Subject-specific patch-based denoising for contrast-enhanced cardiac MR images
Bibliographic record
Abstract
Many patch-based techniques in imaging, e.g., Non-local means denoising, require tuning parameters to yield optimal results. In real-world applications, e.g., denoising of MR images, ground truth is not generally available and the process of choosing an appropriate set of parameters is a challenge. Recently, Zhu et al. proposed a method to define an image quality measure, called <i>Q</i>, that does not require ground truth. In this manuscript, we evaluate the effect of various parameters of the NL-means denoising on this quality metric <i>Q</i>. Our experiments are based on the late-gadolinium enhancement (LGE) cardiac MR images that are inherently noisy. Our described exhaustive evaluation approach can be used in tuning parameters of patch-based schemes. Even in the case that an estimation of optimal parameters is provided using another existing approach, our described method can be used as a secondary validation step. Our preliminary results suggest that denoising parameters should be case-specific rather than generic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".