CT-Less Attenuation Correction Using Multiview Ensemble Conditional Diffusion Model on High-Resolution Uncorrected PET Images
Bibliographic record
Abstract
Positron emission tomography requires precise quantification to ensure reliable diagnostic outcomes and treatment monitoring. The attenuation of emitted photons in tissues before detector capture poses a significant challenge. Without appropriate correction methods, attenuation can introduce quantitative bias, making it difficult to differentiate benign from malignant conditions, and potentially leading to misdiagnosis. Traditional attenuation correction relies on co-computed tomography (CT) scans, providing anatomical information to calculate signal loss. Unfortunately, this approach exposes patients to additional radiation, is prone to misalignment between scans, and requires expensive hardware. Recent innovations in deep learning offer an alternative solution through pseudo-CT generation. Our research demonstrates that Conditional Denoising Diffusion Probabilistic Models (DDPMs) significantly outperform previous state-of-the-art UNet approaches. By utilizing all three orthogonal views from non-attenuation-corrected PET images, the DDPM approach combined with ensemble voting generates higher quality pseudo-CT images with reduced artifacts and improved slice-to-slice consistency. Results from a study of 159 head scans acquired on the Siemens Biograph Vision PET/CT scanner show both qualitative and quantitative improvements in pseudo-CT generation using this diffusion model approach with an average absolute error of$(0.34 \pm 0.35) \%$for a typical PET slice compared to the PET image reconstructed with the CT.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".