Perfusion CT in Acute Ischemic Stroke: A Qualitative and Quantitative Comparison of Deconvolution and Maximum Slope Approach
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: PCT postprocessing commonly uses either the MS or a variant of the DC approach for modeling of voxel-based time-attenuation curves. There is an ongoing discussion about the respective merits and limitations of both methods, frequently on the basis of theoretic reasoning or simulated data. We performed a qualitative and quantitative comparison of DC and MS by using identical source datasets and preprocessing parameters. MATERIALS AND METHODS: From the PCT data of 50 patients with acute ischemic stroke, color maps of CBF, CBV, and various temporal parameters were calculated with software implementing both DC and MS algorithms. Color maps were qualitatively categorized. Quantitative region-of-interest–based measurements were made in nonischemic GM and WM, suspected penumbra, and suspected infarction core. Qualitative results, quantitative results, and PCT lesion sizes from DC and MS were statistically compared. RESULTS: CBF and CBV color maps based on DC and MS were of comparably high quality. Quantitative CBF and CBV values calculated by DC and MS were within the same range in nonischemic regions. In suspected penumbra regions, average CBFDC was lower than CBFMS. In suspected infarction core regions, average CBVDC was similar to CBVMS. Using adapted tissue-at-risk/nonviable-tissue thresholds, we found excellent correlation of DC and MS lesion sizes. CONCLUSIONS: DC and MS yielded comparable qualitative and quantitative results. Lesion sizes indicated by DC and MS showed excellent agreement when using adapted thresholds. In all cases, the same therapy decision would have been made. AIF : arterial input function ASPECTS : Alberta Stroke Program Early CT Score CBF : cerebral blood flow CBV : cerebral blood volume DC : deconvolution GM : gray matter LMS : least mean square MIP : maximum intensity projection MS : maximum slope MTT : mean transit time NVT : nonviable tissue PCA : posterior cerebral artery PCT : perfusion CT rCBF : relative cerebral blood flow rCBV : relative cerebral blood volume rMTT : relative mean transit time rTTD : relative time to drain rTTP : relative time to peak ROI : region of interest SNR : signal-to-noise ratio SVD : singular value decomposition TAC : time attenuation curve TAR : tissue at risk TTD : time to drain TTP : time to peak TTS : time to start WM : white matter
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".