Assessment of a novel compressed sensing algorithm for reconstructing phase contrast CT images of the canine prostate
Bibliographic record
Abstract
Phase contrast computed tomography (PC CT) represents a generational advance in medical and anatomical imaging with greatly improved spatial and soft tissue contrast resolution. Achieving good image quality with PC CT can require up to 4000 image projections leading to a high radiation dose and long image acquisition time. New image reconstruction methods would greatly reduce the number of projections without substantially sacrificing image quality. In sparse‐view imaging, strong streak artifacts may appear in conventionally reconstructed images, compromising image quality. Compressed sensing algorithm has shown potential to accurately recover images from highly incomplete data. The main feature of our algorithm is the use of two sparsity transforms: discrete wavelet transform and discrete gradient transform, both of which are proven to be powerful sparsity transforms. We reconstructed canine prostate images with filtered back projection and our compressed sensing algorithm using 50, 100, 120, 150, and 180 projections. Our results demonstrate that our proposed method can produce satisfactory images from only 150 projections. Research funding was provided by the Saskatchewan Health Research Fund.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".