Applying the transient error reconstruction algorithm in the assessment of the cerebral blood flow
Bibliographic record
Abstract
The brain perfusion level, characterized by the cerebral blood flow (CBF) parameter, is a known indicator of blood supply in cerebral ischemic stroke. In magnetic resonance dynamic susceptibility contrast (DSC) perfusion studies the CBF parameter is estimated from the residue function obtained from deconvolving the tissue concentration curve by the arterial concentration curve. Deconvolution is a noise sensitive process and ensuring algorithmic stability leads to CBF biases. Distortions are introduced by noise reducing techniques in both the time-domain singular value decomposition (SVD) and frequency-domain based Fourier transform (FT) deconvolution approaches. We provide preliminary results of using the transient error reconstruction algorithm (TERA), an auto regressive moving average based technique, to compensate for these distortions. TERA is applied to determine the characteristics of the low-noise low frequency components of the residue function and then used to reconstruct the time-domain residue function. Results using noise-free signals indicate that the CBF estimates determined using TERA were less sensitive to the tissue mean transit time (MTT) than the time-domain SVD techniques. The difficulties encountered when applying TERA approach to signals with noise levels commonly found in MR perfusion studies are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".