Perception of dim targets on dark backgrounds in MRI
Bibliographic record
Abstract
Some diagnostic tasks in MRI involve determining the presence of a faint feature (target) relative to a dark background. In MR images produced by taking pixel magnitudes it is well known that the contrast between faint features and dark backgrounds is reduced due to the Rician noise distribution. In an attempt to enhance detection we implemented three different MRI reconstruction algorithms: the normal magnitude, phase-corrected real, and a wavelet thresholding algorithm designed particularly for MRI noise suppression and contrast enhancement. To compare these reconstructions, we had volunteers perform a two-alternative forced choice (2AFC) signal detection task. The stimuli were produced from high-field head MRI images with synthetic thermal noise added to ensure realistic backgrounds. Circular targets were located in regions of the image that were dark, but next to bright anatomy. Images were processed using one of the three reconstruction techniques. In addition we compared a channelized Hotelling observer (CHO) to the human observers in this task. We measured the percentage correct in both the human and model observer experiments. Our results showed better performance with the use of magnitude or phase-corrected real images compared to the use of the wavelet algorithm. In particular, artifacts induced by the wavelet algorithm seem to distract some users and produce significant inter-subject variability. This contradicts predictions based only on SNR. The CHO matched the mean human results quite closely, demonstrating that this model observer may be used to simulate human response in MRI target detection tasks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".