3D Cartesian ultrashort double half‐echo imaging of the lung parenchyma for water density imaging
Bibliographic record
Abstract
PURPOSE: Develop and illustrate a 3D double half-echo Cartesian UTE method for spin-density weighted imaging of the lung parenchyma and calculation of lung water density (LWD). METHODS: A 3D gradient-echo pulse sequence was modified to acquire half-echoes, to enable UTEs (TE/TR = 145 μs/1.2 ms), with an acquired resolution of 3.125 mm by 3.125 mm by 5 mm. Breath-hold (12.9 s) and free-breathing (94 s) acquisitions, using a center of k-space navigator, were compared to a previously validated yarnball UTE sequence (1.5T/2.89T). Apparent SNR in the lung parenchyma was measured for all in-vivo acquisitions. Illustrative clinical cases included heart failure and sarcoidosis with a comparison to CT images. RESULTS: Lung image quality and calculated LWD was similar for all compared methods at 1.5T and 2.89T for breath-hold and free-breathing acquisitions (N = 10, p > 0.05), with no visible artifacts. The mean lung parenchyma SNR values were 18.4 ± 1.4, 21.8 ± 1.7 and 15.1 ± 1.0 for 1.5T free-breathing, 2.89T free-breathing and 2.89T breath-hold, respectively, and 20.7 ± 1.1 for yarnball acquisitions (2.89T), with corresponding average LWD values of 26.7 ± 2.9%, 27.1 ± 2.5%, 27.1 ± 2.1% and 27.7 ± 2.7%. MRI LWD images and CT scans yielded similar image contrast and normalized signal intensity units. All Cartesian UTE images were reconstructed on the scanner without the requirement for gridding. CONCLUSIONS: A double half-echo Cartesian UTE pulse sequence provides water-density weighted images of the lung parenchyma in a breath-hold or short free-breathing acquisition with sufficient signal to noise for quantification of LWD at 1.5T or 2.89T.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".