Image Quality Assessment of Reduced-Dose Pediatric Tc-DMSA Renal SPECT Imageswith Clinical Readers
Bibliographic record
Abstract
In prior work we demonstrated the feasibility of applying a deep learning (DL) denoising network for suppressing the elevated imaging noise in reduced-dose pediatric renal studies with single photon emission computed tomography (SPECT) using 99mTclabelled dimercaptosuccinic acid (DMSA), where quantitative image metrics were used to assess the image fidelity of reduced studies with respect to their standard-dose counterparts. In this study we further assess the clinical adequacy of the reconstructed images from reduced-dose studies with experienced nuclear medicine physicians. For this purpose, we conducted a reader study in which quarter-dose images from a set of 45 subjects were compared with their standard-dose counterparts in a pairwise fashion, (with and without DL denoising), in terms of their diagnostic quality on a 5-point Likert scale. The results demonstrate that, without DL denoising, the quarter-dose images were judged to be worse in quality than the standard dose$(p-\text{value} <0.001)$; however, with DL denoising, no statistical difference was observed between the quarter-dose and the standard-dose images$({p}-\text{value} =0.254)$.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".