Experimental verification of SNR and parallel imaging improvements using composite arrays
Bibliographic record
Abstract
Composite MRI arrays consist of triplets where two orthogonal upright loops are placed over the same imaging area as a standard surface coil. The optimal height of the upright coils is approximately half the width for the 7 cm coils used in this work. Resistive and magnetic coupling is shown to be negligible within each coil triplet. Experimental evaluation of imaging performance was carried out on a Philips 3 T Achieva scanner using an eight-coil composite array consisting of three surface coils and five upright loops, as well as an array of eight surface coils for comparison. The composite array offers lower overall coupling than the traditional array. The sensitivities of upright coils are complementary to those of the surface coils and therefore provide SNR gains in regions where surface coil sensitivity is low, and additional spatial information for improved parallel imaging performance. Near the surface of the phantom the eight-channel surface coil array provides higher overall SNR than the composite array, but this advantage disappears beyond a depth of approximately one coil diameter, where it is typically more challenging to improve SNR. Furthermore, parallel imaging performance is better with the composite array compared with the surface coil array, especially at high accelerations and in locations deep in the phantom. Composite arrays offer an attractive means of improving imaging performance and channel density without reducing the size, and therefore the loading regime, of surface coil elements. Additional advantages of composite arrays include minimal SNR loss using root-sum-of-squares combination compared with optimal, and the ability to switch from high to low channel density by merely selecting only the surface elements, unlike surface coil arrays, which require additional hardware.
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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 | 0.000 |
| 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".