Is correction necessary when clinically determining quantitative cerebral perfusion parameters from multi-slice dynamic susceptibility contrast MR studies?
Bibliographic record
Abstract
Several groups have modified the standard singular value decomposition (SVD) algorithm to produce delay-insensitive cerebral blood flow (CBF) estimates from dynamic susceptibility contrast (DSC) perfusion studies. However, new dependences of CBF estimates on bolus arrival times and slice position in multi-slice studies have been recently recognized. These conflicting findings can be reconciled by accounting for several experimental and algorithmic factors. Using simulation and clinical studies, the non-simultaneous measurement of arterial and tissue concentration curves (relative slice position) in a multi-slice study is shown to affect time-related perfusion parameters, e.g. arterial-tissue-delay measurements. However, the current clinical impact of relative slice position on amplitude-related perfusion parameters, e.g. CBF, can be expected to be small unless any of the following conditions are present individually or in combination: (a) high concentration curve signal-to-noise ratios, (b) small tissue mean transit times, (c) narrow arterial input functions or (d) low temporal resolution of the DSC image sequence. Recent improvements in magnetic resonance (MR) technology can easily be expected to lead to scenarios where these effects become increasingly important sources of inaccuracy for all perfusion parameter estimates. We show that using Fourier interpolated (high temporal resolution) residue functions reduces the systematic error of the perfusion parameters obtained from multi-slice studies.
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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 | 0.024 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".