Effect of Regional Tracer Delay on CBF in Healthy Subjects Measured with Dynamic Susceptibility Contrast-Enhanced MRI: Comparison with 15O-PET
Bibliographic record
Abstract
PURPOSE: Deconvolution based on truncated singular value decomposition (SVD deconvolution) is a promising method for measuring cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI), but it has proved extremely sensitive to tracer delay. The purpose of this study was to investigate the effect of regional tracer delay on CBF determined by SVD deconvolution (SVD-CBF). SVD-CBFs with and without correction for the delay were compared with CBF measured by positron emission tomography (PET-CBF), which is regarded as the gold standard for quantification of CBF. METHODS: Perfusion MRI and PET were performed on seven healthy men. In the PET study, the CBF image was obtained with bolus injection of H2(15)O and continuous arterial sampling. In the DSC-MRI study with bolus injection of Gd-based contrast agent, dynamic perfusion data were obtained with a 1.5T scanner at 1-s intervals by means of gradient-echo echo-planar imaging. CBF was determined by the SVD deconvolution method with and without correction for the tracer delay. Region-of-interest measurements were obtained in the gray matter (cerebral cortex in the middle cerebral artery territory) and white matter (centrum semiovale). RESULTS: Tracer delay was significantly longer in white matter than in gray matter (1.45+/-0.61 s vs. 0.59+/-0.35 s, P<0.01). Correction for the delay increased SVD-CBF in the white matter and consequently reduced the gray-to-white SVD-CBF ratio. The uncorrected gray-to-white SVD-CBF ratio was significantly larger than that of PET-CBF (3.33+/-0.66 vs. 2.54+/-0.49, P<0.01). However, the gray-to-white delay-corrected SVD-CBF ratio did not differ significantly from that of PET-CBF (2.83+/-0.31 vs. 2.54+/-0.49, P=0.10). CONCLUSION: The tracer delay in DSC-MRI causes errors in CBF estimates, even in healthy persons, and therefore should be corrected for when delay-sensitive deconvolution, such as SVD deconvolution, is used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".