Reconstruction of HARDI using compressed sensing and its application to contrast HARDI
Bibliographic record
Abstract
High angular resolution diffusion imaging (HARDI) is known to excel in delineating multiple diffusion flows through a given location within the white matter of the brain. Unfortunately, many current methods of implementation of HARDI require collecting a relatively large number of diffusion-encoded images, which is in turn translated in prohibitively long acquisition times. As a possible solution to this problem, one can undersample HARDI data by using fewer diffusion-encoding gradients than it is prescribed by the classical sampling theory, while exploiting the tools of compressed sensing (CS). Accordingly, the goal of the present paper is twofold. First, the paper presents a novel CS-based framework for the reconstruction of HARDI data using a reduced set of diffusion-encoding gradients. As opposed to similar studies reported in the literature, the proposed method has been optimized for the Rician statistics of measurement noises, which are known to be prevalent in HARDI, and in fact, in MRI in general. Second, we introduce the concept of rotational invariant Fourier signatures (RIFS), and show how they can be used to generate a composite HARDI contrast, which we refer to as colour-HARDI (cHARDI). Finally, via a series of experiments with both simulated and in vivo MRI data, we demonstrate that the quality and informativeness of the proposed contrast deteriorates little, when used in conjunction with the proposed CS-based reconstruction framework. Thus the present work proposes a way to improve the time efficiency of HARDI, and shows its application to the computation of a new HARDI-based contrast which has a potential to improve the clinical value of this important imaging modality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".