Efficient multichannel coil data compression: A prospective study for distributed detection in wireless high‐density arrays
Bibliographic record
Abstract
Abstract Wireless links have been proposed to connect magnetic resonance (MR) array receiver coils to the rest of the system to eliminate the safety and cross‐talk issues encountered using coaxial cables. Analog transmission methods are unsuited because of limited dynamic range and noise figure; therefore, fully independent individual digital receiver modules must be developed. Limited data rates supported by wireless links restrict the number of coil channels that can be transmitted to well below those of the state‐of‐the‐art high‐density arrays that would benefit the most from wireless technology. Two independent methods of compressing MR data prior to transmission are presented that when combined can readily reduce it to one‐third or less of the original amount with negligible impact on image quality parameters such as artifact power (AP) and signal‐to‐noise ratio (SNR). These conservative results were obtained from arrays of six to 16 channels which is typical of today's clinical systems and show that compression efficiency improves with increasing channel density. Simulated and experimental 2D spiral data acquired from a standard head array at 3 tesla was used to evaluate AP as a function of compression by off‐line processing. Spectral compression reduces the acquired data by up to 45% using dynamic demodulation, filtering and decimation. Dynamic range compression followed by bit‐depth reduction further reduces the data by up to 37.5% without visible image artifacts. Image SNR improves 1–25% at the periphery of the field‐of‐view (FOV) due to spectral compression, while dynamic range compression results in a uniform SNR gain of 4–8% over the whole FOV. © 2011 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 39B: 64–77, 2011
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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 | 0.001 | 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 | 0.001 |
| 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".