3D MR DSA: Effects of injection protocol and image masking
Bibliographic record
Abstract
The purpose of this study was to investigate the effect on three-dimensional (3D) magnetic resonance digital subtraction angiography (MR DSA) images of various injection protocol parameters (ie, injection order, volume, and rate), as well as image masking. The pelves of 10 normal volunteers were scanned using seven different contrast agent volume/injection rate combinations. Subtraction of a precontrast mask image resulted in vascular image contrast improvements of between 4.0 and 7.7 times. Image quality and smaller vessel image contrast in the masked data decreased with increasing injection number. Data acquired with a high (0.150 mmol kg(-1)) volume yielded the highest quality images, although only small nonsignificant differences in image quality and large vessel conspicuity were found between images obtained using the high and medium (0.075 mmol kg(-1)) volumes. Images acquired with a low (0. 038 mmol kg(-1)) volume, while of lower image contrast, were judged to be of reasonable quality, especially when acquired as the first or second injection. Injection rate (1 ml s(-1), 2 ml s(-1), and 4 ml s(-1)) was not found to affect the images significantly, although selection of an injection rate that gave an injection duration of approximately 10 seconds tended to give better vascular image contrast. Based on these data, a series of escalating volumes for multi-injection examination is proposed. J. Magn. Reson. Imaging 2000;12:476-487.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".