Exploiting rank deficiency for MR image reconstruction from multiple partial K-space scans
Bibliographic record
Abstract
In Magnetic Resonance Imaging (MRI) the acquired K-space data is corrupted by white Gaussian noise or by motion artifacts. In order to reduce the effects of these factors, it is a common practice to take multiple scans of the K-space. Noise/motion artifacts are reduced by averaging the K-space scans. For fully scanned K-space data, the image is obtained from this averaged K-space by applying the inverse Fourier transform. However, sampling the full K-space is time consuming. To reduce the scan-time smart reconstruction algorithms are employed obtain the MR image partial K-space scans. Generally Compressed Sensing (CS) based techniques are used to this end. In this work, we will show how the image can be reconstructed from multiple partial K-space scans by nuclear norm minimization. The reconstruction accuracy from our proposed method is the same as CS based techniques but is about ten times faster.
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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".