A cradle-shaped gradient coil to expand the clear-bore width of an animal MRI scanner
Bibliographic record
Abstract
The never ending quest for higher magnetic field strengths in MRI and MRS has led to small and medium bore scanners at 9.4 T and above for both human and animal use; however, these bore diameters restrict the size of object that can be accommodated when using a conventional gradient coil. By replacing a cylindrical gradient-coil insert with a single-sided gradient coil, the scanner's functionality can be extended to include localized imaging of wider samples. As a prototype, a three-axis, cradle-shaped gradient coil was designed, fabricated and implemented in a 9.4 T animal MRI scanner. Since gradient fields are required only to be monotonic over the desired field of view, the cradle gradient coil was designed to produce high gradient efficiencies (up to 2.25 mT m(-1) A(-1) over a 5 cm imaging region) at the expense of gradient linearity. A dedicated three-dimensional algorithm was developed to correct the resultant image distortion. Preliminary images of a grid phantom and a mouse demonstrated the fidelity of the algorithm in correcting image distortion of greater than 200%. Eddy currents were measured along each gradient axis. A large 65.2 (Hz mT(-1) m) B(0) eddy current was produced by the y-axis, suggesting potential limitations of single-sided gradient coils.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".