Advantages of frequency‐domain modeling in dynamic‐susceptibility contrast magnetic resonance cerebral blood flow quantification
Bibliographic record
Abstract
In dynamic-susceptibility contrast magnetic resonance perfusion imaging, the cerebral blood flow (CBF) is estimated from the tissue residue function obtained through deconvolution of the contrast concentration functions. However, the reliability of CBF estimates obtained by deconvolution is sensitive to various distortions including high-frequency noise amplification. The frequency-domain Fourier transform-based and the time-domain singular-value decomposition-based (SVD) algorithms both have biases introduced into their CBF estimates when noise stability criteria are applied or when contrast recirculation is present. The recovery of the desired signal components from amid these distortions by modeling the residue function in the frequency domain is demonstrated. The basic advantages and applicability of the frequency-domain modeling concept are explored through a simple frequency-domain Lorentzian model (FDLM); with results compared to standard SVD-based approaches. The performance of the FDLM method is model dependent, well representing residue functions in the exponential family while less accurately representing other functions.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".