Accelerated multicontrast volumetric imaging with isotropic resolution for improved peri‐infarct characterization using parallel imaging, low‐rank and spatially varying edge‐preserving sparse modeling
Bibliographic record
Abstract
PURPOSE: To achieve consistent effectiveness in reconstruction of fine image features for cases of varying contrast-to-noise ratio (CNR) to facilitate translating accelerated multicontrast volumetric imaging with isotropic resolution toward clinical utility in peri-infarct characterization. THEORY AND METHODS: A low-rank and spatially varying edge-preserving constrained compressed sensing parallel imaging reconstruction method (CP-LASER) is developed to effectively preserve contrast of small-scale structures for highly accelerated multicontrast volumetric imaging in CNR-limited scenarios. CP-LASER synergistically integrates parallel imaging, low-rank and spatially varying edge-preserving sparse modeling to achieve high signal-to-noise-ratio efficiency by leveraging prior knowledge about signal properties including coil sensitivity weighting, spatiotemporally correlated signal relaxation, and spatially varying sparsity. RESULTS: In the preclinical study using highly accelerated multicontrast volumetric imaging with an isotropic 1.5-mm resolution, CP-LASER demonstrated robust multicontrast reconstruction of peri-infarct characteristics with excellent correspondence with histopathology. CP-LASER provides better delineation of the peri-infarct border zone with improved sharpness than alternative methods in a clinical demonstration on 1.5T with an isotropic 2.2-mm resolution achieved in a single breath-hold. CONCLUSION: Accelerated multicontrast volumetric imaging with isotropic resolution using CP-LASER has demonstrated the potential to improve peri-infarct characterization in a clinical setting. Magn Reson Med 79:3018-3031, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".