Rician compressed sensing for fast and stable signal reconstruction in diffusion MRI
Bibliographic record
Abstract
The advent of the theory of compressed sensing (CS) has revolutionized multiple areas of applied sciences, a particularly important instance of which is medical imaging. In particular, the theory provides a solution to the problem of long acquisition times, which is intrinsic in diffusion MRI (dMRI). As a specific instance of dMRI, this work focuses on high angular resolution diffusion imaging (HARDI), which is known to excel in delineating multiple diffusion flows through a given voxel within the brain. Specifically, to reduce the acquisition time, CS allows undersampling the HARDI data by employing fewer diffusion-encoding gradients than it is required by the classical sampling theory. Subsequently, the undersampled data is used to recover the original signals by means of non-linear decoding. In earlier reconstruction methods, such decoding has been carried out under a Gaussian model for measurement noises, instead of the Rician model which is known to prevail in MRI. Accordingly, the main contribution of the present work is twofold. First, we introduce a way to substantially improve the stability of the CS-based reconstruction of HARDI signals under the assumption of Gaussian noises. Second, we extend this approach to the case of Rician noise statistics. In addition to providing formal developments of the reconstruction algorithm based on Rician statistics, we also detail a computationally efficient numerical scheme which can be used to implement the above reconstruction. Finally, the methods based on the Gaussian and the Rician noise models are compared using both simulated and in-vivo MRI data under various measurement conditions.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".