Efficient compressed sensing reconstruction frameworks for accelerated cardiac magnetic resonance imaging
Bibliographic record
Abstract
Dynamic magnetic resonance imaging requires rapid data acquisition to provide an appropriate combination of spatial and temporal resolution, and volumetric coverage for clinical studies. In the most challenging clinical situations, conventional dynamic MR scanners are often incapable of simultaneously providing images with sufficient temporal resolution and high spatial resolution. In practice, clinicians are often forced to compromise between these parameters, often resulting in sub-optimal performance. Cardiac MRI is the most challenging and inspiring dynamic MRI application. In cardiac MRI, the main challenge is the sensitivity of reconstruction methods to large inter frame motion. The reconstructions often suffer from temporal blurring and motion related artifacts at high acceleration factors. In this dissertation, three novel approaches are proposed specifically designed to minimize the sensitivity of the reconstructions to inter frame motion. First, a compressed sensing (CS) based image reconstruction method in conjunction with spiral sampling is developed for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. In the second algorithm, the problem of motion artifacts including respiratory motion and cardiac motion in compressed sensing reconstructions is addressed. A motion estimation/motion compensation algorithm based on a modified search that aids block matching and results in improved residual reconstruction is incorporated into the CS reconstruction for dynamic MRI. In the third algorithm, a novel formulation for the joint estimation of the deformation and the dynamic images in cardiac cine MR imaging is introduced. The motion estimation algorithm estimates the deformation by registering the dynamic data to a reference dataset that is free of respiratory motion, which is derived from the measurements themselves. A variable splitting framework is used to minimize the objective function, and thus derive the deformation and the dynamic images. The validation of the proposed algorithms is illustrated using a numerical phantom and in-vivo cine MRI data to show the feasibility in precisely recovering cardiac MRI data from extensively under-sampled data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".