Quality of Computed Tomography Pulmonary Embolism and Aortic Phase Images Acquired with Power Injection via an Arm Port
Bibliographic record
Abstract
Abstract Background: The safety of power-injectable implanted arm ports is well established, but there is insufficient data to conclude that image quality of computed tomography resulting from contrast introduced via the port is of equal quality to images derived from contrast introduced via traditional peripheral access. The objective of this study was to determine whether the image quality of computed tomography pulmonary embolism and computed tomography aorta studies would differ when injecting contrast via an implanted arm port vs a peripheral intravenous site. We hypothesized that injecting via an implanted arm port would produce better-quality images, the result of more appropriate timing and less streak artifact. Methods: Scans from a provincial database search for patients who underwent a computed tomography pulmonary embolism or aorta study with contrast injection via the implanted arm port and thin section images available, were reviewed (pulmonary embolism studies n = 3, aorta studies n = 3). Only a limited number of patients were available for review because there are currently few patients with these ports in place and we limited evaluation to thin section images. Comparison was made with 6 control patients who did not have a port and had received a peripheral arm intravenous contrast injection for these study types. Objective measurements included signal-to-noise ratio and contrast-to-noise ratio of the pulmonary arteries (4 sites) and aorta (2 sites) as appropriate for scan type. Subjective analysis of image quality was performed by 2 radiologists. Results: Although sample size was limited, the implanted arm port group had similar or higher mean signal-to-noise ratio and contrast-to-noise ratio values at all sites. Subjective assessments showed the implanted arm port group to have similar or better opacification and diagnostic confidence; similar or less streak artifact was also observed at each of the sites. Conclusions: These exploratory results suggest that studies with implanted arm port injection can generate high-quality images on both objective and subjective assessment, similar to, or possibly better than, images generated from usual peripheral intravenous access for contrast injection.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".