Reflection ultrasound computed tomography with sparse data by residual diffusion models
Bibliographic record
Abstract
Ultrasound computed tomography (UCT) has emerged as a promising modality to produce high-resolution and isotropic anatomical visuals. Yet, collecting extensive UCT data via numerous transmissions is time-intensive. Sparse transmission strategies offer a practical solution to streamline data collection, but traditional Delay-and-Sum (DAS) approaches can significantly compromise image quality. Addressing these challenges, this research introduces an efficient framework for the reconstruction of reflection UCT images utilizing sparse transmission data, grounded in a novel residual-based diffusion probabilistic model. This method employs a Markov chain to enable seamless transitions between high and low-resolution images by manipulating residuals. Through evaluation experiments utilizing in-vivo human limb imaging data, we demonstrate the ability of our proposed method to produce high-quality reflection UCT images with a reduced transmission count. Quantitative analysis demonstrates the method’s proficiency in reconstructing superior reflection UCT images from limited transmission data. The results imply this method has the potential to be incorporated into UCT imaging systems for clinical applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".