Improving three-dimensional automated breast ultrasound resolution with orthogonal images
Bibliographic record
Abstract
With increasing evidence for supplemental ultrasound (US) for breast cancer screening in women with dense breasts, there is an interest in developing more robust and cost-effective techniques. Compared with handheld US, automated breast ultrasound (ABUS) shows improvements in detection, reproducibility, and operator dependence. However, limitations exist as high-quality image acquisition is still reliant on operator training and patient positioning. Moreover, installation of current commercial systems is expensive, and they lack point-of-care capabilities, limiting their bedside utility. We developed a dedicated three-dimensional (3D) ABUS device that contains a wearable patient-conforming 3D-printed dam, compression assembly, and motorized 3DUS scanner. Acquisition involves acquiring 2DUS images at a fixed spatial interval and reconstructing them into a 3DUS image. While the 3DUS image has a high in-plane resolution, its out-ofplane (elevational) US resolution in the reconstruction plane is poor. We hypothesize that combining orthogonal images can improve 3DUS image resolution by recovering some out-of-plane resolution. With orthogonal 3DUS images occupying the same volume, the intensity at any 3D voxel coordinate can be computed from a spherical-weighted function of the voxel intensities from the two original 3DUS images. In this paper, we describe the dedicated 3D ABUS device, its orthogonal acquisition, and the combination approach for creating a 3D complementary breast ultrasound (CBUS) image. We perform experiments to evaluate their impact on 3D image resolution. The proposed CBUS method was evaluated with orthogonally acquired craniocaudal and mediolateral 3DUS images of an angular wire phantom, then calculating the full width at half maximum (FWHM) of the line spread function for each wire. Our results show that 3D CBUS images with orthogonal 3DUS images improves resolution uniformity by recovering some out-of-plane resolution.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".