Sampling pattern discrepancy in the application of compressed sensing hyperpolarized xenon‐129 lung MRI
Bibliographic record
Abstract
Abstract Although hyperpolarized (HP) 129 Xe ventilation MRI can be carried out within a breath hold, it is still challenging for many sick patients. Compressed sensing (CS) is a viable alternative to accelerate this approach. However, undersampled images with identical sampling ratios differ from one another. Twenty subjects ( n = 10 healthy and n = 10 patients with asthma) were scanned using a GE MR750 3 T scanner, acquiring fully sampled 2D multi‐slice HP 129 Xe lung ventilation images (10 s breath hold, 128 × 80 (FE × PE—frequency encoding × phase encoding) and 16 slices). Using fully sampled data, 500 variable‐density Cartesian random undersampling patterns were generated, each at eight different sampling ratios from 10% to 80%. The parallel imaging and compressed sensing (PICS) command from BART was employed to reconstruct undersampled data. The signal to noise ratio (SNR), structural similarity index measurement (SSIM) and sidelobe to peak ratio of each were subsequently compared. There was a high degree of variation in both SNR and SSIM results from each of the 500 masks of each sampling rate. As the undersampling increases, there is more variation in the quantifying metrics, for both healthy and asthmatic individuals. Our study shows that random undersampling poses a significant challenge when applied at sampling ratios less than 60%, despite fulfilling CS's incoherency criteria. Such low sampling ratios will result in a large variety of undersampling patterns. Therefore, skipped segments of k ‐space cannot be allowed to happen randomly at low sampling rates. By optimizing the sampling pattern, CS will reach its full potential and be able to be applied to a highly undersampled 129 Xe lung dataset.
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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 | 0.001 |
| 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".