Multimodal Wearable Whole-Breast 3D Ultrasound System for Diagnosis and Needle Interventions
Bibliographic record
Abstract
Breast cancer is the most common cancer in women, but screening and biopsy accuracy are limited, especially for dense breasts, highlighting the need for cost-effective, accessible solutions. We developed a cost-effective, wearable 3D automated breast ultrasound (3D ABUS) device compatible with any commercial ultrasound (US) system and showed its potential for point-of-care supplemental breast cancer screening. With USMRI image registration, the system could enable US-guided biopsy of MRI-visible lesions, reducing the need for resourceintensive MRI-based biopsy. Our goal is to develop and integrate an US-MRI image registration process into the 3D ABUS system and to characterize the system’s novel 3D Doppler capabilities. The biopsy system’s registration capabilities were tested using a breast phantom with inclusions. Centroids of segmented inclusions were used to calculate US-MRI target registration error (TRE) and fiducial localization error (FLE). 3D power Doppler (PD) and superb microvasculature imaging (SMI) images were acquired in a custom flow phantom, and system feasibility was tested in healthy volunteers. The system displayed high registration accuracy in 3D ABUS-MRI lesions. The 3D PD and SMI images from the vascular phantom were able to be viewed dynamically in oblique and non-oblique planes using 3D visualization software. Images acquired in healthy volunteers demonstrated clear visualization of anatomical structures. The system was effective in registering lesions in US-MRI images and in acquiring 3D Doppler and SMI images. These developments show the potential of our system for supplemental breast cancer screening and as costeffective alternative to MRI-guided breast biopsy, particularly in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.012 | 0.004 |
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".