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Record W4362734759 · doi:10.1117/12.2653141

Improving three-dimensional automated breast ultrasound resolution with orthogonal images

2023· article· en· W4362734759 on OpenAlexaff
Claire K. Park, Tiana Trumpour, Igor Gyacskov, Jeffrey Bax, David Tessier, Lori Gardi, Madeline Ico, Aaron Fenster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsWestern UniversityRobarts Clinical Trials
Fundersnot available
KeywordsComputer vision3D ultrasoundArtificial intelligenceComputer scienceVoxelImage resolutionImage qualityBreast ultrasoundImaging phantomIterative reconstructionUltrasoundImage (mathematics)Breast cancerMedicineNuclear medicineRadiologyMammography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.235
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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