Comparison of free breathing versus breath-hold in perfusion imaging using dynamic volume CT
Bibliographic record
Abstract
OBJECTIVES: To compare two scanning protocols (free breathing versus breath-hold) for perfusion imaging using dynamic volume computed tomography (CT) and to evaluate their effects on image registration. MATERIAL AND METHODS: Forty patients underwent dynamic volume CT for pancreatic perfusion analysis and were randomly assigned to either a shallow-breathing (I) or breath-hold (II) group. Both dynamic CT protocols consisted of 17 low-dose volumetric scans. Rigid image registration was performed by using the volume with highest aortic attenuation as reference. All other volumes were visually matched with the pancreatic lesion serving as the volumetric region of interest. The overall demand for post-processing per patient was calculated as the median of three-dimensional vector lengths of all volumes in relation to the relative patient origin. The number of volumes not requiring registration was recorded per group. RESULTS: Registration mismatch for groups I and II was 2.61 mm (SD, 1.57) and 4.95 mm (SD, 2.71), respectively (P < 0.005). Twenty-eight volumes in group I (8.2%) and 47 volumes in group II (14.1%) did not require manual registration (P = 0.014). CONCLUSION: Shallow breathing during dynamic volume CT scanning reduces the overall demand for motion correction and thus may be beneficial in perfusion imaging of the pancreas MAIN MESSAGES: • Shallow breathing during perfusion CT scanning reduces the overall demand for motion correction. • Shallow breathing may be beneficial in perfusion imaging of the pancreas. • Image registration is crucial for CT perfusion imaging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".