Functional CT in lung with a conventional scanner: simulations and sampling considerations
Bibliographic record
Abstract
Due to rapid transit times, motion artefacts from breathing and the low signal intensity, functional computed tomography (f-CT) studies in lung tissue remain challenging with conventional CT scanners. The purpose of this study is to examine the accuracy of parameter estimates when performing deconvolution analysis with signals from lung tissue. The effects of partial volume averaging in lung tissue, differing transit times, variable vascular and capillary responses, expected noise levels, differing sampling rate and durations were simulated on a computer. Deconvolution using singular-value decomposition (SVD) analysis was performed for realistic lung signals using published and measured values of the arterial input and noise levels. The accuracy, bias and variance of the estimated residue functions and their associated parameter estimates were evaluated. We find that f-CT signals may be measured and analysed using SVD and other deconvolution approaches. Functional CT signals in the lung may be analysed provided that the rise and fall of the tissue and input curves are well sampled (regardless of sampling rate) and noise levels in the lung ROI tissue are approximately 20 HU or less, even for regions of interest that are mostly occupied by air. Estimates of the mean tissue transit time (MTT) are insensitive to air volume. Other decovolution methods such as fast Fourier transform methods provide more accurate estimates of PBF, whereas SVD approaches provide more accurate estimates of pulmonary blood volume and MTT. F-CT of the lung with a conventional scanner should be possible, when the extra dose is not a consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".