Fat-suppressed t2* sequences for routine 3.0-tesla lumbar spine magnetic resonance imaging: a preliminary report
Bibliographic record
Abstract
BACKGROUND: Clear depiction of the ligamentum flavum on routine lumbar magnetic resonance imaging (MRI) is essential in accurately describing the extent of degenerative disease. In routine, noncontrast evaluations, focal fatty deposition or hemangiomas can be difficult to distinguish from malignant foci on fast spin-echo (FSE) T2-weighted images. PURPOSE: To describe the use of T2* fast field echo (T2FFE) in combination with spectral presaturation inversion recovery (SPIR) fat suppression for noncontrast, routine lumbar spine outpatient MR imaging at 3.0 Tesla (3T). MATERIAL AND METHODS: An axial gradient echo (GE) T2FFE sequence was combined with SPIR fat suppression (T2FFE-SPIR), via a 3T Philips Intera (Philips Medical Systems, Best, The Netherlands) scanner, and added to the routine, noncontrast lumbar MRI examinations, which included sagittal FSE T1-weighted (T1WI), T2-weighted (T2WI), short-tau inversion recovery (STIR), and axial FSE T2WI. The sequence was performed in over 500 patients over a 1-year period, without intravenous contrast, and with slice thickness and planes of section identical to the axial FSE T1WI and T2WI images. The sequence typically lasted about 4.5-6 min. RESULTS: The use of T2FFE-SPIR enabled visualization of the ligamentum flavum in degenerative disease, and the exclusion of focal fatty lesions on FSE T2WI. Other benefits included: the identification of malignant foci, the uncommon detection of hemorrhage, and the elimination of spurious flow voids. Several brief examples are provided to demonstrate the utility of this technique. CONCLUSION: The addition of T2FFE-SPIR to routine, noncontrast protocols in outpatients could provide further confidence in the visualization of the ligamentum flavum in degenerative disease, and can exclude malignancy in T2-bright areas of focal fatty marrow. Larger studies would be helpful to evaluate the accuracy of this technique versus FSE techniques in depicting degenerative, malignant, or inflammatory disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".