Single‐shot spiral diffusion‐weighted imaging at 7T using expanded encoding with compressed sensing
Bibliographic record
Abstract
Purpose The expanded encoding model incorporates spatially‐ and time‐varying field perturbations for correction during reconstruction. To date, these reconstructions have used the conjugate gradient method with early stopping used as implicit regularization. However, this approach is likely suboptimal for low‐SNR cases like diffusion or high‐resolution MRI. Here, we investigate the extent that ‐wavelet regularization, or equivalently compressed sensing (CS), combined with expanded encoding improves trade‐offs between spatial resolution, readout time and SNR for single‐shot spiral DWI at 7T. The reconstructions were performed using our open‐source graphics processing unit‐enabled reconstruction toolbox, “MatMRI,” that allows inclusion of the different components of the expanded encoding model, with or without CS. Methods In vivo accelerated single‐shot spirals were acquired with five acceleration factors (R) (2×–6×) and three in‐plane spatial resolutions (1.5, 1.3, and 1.1 mm). From the in vivo reconstructions, we estimated diffusion tensors and computed fractional anisotropy maps. Then, simulations were used to quantitatively investigate and validate the impact of CS‐based regularization on image quality when compared to a known ground truth. Results In vivo reconstructions revealed improved image quality with retainment of small features when CS was used. Simulations showed that the joint use of the expanded encoding model and CS improves accuracy of image reconstructions (reduced mean‐squared error) over the range of R investigated. Conclusion The expanded encoding model and CS regularization are complementary tools for single‐shot spiral diffusion MRI, which enables both higher spatial resolutions and higher R.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".