Accuracy of the Compressed Sensing Accelerated 3D-FLAIR Sequence for the Detection of MS Plaques at 3T
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The use of 3D FLAIR improves the detection of brain lesions in MS patients, but requires long acquisition times. Compressed sensing reduces acquisition time by using the sparsity of MR images to randomly undersample the k-space. Our aim was to compare the image quality and diagnostic performance of 3D-FLAIR with and without compressed sensing for the detection of multiple sclerosis lesions at 3T. MATERIALS AND METHODS: Twenty-three patients with relapsing-remitting MS underwent both conventional 3D-FLAIR and compressed sensing 3D-FLAIR on a 3T scanner (reduction in scan time 1 minute 25 seconds, 27%; compressed sensing factor of 1.3). Two blinded readers independently evaluated both conventional and compressed sensing FLAIR for image quality (SNR and contrast-to-noise ratio) and the number of MS lesions visible in the periventricular, intra-juxtacortical, infratentorial, and optic nerve regions. The volume of white matter lesions was measured with automatic postprocessing segmentation software for each FLAIR sequence. RESULTS: Image quality and the number of MS lesions detected by the readers were similar between the 2 FLAIR acquisitions (P = .74 and P = .094, respectively). Almost perfect agreement was found between both FLAIR acquisitions for total MS lesion count (Lin concordance correlation coefficient = 0.99). Agreement between conventional and compressed sensing FLAIR was almost perfect for periventricular and infratentorial lesions and substantial for intrajuxtacortical and optic nerve lesions. Postprocessing with the segmentation software did not reveal a significant difference between conventional and compressed sensing FLAIR in total MS lesion volume (P = .63) or the number of MS lesions (P = .15). CONCLUSIONS: With a compressed sensing factor of 1.3, 3D-FLAIR is 27% faster and preserves diagnostic performance for the detection of MS plaques at 3T.
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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 | 0.004 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".