fetched live from OpenAlex

For dynamic contrast-enhanced MRI studies in the human brain, it is useful to measure the venous output function (VOF). The purpose of this work was to explore the feasibility of measuring the VOF using the MR signal phase (in absolute units of gadolinium concentration) in the superior sagittal sinus. Phantom experiments were performed to validate the technique for different superior sagittal sinus angles (theta = 0-48 degrees relative to the main magnetic field), different curvatures (straight or radius = 45 mm), and different spatial resolutions (2.2-5.5 mm, to study partial-volume effects). Additionally, the technique was tested on three patients. The phantom experimental results (echo time = 5.5 ms, theta <or= 21 degrees) agreed with theoretical predictions to within 10%. For the patient studies, the measured VOFs had reasonable amplitude and shape characteristics and the patients' superior sagittal sinus angles (<15 degrees) and curvatures (radii approximately 40 mm) were within the range explored with phantoms. Our results suggest that partial-volume contributions to the VOF will be <5% and that the VOF can be evaluated in vivo to within 10% error. In conclusion, it is highly feasible to use MR signal phase to measure the VOF in the superior sagittal sinus for human dynamic contrast-enhanced MRI.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.536
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.051
GPT teacher head0.373
Teacher spread0.322 · 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 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

Citations40
Published2010
Admission routes1
Has abstractyes

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