Confidence-Aware 3D Spatial Compounding of 2D Ultrasound Images for Needle Shadow Removal
Bibliographic record
Abstract
Ultrasound (US) imaging clarity is often hindered by acoustic shadows caused by highly reflective structures, obscuring critical anatomy in the images. This issue is particularly problematic in needle-based interventions, where needle-induced shadows and reverberations can severely obstruct visualization, complicating the procedure and increasing the risk of misplacement. To address this problem, we propose a new 3D image compounding algorithm to remove needle shadows and allow the US probe to see behind reflective objects. Our approach acquires 2D US images from multiple imaging angles and computes the probability that the US wave has reached each pixel in the images. We then propose a nonlinear function to weight these 2D images based on their pixel intensity and acoustic signal consistency before they are combined to form a 3D volume. Regions corresponding to shadows are adaptively suppressed, while highly informative areas are compounded to create an accurate volumetric representation. This volume is then sliced along arbitrary imaging planes to form new, shadow-free images.Experimental validation in phantom tissue comparing the original US images with the images created with the proposed algorithm shows significant improvement in image clarity and tissue inclusion delineation. The proposed method is compared against other conventional image compounding techniques and shows improvements in image signal to noise ratio and normalized cross corelation. By enhancing the reliability of 3D US image compounding, the proposed multivariable, nonlinear weighting function contributes to more precise and accurate US image guidance for needle-based diagnostics and interventions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".