FLAIR<sup>2</sup>: A Combination of FLAIR and T2 for Improved MS Lesion Detection
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: FLAIR and double inversion recovery are important MR imaging scans for MS. The suppression of signal from CSF in FLAIR and the additional suppression of WM signal in double inversion recovery improve contrast between lesions, WM and GM, albeit at a reduced SNR. However, whether the acquisition of double inversion recovery is necessary is still debated. Here, we present an approach that allows obtaining CSF-suppressed images with improved contrast between lesions, WM and GM without strongly penalizing SNR. MATERIALS AND METHODS: 3D T2-weighted and 3D-FLAIR data acquired from September 2014 to April 2015 in healthy volunteers (23.4 ± 2.4 years of age; female/male ratio, 3:2) and patients (44.1 ± 14.0 years of age; female/male ratio, 4:5) with MS were coregistered and multiplied (FLAIR(2)). SNR and contrast-to-noise measurements were performed for focal lesions and GM and WM. Furthermore, data from 24 subjects with relapsing-remitting and progressive MS were analyzed retrospectively (52.7 ± 8.1 years of age; female/male ratio, 14:10). RESULTS: The GM-WM contrast-to-noise ratio was by 133% higher in FLAIR(2) than in FLAIR and improved between lesions and WM by 31%, 93%, and 158% compared with T2, DIR, and FLAIR, respectively. Cortical and juxtacortical lesions were more conspicuous in FLAIR(2). Furthermore, the 3D nature of FLAIR(2) allowed reliable visualization of callosal and infratentorial lesions. CONCLUSIONS: We present a simple approach for obtaining CSF suppression with an improved contrast-to-noise ratio compared with conventional FLAIR and double inversion recovery without the acquisition of additional data. FLAIR(2) can be computed retrospectively if T2 and FLAIR scans are available.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".