Magnetic Resonance Imaging at 3.0 Tesla: Challenges and Advantages in Clinical Neurological Imaging
Bibliographic record
Abstract
MR imaging at very high field (3.0 T) is a significant new clinical tool in the modern neuroradiological armamentarium. In this report, we summarize our 40-month experience in performing clinical neuroradiological examinations at 3.0 T and review the relevant technical issues. We report on these issues and, where appropriate, their solutions. Issues examined include: increased SNR, larger chemical shifts, additional problems associated with installation of these scanners, challenges in designing and obtaining appropriate clinical imaging coils, greater acoustic noise, increased power deposition, changes in relaxation rates and susceptibility effects, and issues surrounding the safety and compatibility of implanted devices. Some of the these technical factors are advantageous (eg, increased signal-to-noise ratio), some are detrimental (eg, installation, coil design and development, acoustic noise, power deposition, device compatibility, and safety), and a few have both benefits and disadvantages (eg, changes in relaxation, chemical shift, and susceptibility). Fortunately solutions have been developed or are currently under development, by us and by others, for nearly all of these challenges. A short series of 1.5 T and 3.0 T patient images are also presented to illustrate the potential diagnostic benefits of scanning at higher field strengths. In summary, by paying appropriate attention to the discussed technical issues, high-quality neuro-imaging of patients is possible at 3.0 T.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".