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 T 2 measurement of short‐T 2 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 T 2 relaxometry for short‐T 2 tendons, ligaments, and menisci; and for Dixon water‐fat imaging. In vivo T 2 estimate repeatability and SNR efficiency for UTEDESS and Cartesian DESS were compared. The impact of coil combination methods on short‐T 2 measurements was evaluated by means of simulations. UTEDESS T 2 measurements were compared with T 2 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‐T 2 tissues. Simulations and experiments demonstrated that sum‐of‐squares coil combinations overestimated short‐T 2 measurements. UTEDESS measurements of meniscal T 2 were comparable to DESS, MESE, and FSE measurements while the tendon and ligament measurements were less biased than those from Cartesian DESS. Average UTEDESS T 2 repeatability variation was under 10% in all tissues. Conclusion The T 2 measurements of short‐T 2 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".