Volume Imaging in the Abdomen With Ultrasound: How We Do It
Bibliographic record
Abstract
OBJECTIVE: The objective of our study was to evaluate the feasibility of volumetric acquisition of the abdominal organs using performance guidelines that we developed in our preliminary experience. MATERIALS AND METHODS: Mechanical volumetric acquisitions of each abdominal organ, including the liver, gallbladder, pancreas, kidneys, spleen, bowel, and aorta, were performed in 200 consecutive patients. RESULTS: One thousand four hundred fifty-four volume data sets were graded for feasibility of performance and technical adequacy from I (impossible, incomplete) to V (excellent, complete). The most successfully imaged organ was the right kidney (grades IV and V, 95.0%) and the least successfully imaged, the spleen (grades IV and V, 69.0%). Very good to excellent grades (IV and V) were obtained in 1,215 (83.6%) of the 1,454 volumes. One hundred twelve (7.7%) of the 1,454 volumes were failures (grades I and II). The three organs with the highest success compared with the right kidney were the left kidney, gallbladder, and liver. The data sets of all the other organs showed a statistically significant difference in the feasibility of performance from the right kidney. Liver acquisition failures were associated with end-stage liver cirrhosis (n = 6), fatty liver (n = 3), and obesity (n = 3). Other acquisition failures, similar to conventional sonography, were associated with bowel gas interference and poor acoustic window. The technical limitations include poor resolution in the B and C planes and a limited range of frequencies; these limitations can be overcome in the future with matrix transducers and introduction of the technology to a broader frequency range. CONCLUSION: Volumetric acquisition in the abdomen performed using defined guidelines is feasible with recognized limitations. Technology advances will improve this imaging technique in the future.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".