Short-Axis PET Image Quality Improvement by Attention CycleGAN Using Total-Body PET
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The quality of positron emission tomography (PET) imaging is positively correlated with scanner sensitivity, which is closely related to the axial field of view (FOV). Conventional short-axis PET scanners (200-350 mm FOV) reduce the imaging quality during fast scanning (2-3 minutes) due to the limitation of FOV, which reduce the reliability of diagnosis. To overcome hardware limitations and improve the image quality of short-axis PET scanners, we propose a supervised deep learning model, CycleAGAN, which is based on a cycle-consistent adversarial network (CycleGAN). We introduced the attention mechanism into the generator and focus on channel and spatial representative features and supervised learning using pairs of data to maintain the spatial consistency of the generated images with the ground truth. The imaging information of 386 patients from Henan Provincial People's Hospital was prospectively included as the dataset in this study. The training data come from the total-body PET scanner uEXPLORER. The proposed CycleAGAN is compared with traditional gray-level-based methods and learning-based methods. The results confirm that CycleAGAN achieved the best results on SSIM and NRMSE and achieved the closest distribution to ground truth in expert rating. The proposed method is not only able to improve the image quality of PET scanners with 320 mm FOV but also achieved good results on shorter FOV scanners. Patients and radiologists can benefit from the computer-aided diagnosis (CAD) system integrated with CycleAGAN.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it