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Record W1984655248 · doi:10.1186/1532-429x-11-s1-p287

Assessment of transmural perfusion effect with Blood Oxygen Level-Dependent Cardiovascular Magnetic Resonance Imaging (BOLD-CMR)

2009· article· en· W1984655248 on OpenAlexaff
Jacqueline Flewitt, Matthias Vöhringer, Jordin Green, Matthias G. Friedrich

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2009
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsCARE Canada
Fundersnot available
KeywordsAngiologyMedicineBlood-oxygen-level dependentMagnetic resonance imagingPerfusionCardiologyPerfusion scanningInternal medicineFunctional magnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

OLD-CMR has successfully been used for the diagnosis of perfusion deficits caused by coronary stenosis. New BOLD-CMR sequences show improved image quality for assessing myocardial oxygenation with excellent spatial resolution, offering a distinct advantage over other image modalities. We hypothesized that these new BOLD-CMR techniques can detect differences in oxygenation throughout the myocardial wall. To investigate the epicardial and endocardial oxygenation with a new BOLD-CMR sequence in healthy volunteers compared to patients with suspected coronary artery disease (CAD). 11 healthy volunteers (mean age 29 ± 4 years) and 11 patients with suspected CAD (mean age 59 ± 8 years) who all had abnormal perfusion results on SPECT were recruited for scanning. Using a clinical 1.5 T MRI system (MAGNETOM Avanto, Siemens Healthcare, Erlangen, Germany), SSFP BOLD-CMR was performed on a mid left ventricular short axis slice at baseline and during adenosine infusion (140 micro-g/kg). Typical scan parameters were: Field-of view 193 × 280 mm; matrix size 106 × 192; slice thickness 10 mm; T R /T E 5.8/2.9 ms; flip angle 90°; typical breath-hold duration 14 s. Images were analyzed using clinically validated software (cmr , Circle Cardiovascular Imaging Inc., Calgary, Canada). After automatic definition of the subendocardial 50% and the subepicardial 50% of the wall thickness, the BOLD signal intensity (SI) for each was analyzed and the relative change from baseline during adenosine infusion was calculated. Late enhancement was performed to ensure that patients did not have an infarct in the selected slice for analysis. Image quality was excellent in 8 patients and all healthy volunteers. Three patients were excluded due to motion artifacts on the stress portion of the exam. Results are illustrated in Figure 1 and summarized in Table 1 as means and standard error (SE). Heart rate (HR) increased from baseline with the infusion of adenosine. The mean HR increase was 48 ± 8% for the suspected CAD patients, and 29 ± 8% for the healthy volunteers. The increase in BOLD-SI of the whole myocardium from baseline to adenosine was similar in CAD patients (19.6 ± 4.8%) and in healthy volunteers (21.2 ± 3.0%). In healthy volunteers there was only a marginal difference between the BOLD-SI increase in the endocardium only, and epicardium only (mean difference 0.2 ± 1.6%, p = 0.88). However, in the suspected CAD patients, the endocardial BOLD-SI increases were significantly lower than epicardial BOLD-SI increases (mean difference 5.7 ± 2.2%, p = 0.03). BOLD SI changes for suspected CAD patients and healthy volunteers . A state-of-the-art BOLD-sensitive MRI sequence, with its improved image quality and excellent spatial resolution, can detect differences in epicardial and endocardial myocardial oxygenation and are feasible for the clinical setting.

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)0.001
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.258
Teacher spread0.250 · 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".

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Citations0
Published2009
Admission routes1
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