Trading off SNR and resolution in MR images
Bibliographic record
Abstract
With a fixed time to acquire a magnetic resonance (MR) image, time can be spent to acquire better spatial resolution with decrease in signal-to-noise ratio (SNR) or decreased resolution with increase in SNR. This resolution/SNR tradeoff at fixed time has been investigated by a visual rater study using images of ex vivo mouse brains. Simulated images with a tradeoff between SNR and resolution were produced from high-quality, 3D isotropic mouse brain images to emulate shorter constant acquisition times. The tradeoff images spanned a range of SNRs (63-6) and isotropic resolutions (32-81 microm). Fourteen readers identified the image which best displayed neuroanatomy. Additional experiments tested for (i) intra-observer consistency, (ii) the effect of emulated scan time, and (iii) specifically biased questions pertaining to the perception of neuroanatomy. Optimal anatomical viewing depended primarily on the SNR of the images. Specifically, for fixed imaging time, preference lay in the SNR range of approximately 30-35 with strong consistency and there was minimal effect from overall imaging time.
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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".