RF coil loading measurements between 1 and 50 MHz to guide field‐cycled MRI system design
Bibliographic record
Abstract
Abstract Conventional magnetic resonance imaging (MRI) uses a single superconducting magnet both to polarize the nuclear magnetic moments in a sample and to provide the magnetic field environment under which an image is acquired. In field‐cycled MRI (FCMRI), these two tasks are independently accomplished by two actively controlled resistive magnets. As a result, the expressions for signal‐to‐noise ratio for an FCMRI system, as with other systems making use of prepolarization techniques (such as hyperpolarized noble gas MRI), are fundamentally different than those of a conventional system. Once losses in the receiver coil are dominated by contributions from the sample, there is little benefit to increasing the receive frequency. The added freedom to independently vary the two field strengths can lead to substantial relative improvements in SNR over a range of Larmor frequencies, while precluding the difficulties associated with the operation of high‐field systems. Radiofrequency coil loading measurements were recorded over a frequency range of 1–50 MHz. The optimal frequency range for an FCMRI scanner intended for small animal imaging was determined to be between 5 and 48 MHz, depending on the given receiver coil and sample conductivity. The implication of these results on the design of systems employing prepolarization techniques is provided, with an emphasis being placed on the design and operation of FCMRI systems. © 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 33B: 177–191, 2008
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".