Current Applications of PET/MR: Part II: Clinical Applications II
Bibliographic record
Abstract
Due to the major improvements in the hardware and image reconstruction algorithms, positron emission tomography/magnetic resonance imaging (PET/MR) is now a reliable state-of-the-art hybrid modality in medical practice. Currently, it can provide a broad range of advantages in preclinical and clinical imaging compared to single-modality imaging. In the second part of this review, we discussed the further clinical applications of PET/MR. In the chest, PET/MR has particular potential in the oncology setting, especially when utilizing ultrashort/zero echo time MR sequences. Furthermore, cardiac PET/MR can provide reliable information in evaluating myocardial inflammation, cardiac amyloidosis, myocardial perfusion, myocardial viability, atherosclerotic plaque, and cardiac masses. In gastrointestinal and hepato-pancreato-biliary malignancies, PET/MR is able to precisely detect metastases to the liver, being superior over the other imaging modalities. In genitourinary and gynaecology applications, PET/MR is a comprehensive diagnostic method, especially in prostate, endometrial, and cervical cancers. Its simultaneous acquisition has been shown to outperform other imaging techniques for the detection of pelvic nodal metastases and is also a reliable modality in radiation planning. Lastly, in haematologic malignancies, PET/MR can significantly enhance lymphoma diagnosis, particularly in detecting extra-nodal involvement. It can also comprehensively assess treatment-induced changes. Furthermore, PET/MR may soon become a routine in multiple myeloma management, being a one-stop shop for evaluating bone, bone marrow, and soft tissues.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".