Determination of detection sensitivity for cerebral microbleeds using susceptibility‐weighted imaging
Bibliographic record
Abstract
Cerebral microbleeds (CMBs) are small brain hemorrhages caused by the break down or structural abnormalities of small vessels of the brain. Owing to the paramagnetic properties of blood degradation products, CMBs can be detected in vivo using susceptibility-weighted imaging (SWI). SWI can be used not only to detect iron changes and CMBs, but also to differentiate them from calcifications, both of which may be important MR-based biomarkers for neurodegenerative diseases. Moreover, SWI can be used to quantify the iron in CMBs. SWI and gradient echo (GE) imaging are the two most common methods for the detection of iron deposition and CMBs. This study provides a comprehensive analysis of the number of voxels detected in the presence of a CMB on GE magnitude, phase and SWI composite images as a function of resolution, signal-to-noise ratio (SNR), TE, field strength and susceptibility using in silico experiments. Susceptibility maps were used to quantify the bias in the effective susceptibility value and to determine the optimal TE for CMB quantification. We observed a non-linear trend with susceptibility for CMB detection from the magnitude images, but a linear trend with susceptibility for CMB detection from the phase and SWI composite images. The optimal TE values for CMB quantification were found to be 3 ms at 7 T, 7 ms at 3 T and 14 ms at 1.5 T for a CMB of one voxel in diameter with an SNR of 20: 1. The simulations of signal loss and detectability were used to generate theoretical formulae for predictions. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".