Imaging and T<sub>2</sub> relaxometry of short‐T<sub>2</sub> connective tissues in the knee using ultrashort echo‐time double‐echo steady‐state (UTEDESS)
Bibliographic record
Abstract
Purpose To develop a radial, double‐echo steady‐state (DESS) sequence with ultra‐short echo‐time (UTE) capabilities for T2 measurement of short‐T2 tissues along with simultaneous rapid, signal‐to‐noise ratio (SNR)‐efficient, and high‐isotropic‐resolution morphological knee imaging. Methods THe 3D radial UTE readouts were incorporated into DESS, termed UTEDESS. Multiple‐echo‐time UTEDESS was used for performing T2 relaxometry for short‐T2 tendons, ligaments, and menisci; and for Dixon water‐fat imaging. In vivo T2 estimate repeatability and SNR efficiency for UTEDESS and Cartesian DESS were compared. The impact of coil combination methods on short‐T2 measurements was evaluated by means of simulations. UTEDESS T2 measurements were compared with T2 measurements from Cartesian DESS, multi‐echo spin‐echo (MESE), and fast spin‐echo (FSE). Results UTEDESS produced isotropic resolution images with high SNR efficiency in all short‐T2 tissues. Simulations and experiments demonstrated that sum‐of‐squares coil combinations overestimated short‐T2 measurements. UTEDESS measurements of meniscal T2 were comparable to DESS, MESE, and FSE measurements while the tendon and ligament measurements were less biased than those from Cartesian DESS. Average UTEDESS T2 repeatability variation was under 10% in all tissues. Conclusion The T2 measurements of short‐T2 tissues and high‐resolution morphological imaging provided by UTEDESS makes it promising for studying the whole knee, both in routine clinical examinations and longitudinal studies. Magn Reson Med 78:2136–2148, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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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) | 0.000 | 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 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".