Sci-Thur PM: YIS - 06: Effect of the k-space sampling pattern on the MTF of compressed sensing MRSI
Bibliographic record
Abstract
Compressed Sensing MRSI (CS-MRSI) offers the ability to accelerate MRSI sequences while suffering minimal artifacts compared to conventional fast MRSI techniques. CS-MRSI exploits the inherent sparsity of MRSI images and incoherent artifacts of pseudo-random sub-Nyquist sampling of k-space combined with non-linear reconstruction to produces MRSI images. CS-MRSI can be used as an acceleration tool to decrease the scan time while maintaining acceptable spatial definition or to enable the acquisition of higher resolution scans while minimizing the associated time penalty. In this work we adopt the compressed sensing technique to accelerate a clinically relevant 2-D point resolved spectroscopy sequence. However, the process of weighing the cost and benefit of applying such a fast imaging technique is complicated due to the unique non-linear nature of the reconstruction process and has largely relied on qualitative assessments. Moreover, pseudo-random sub-Nyquist sampling of k-space can have unwanted effects on the modulation transfer function. In this work we set out to quantify the loss in image quality associated with CS-MRSI. We used simulations of a phantom based method to investigate the MTF behaviour of CS-MRSI with regard to different k-space sampling patterns. As expected, the k-space sampling patterns tested were found to have a direct effect on the MTFs. Moreover, limiting the deviation of the resulting k-space sampling pattern from the prescribed probability distribution function had a positive effect on the MTF overall. Not only was low-resolution response improved, but we also noticed an improvement of ∼ 26% in resolution at 0.1 MTF.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".