Combining High-Resolution FLAIR and T2 to Improve Multiple Sclerosis Lesion Conspicuity (P4.155)
Bibliographic record
Abstract
Objective: To develop a high resolution Magnetic Resonance Imaging (MRI) protocol for Multiple Sclerosis (MS) with better contrast-to-noise ratio (CNR) for MS lesion detection. Background: The conventional MRI protocol for MS includes 2D/3D-FLAIR (fluid attenuated inversion recovery), T2- and/or proton density images. FLAIR and double inversion recovery (DIR) suppress signal from cerebrospinal fluid and/or white matter (WM) to improve WM and cortical lesion visualization, however suffer signal loss. Combining high-resolution 3D-images can overcome weaknesses of individual techniques and improve conspicuity of WM hyperintensities. Methods: Sagittal 3D-T2 and 3D-FLAIR images were acquired at 3T from 5 healthy controls and 7 patients with relapsing-remitting and progressive MS with varying spatial resolutions. FLAIR² images were then computed by registering and multiplying the T2 and FLAIR images together. Signal-to-noise ratio (SNR) and CNR were estimated in these subjects by assessing noise between subsequent acquisitions of the same scan. Additionally, FLAIR² was computed retrospectively in 24 MS patients. Results: FLAIR² provided the best SNR and lesion conspicuity when T2 and FLAIR were acquired at 0.6×0.75×1.35mm³ and reconstructed to 0.3mm³ voxels. Data acquisition of these high-resolution images takes 14 minutes, which is comparable to the acquisition of DIR images at 1mm³. FLAIR² achieves an improvement in contrast between MS lesions and their surrounding WM by 93[percnt], and 158[percnt] compared with DIR and FLAIR and has three times higher SNR than DIR. The 3D-nature of FLAIR² allows for improved visualization of callosal and infratentorial MS lesions and excellent image registration for serial studies. Conclusions: FLAIR² achieves CSF suppression with improved CNR compared to conventional scans, suggesting it may replace DIR. Lesions in the entire brain are captured, including infratentorial regions and most of the cervical cord. The improved detection of WM hyperintensities will also benefit research and diagnosis in Alzheimer9s disease, neurotrauma, stroke and other applications. Disclosure: Dr. Wiggermann has nothing to disclose. Dr. Hernandez-Torres has nothing to disclose. Dr. Traboulsee has received personal compensation for activities with Genzyme and Roche. Dr. Traboulsee has received research support from Genzyme, Roche, Chugai. Dr. Li has received personal compensation for activities with Roche, Nuron Biotech, and Opexa Therapeutics as a consultant. Dr. Rauscher has received personal compensation for activities with Roche Diagnostics Corporation as a scientific advisory board member.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".