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 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 | 0.000 | 0.000 |
| 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 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".