Generation of <sup>18</sup> F-FDG PET standard scan images from short scans using cycle-consistent generative adversarial network
Bibliographic record
Abstract
Abstract Objective. To improve positron emission tomography (PET) image quality, we aim to generate images of quality comparable to standard scan duration images using short scan duration (1/8 and 1/16 standard scan duration) inputs and assess the generated standard scan duration images quantitative and qualitatively. Also, the effect of training dataset properties (i.e. body mass index (BMI)) on the performance of the model(s) will be explored. Approach. Whole-body PET scans of 42 patients (41 18 F-FDG and one 68 Ga-PSMA) scanned with standard radiotracer dosage were included in this study. One 18 F-FDG patient data was set aside and the remaining 40 patients were split into four subsets of 10 patients with different mean patient BMI. Multiple copies of a developed cycle-GAN network were trained on each subset to predict standard scan images using 1/8 and 1/16 short duration scans. Also, the models’ performance was tested on a patient scanned with the 68 Ga-PSMA radiotracer. Quantitative performance was tested using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and normalized root mean squared error (NRMSE) metrics, and two nuclear medicine specialists analyzed images qualitatively. Main results. The developed cycle-GAN model improved the PSNR, SSIM, and NRMSE of the 1/8 and 1/16 short scan duration inputs both 18 F-FDG and 68 Ga-PSMA radiotracers. Although, quantitatively PSNR, SSIM, and NRMSE of the 1/16 scan duration level were improved more than 1/8 counterparts, however, the later were qualitatively more appealing. SUV mean and SUV max of the generated images were also indicative of the improvements. The cycle-GAN model was much more capable in terms of image quality improvements and speed than the NLM denoising method. All results proved statistically significant using the paired-sample T-Test statistical test ( p -value < 0.05). Significance. Our suggested approach based on cycle-GAN could improve image quality of the 1/8 and 1/16 short scan-duration inputs through noise reduction both quantitively (PSNR, SSIM, NRMSE, SUVmean, and SUVmax) and qualitatively (contrast, noise, and diagnostic capability) to the level comparable to the standard scan-duration counterparts. The cycle-GAN model(s) had a similar performance on the 68 Ga-PSMA to the 18 F-FDG images and could improve the images qualitatively and quantitatively but requires more extensive study. Overall, images predicted from 1/8 short scan-duration inputs had the upper hand compared with 1/16 short scan-duration inputs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".