Advances in Multimodality Imaging Through a Hybrid PET/MRI System
Bibliographic record
Abstract
The development of integrated imaging systems for magnetic resonance imaging (MRI) and positron emission tomography (PET) is currently being explored in a number of laboratories and industrial settings. PET/MRI scanners for both preclinical and human research applications are being developed. PET/MRI overcomes many limitations of PET/computed tomography (CT), such as limited tissue contrast and high radiation doses delivered to the patient or the animal being studied. In addition, recent PET/MRI designs allow for simultaneous rather than sequential acquisition of PET and MRI data, which could not have been achieved through a combination of PET and CT scanners. In a combined PET/CT scanner, while both scanners share a common patient bed, they are hard-wired back-to-back and therefore do not allow simultaneous data acquisition. While PET/MRI offers the possibility of novel imaging strategies, it also creates considerable challenges for acquiring artifact-free images from both modalities. In this review, we discuss motivations, challenges, and potential research applications of developing PET/MRI technology. A brief overview of both MRI and PET is presented and preclinical and clinical applications of PET/MRI are identified. Finally, issues and concerns about image quality, clinical practice, and economic feasibility are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".