Cardiac PET/MR Attenuation Correction Using Dual Contrast CycleGAN with Novel μ-Map Loss
Bibliographic record
Abstract
Attenuation correction (AC) for positron emission tomography (PET) is usually based on a sequentially acquired CT image. In PET/MRI systems, however, AC is challenging as MR intensity has no direct relationship with tissue density. We propose an improved method for cardiac PET AC using simultaneous Dixon MR. We use a deep learning method, based on Cycle Generative Adversarial Networks (CycleGANs), for bidirectional MR ↔CT translation, trained and evaluated on a dataset of PET/CT and PET/MR scans from 10 subjects ( 5 cardiac patients and 5 healthy controls). For each PET/MR scan, the MR is translated into a synthetic CT image, from which a μ-map is produced for PET AC via standard reconstruction. We enhanced the original CycleGAN with a novel μ-map loss term that penalizes differences between true CT-derived and synthetic CT-derived μ-maps. Testing on 2 subjects showed our GAN-MR PET-AC images had lower mean SUV Error (14%) than vendor-provided PET-AC (27\%), with in-phase Dixon MR significantly outperforming out-of-phase Dixon MR. In future work, we will evaluate clinical variables from different AC methods, with potential applications in radiation therapy planning and other inter-modal image translation tasks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".