Denoising Low-Dose CT Images Using Multiframe Blind Source Separation and Block Matching Filter
Bibliographic record
Abstract
In order to reduce radiation effect during CT scans, low-dose techniques are employed in different medical imaging applications. But images in the low-dose CT tend to be rather noisy to be diagnostically useful. One way to improve the quality of low-dose CT images is to use a multiframe imaging technique. Here, we proposed a blind source separation (BSS) based CT image method using a multiframe low-dose image sequence. Because we found that BSS alone cannot denoise the image completely, we incorporated a nonlocal GroupWise block matching 3-D filter with BSS using the noise statistics, extracted from the noise components. With this technique, we produced a better quality image than that produced with a single frame half dose CT image and other multiframe imaging techniques, such as, frame averaging and applying the Wiener filter after BSS. Denoising performance, spatial resolution, and noise characteristics were measured by evaluating the peak signal to noise ratio, structural similarity index, modulation transfer function, and Bland-Altman analysis. This hybrid model shows a better denoising performance with less compromise in image details as more frames are included in an image sequence.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".