Spatially tracked whole‐breast three‐dimensional ultrasound system toward point‐of‐care breast cancer screening in high‐risk women with dense breasts
Bibliographic record
Abstract
Abstract Background Mammographic screening has reduced mortality in women through the early detection of breast cancer. However, the sensitivity for breast cancer detection is significantly reduced in women with dense breasts, in addition to being an independent risk factor. Ultrasound (US) has been proven effective in detecting small, early‐stage, and invasive cancers in women with dense breasts. Purpose To develop an alternative, versatile, and cost‐effective spatially tracked three‐dimensional (3D) US system for whole‐breast imaging. This paper describes the design, development, and validation of the spatially tracked 3DUS system, including its components for spatial tracking, multi‐image registration and fusion, feasibility for whole‐breast 3DUS imaging and multi‐planar visualization in tissue‐mimicking phantoms, and a proof‐of‐concept healthy volunteer study. Methods The spatially tracked 3DUS system contains (a) a six‐axis manipulator and counterbalanced stabilizer, (b) an in‐house quick‐release 3DUS scanner, adaptable to any commercially available US system, and removable, allowing for handheld 3DUS acquisition and two‐dimensional US imaging, and (c) custom software for 3D tracking, 3DUS reconstruction, visualization, and spatial‐based multi‐image registration and fusion of 3DUS images for whole‐breast imaging. Spatial tracking of the 3D position and orientation of the system and its joints (J1–6) were evaluated in a clinically accessible workspace for bedside point‐of‐care (POC) imaging. Multi‐image registration and fusion of acquired 3DUS images were assessed with a quadrants‐based protocol in tissue‐mimicking phantoms and the target registration error (TRE) was quantified. Whole‐breast 3DUS imaging and multi‐planar visualization were evaluated with a tissue‐mimicking breast phantom. Feasibility for spatially tracked whole‐breast 3DUS imaging was assessed in a proof‐of‐concept healthy male and female volunteer study. Results Mean tracking errors were 0.87 ± 0.52, 0.70 ± 0.46, 0.53 ± 0.48, 0.34 ± 0.32, 0.43 ± 0.28, and 0.78 ± 0.54 mm for joints J1–6, respectively. Lookup table (LUT) corrections minimized the error in joints J1, J2, and J5. Compound motions exercising all joints simultaneously resulted in a mean tracking error of 1.08 ± 0.88 mm (N = 20) within the overall workspace for bedside 3DUS imaging. Multi‐image registration and fusion of two acquired 3DUS images resulted in a mean TRE of 1.28 ± 0.10 mm. Whole‐breast 3DUS imaging and multi‐planar visualization in axial, sagittal, and coronal views were demonstrated with the tissue‐mimicking breast phantom. The feasibility of the whole‐breast 3DUS approach was demonstrated in healthy male and female volunteers. In the male volunteer, the high‐resolution whole‐breast 3DUS acquisition protocol was optimized without the added complexities of curvature and tissue deformations. With small post‐acquisition corrections for motion, whole‐breast 3DUS imaging was performed on the healthy female volunteer showing relevant anatomical structures and details. Conclusions Our spatially tracked 3DUS system shows potential utility as an alternative, accurate, and feasible whole‐breast approach with the capability for bedside POC imaging. Future work is focused on reducing misregistration errors due to motion and tissue deformations, to develop a robust spatially tracked whole‐breast 3DUS acquisition protocol, then exploring its clinical utility for screening high‐risk women with dense breasts.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".