MRI using radiofrequency magnetic field phase gradients
Bibliographic record
Abstract
Conventionally, MR images are formed by applying gradients to the main static magnetic field (B0). However, the B0 gradient equipment is expensive, power-hungry, complex, and noisy and can induce eddy currents in nearby conducting structures, including the patient. Here, we describe a new silent, B0 gradient-free MRI principle, Transmit Array Spatial Encoding (TRASE), based on phase gradients of the radio-frequency (RF) field. RF phase gradients offer a new method of k-space traversal. Echo trains using at least two different RF phase gradients allow spin phase to accumulate, causing k-space traversal. Two such RF fields provide one-dimensional imaging and three are sufficient for two-dimensional imaging. Since TRASE is a k-space method, analogues of many conventional pulse sequences are possible. Experimental results demonstrate one-dimensional and two-dimensional RF MRI and slice selection using a single-channel, transmit/receive, 0.2 T, permanent magnet, human MR system. The experimentally demonstrated spatial resolution is much higher than that provided by RF receive coil array sensitivity encoding alone but lower than generally achievable with B0 gradients. Potential applications are those in which one or more of the features of simplified equipment, lower costs, silent MRI, or the different physics of the image formation process are particularly advantageous.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".