Accelerating phase contrast MR angiography by simplified skipped phase encoding and edge deghosting with array coil enhancement
Bibliographic record
Abstract
PURPOSE: The aim of this work is to investigate the feasibility of accelerating phase contrast magnetic resonance angiography (PC-MRA) by the fast imaging method of simplified skipped phase encoding and edge deghosting with array coil enhancement (S-SPEED-ACE). METHODS: The parallel imaging method of skipped phase encoding and edge deghosting with array coil enhancement (SPEED-ACE) is simplified for imaging sparse objects like phase contrast MRA. This approach is termed S-SPEED-ACE in which k-space is sparsely sampled with skipped phase encoding at every Nth step using multiple receiver coils simultaneously. The sampled data are then Fourier transformed into a set of ghosted images, each with N-fold aliasing ghosts. Given signal sparseness of MRA, the ghosted images are modeled with a single-layer structure, in which the most dominant ghost within the potentially overlapped ghosts at each pixel is selected to represent the signal of that pixel. The single-layer model is analogous to that used in maximum-intensity-projection (MIP) that selects only the brightest signal even when there are overlapped vessels. With an algorithm based on a least-square-error solution, a deghosted image is obtained, along with a residual map for quality control. In this way, S-SPEED-ACE partially samples k-space using multiple receiver coils in parallel, and yields a deghosted image with an acceleration factor of N. Without full central k-space sampling and differential filtering, S-SPEED-ACE achieves further scan time reduction with a more straightforward reconstruction. In this work, S-SPEED-ACE is demonstrated to accelerate a computer simulated PC-MRA and a real human 3D PC-MRA, which was acquired using a clinical 1.5 T scanner on a healthy volunteer. RESULTS: Images are reconstructed by S-SPEED-ACE to achieve an undersampling factor of up to 8.3 with four receiver coils. The reconstructed images generally have comparable quality as that of the reference images reconstructed from full k-space data. Maximum-intensity-projection images generated from the reconstructed images also demonstrated to be consistent as those from the reference images. CONCLUSIONS: By taking advantage of signal sparsity naturally existing in the data, SPEED-ACE was simplified and its efficiency was improved. The feasibility of the proposed S-SPEED-ACE is demonstrated in this work with simulated sampling of an actual 3D head PC-MRA scan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".