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 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".