High-field magnetic resonance imaging: Challenges, advantages, and opportunities for novel contrast agents
Bibliographic record
Abstract
There is a need for non-invasive diagnostic tools to detect and monitor the occurrence of diseases. Ideally, this can be done without resorting to ionizing radiation, especially when multiple rounds of imaging are required. Magnetic resonance imaging (MRI), a form of three-dimensional nuclear magnetic resonance, has become a common tool of choice for diagnosticians. Due to the low contrast difference between healthy and diseased tissue, contrast agents—magnetic species administered to the patient prior to imaging—are routinely used for contrast improvement. High-field (B0 ≥ 4.7 T, 1H Larmor frequency ≥ 200 MHz) MRI offers advantages in terms of better signal-to-noise ratio, as well as improved spectral resolution for certain applications. New contrast agents are being developed for high-field MRI, the topic of this review. After discussing the purpose of contrast agents and the advantages and potential issues of high-field MRI, we discuss recent developments in the field of contrast agent design, synthesis, and applications, citing examples of high-field MRI-ready molecular contrast agents, as well as nanoparticulate contrast agents based on various inorganic materials (e.g., coordination polymers, transition metal oxides, or lanthanide halides). We will discuss how certain aspects (composition, shape, ligands) affect the contrasting abilities of these agents. Finally, we highlight recent developments in the promising field of multifunctional probes, wherein multiple imaging and/or therapeutic modalities are combined in a single species. As high-field MRI becomes more commonplace in the clinical setting, such new contrast agents are needed to provide optimized imaging. This will facilitate the clinician's task in resolving pathologies for more efficient diagnosis and patient treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".