Development and translation of 3D ultrasound-based imaging systems for diagnostic and image-guided interventions
Bibliographic record
Abstract
The past 50 years have witnessed unprecedented developments in new imaging systems that use 3D visualization. These new technologies have revolutionized diagnostic radiology, as they provide the clinician with information about the interior of the human body never before available. Conventional 2D ultrasound imaging is an important costeffective technique used routinely in the management of several diseases and is used globally in hospitals and diagnostic clinics. However, 2D viewing of 3D anatomy, using conventional ultrasound, limits our ability to quantify and visualize the anatomy and guide therapy, because multiple 2D images must be integrated mentally. This practice is inefficient and leads, at times, to variability and incorrect diagnoses. Also, since the 2D ultrasound image represents a thin plane at an arbitrary angle in the body, reproduction of this plane at a later time for monitoring disease progression or regression is difficult. Investigators and companies have addressed these limitations by developing 3D ultrasound-based devices and techniques. In this paper, we describe our developments in 3D ultrasound (3D US) imaging instrumentation and techniques. In our approach, the conventional ultrasound transducer is scanned mechanically using various external fixtures. The 2D images are digitized and then reconstructed in real-time into a 3D image, which can be viewed and manipulated interactively. We describe the use of 3D ultrasound for diagnosis, and image-guided intervention with four examples: prostate biopsy, whole breast imaging, imaging of musculoskeletal joints, and imaging and analysis of carotid arterial atherosclerotic plaques.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".