Sparse Reconstruction of Breast MRI Using Homotopic <formula formulatype="inline"><tex Notation="TeX">$L_0$</tex></formula> Minimization in a Regional Sparsified Domain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of MRI for early breast examination and screening of asymptomatic women has become increasing popular, given its ability to provide detailed tissue characteristics that cannot be obtained using other imaging modalities such as mammography and ultrasound. Recent application-oriented developments in compressed sensing theory have shown that certain types of magnetic resonance images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled k-space data below Nyquist sampling rates using homotopic L0 minimization schemes, which holds great potential for significantly reducing acquisition time. An important consideration in the use of such homotopic L0 minimization schemes is the choice of sparsifying transform. In this paper, a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI. By taking local regional characteristics into account, the regional differential sparsifying transform can better account for signal variations and fine details that are characteristic of breast MRI than the popular finite differential transform, while still maintaining strong structure fidelity. Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it