Spatial encoding using the nonlinear field perturbations from magnetic materials
Bibliographic record
Abstract
PURPOSE: A proof-of-concept study was performed to assess the technical feasibility of using magnetic materials to generate spatial encoding fields. THEORY AND METHODS: Spatially varying magnetic fields were generated by the placement of markers with different volume susceptibilities within the imaging volume. No linear gradients were used for spatial encoding during the signal acquisition. A signal-encoding model is described for reconstructing the images encoded with these field perturbations. Simulation and proof-of-concept experimental results are presented. Experiments were performed using field perturbations from a cylindrical marker as an example of the new encoding fields. Based on this experimental setup, annular rings were reconstructed from signals encoded with the new fields. RESULTS: Simulation results were presented for different acquisition parameters. Proof-of-concept was supported by the correspondence of regions in an image reconstructed from experimental data compared to those in a conventional gradient-echo image. Experimental results showed that inclusions of dimensions 1.5 mm in size could be resolved with the experimental setup. CONCLUSION: This study shows the technical feasibility of using magnetic markers to produce encoding fields. Magnetic materials will allow generating spatial encoding fields, which can be tailored to an imaging application with less complexity and at lower cost compared to the use of gradient inserts.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".