Bibliographic record
Abstract
An imaging system that offers the potential to combine the strengths of US and MR imaging is proposed. The purpose of this study was to characterize a prototype system and to develop methods to perform simultaneous ultrasound and MR imaging without significant interference between the two modalities. A computer-controlled device consisting of an MRI-compatible linear positioning system and diagnostic US transducer was designed and built to perform ultrasound imaging inside an MR imager. The ultrasound transducer had a 5 MHz frequency, with a 50-mm focal length. The MR imager was a clinical 1.5 T closed-bore scanner. No noise related to the motor movements or the materials was observed. The broadband excitation pulse used to excite the transducer was detected in the MR images. Significant electrical interference from the MRI was also observed in the ultrasound imaging signals. By synchronizing US and MR acquisition, simultaneous imaging could be performed by sending the US pulses between MR RF pulses. A phantom with multiple nylon strings at different spacing (2-12 mm) and diameters (0.2, 0.4 mm) was imaged with both systems. The strings with a 0.4 mm diameter could be observed in the MR image but the 0.2-mm diameter strings were not visible. Ultrasound imaging during clinical MR imaging was feasible and was more sensitive to structures with a large acoustic impedance mismatch. The setup developed in this study creates an opportunity to perform MRI-guided ultrasound imaging or vice-versa
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".