High‐resolution MRI encoding using radiofrequency phase gradients
Bibliographic record
Abstract
Although MRI offers highly diagnostic medical imagery, patient access to this modality worldwide is very limited when compared with X-ray or ultrasound. One reason for this is the expense and complexity of the equipment used to generate the switched magnetic fields necessary for MRI encoding. These field gradients are also responsible for intense acoustic noise and have the potential to induce nerve stimulation. We present results with a new MRI encoding principle which operates entirely without the use of conventional B0 field gradients. This new approach--'Transmit Array Spatial Encoding' (TRASE)--uses only the resonant radiofrequency (RF) field to produce Fourier spatial encoding equivalent to conventional MRI. k-space traversal (image encoding) is achieved by spin refocusing with phase gradient transmit fields in spin echo trains. A transmit coil array, driven by just a single transmitter channel, was constructed to produce four phase gradient fields, which allows the encoding of two orthogonal spatial axes. High-resolution two-dimensional-encoded in vivo MR images of hand and wrist were obtained at 0.2 T. TRASE exploits RF field phase gradients, and offers the possibility of very low-cost diagnostics and novel experiments exploiting unique capabilities, such as imaging without disturbance of the main B0 magnetic field. Lower field imaging (<1 T) and micro-imaging are favorable application domains as, in both cases, it is technically easier to achieve the short RF pulses desirable for long echo trains, and also to limit RF power deposition. As TRASE is simply an alternative mechanism (and technology) of moving through k space, there are many close analogies between it and conventional B0 -encoded techniques. TRASE is compatible with both B0 gradient encoding and parallel imaging, and so hybrid sequences containing all three spatial encoding approaches are possible.
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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 | 0.001 |
| 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".