Quantitative Comparison of Susceptibility-weighted Imaging Methods for Detection of Differences in Deep Grey Matter in Multiple Sclerosis
Bibliographic record
Abstract
Problem: Investigating signal changes in deep gray matter (DGM) structures isa novel approach to understanding the role of iron in the progression of multiplesclerosis (MS)1,2,3. T2* weighted angiography (SWAN) is a new susceptibility weighted3D imaging method offering less noise than the traditional T2* weighted gradientrecalled echo (T2* GRE) method4. We assess the difference between SWAN and T2*GRE and their ability to detect signal changes in DGM structures and image quality.Method: Five healthy controls and 13 MS patients were selected from an ongoingstudy. MRI was performed on a 3T MR scanner using the standard protocols forthe following sequences: a 3D multi-echo SWAN, a 2D single-echo T2* GRE. Signalmeasurements were taken in DGM structures with the FMRIB software library andcontrast-noise (CNR) and signal-noise (SNR) ratios were calculated. Statistical analysiswas conducted with SPSS v19 using two-way ANOVA and post-hoc for weighted andun-weighted means.Results: An interaction effect was observed between region and module. Controlsconsistently had a higher signal than MS patients in T2* GRE, however this onlyoccurred in two out of the four regions in SWAN. SWAN demonstrated a higher SNRthan T2* GRE offering a cleaner image. T2* GRE and SWAN offered equal contrast ontwo structures. T2* GRE was considerably superior to SWAN in the remaining two.Conclusions: SWAN allows for a cleaner image which may provide a qualitativeadvantage for trained radiologists but is not significantly superior to T2* GRE quantitatively.T2* GRE offered superior contrast to SWAN, providing better separation oftissue. Larger differences in signal intensity between control and MS patients observedin T2* GRE make MS patients more distinguishable. Consistent pattern (control higherthan MS) in T2* GRE makes it more reliable than SWAN for detecting changes in DGMstructures.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".