SU‐GG‐I‐144: Accelerating Non‐Contrast‐Enhanced MRA with Inflow Inversion Recovery by Using Skipped Phase Encoding and Edge Deghosting (SPEED)
Bibliographic record
Abstract
Purpose: Non‐contrast‐enhanced MR angigraphy (MRA) has always been a valuable clinical tool to study vasculatural diseases. The recently arising concerns of constrast agents in contrast‐enhanced MRA encouraged more use of non‐contrast‐enhanced MRA. The aim of this work is to accelerate non‐contrast‐enhanced MRA with inflow inversion recovery (IFIR) with a fast imaging method, Skipped Phase Encoding and Edge Deghosting (SPEED) Method and Materials: IFIR imaging uses a preparatory inversion pulse to reduce signals from static tissue, while leaving inflow arterial blood unaffected, resulting in sparse arterial vasculature on modest tissue background. By taking advantage of vascular sparsity, SPEED can be simplified with a single‐layer‐model to achieve higher efficiency in both scan time reduction and image reconstruction. SPEED can also make use of information available in multiple coils for further acceleration. The techniques are demonstrated with a 3D renal non‐contrast‐enhanced IFIR MRA study performed on a 3.0 T scanner (GE Healthcare,WI) with a Respiratory Triggered IR‐Prepared Fiesta (SSFP) sequence (matrix 256×256, FOV 36cm, TI 200ms, TR 4.2ms, TE 2.1ms, slice thickness 2mm, space between slices 1mm, flip angle 70°, single acquisition, slices 116). Results: Images are reconstructed by SPEED based on a single‐layer model to achieve an undersampling factor of up to 4.2 using one skipped phase encoding direction. By making use of information available in multiple coils, SPEED can 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. Conclusion: This study demonstrated the successful application of SPEED to accelerate non‐contrast‐enhanced IFIR MRA based on a single‐layer‐model. Although the single‐layer‐model SPEED is demonstrated with only renal IFIR MRA, it may have great potential in other MRI applications, particularly in cases where sparse signals are distributed on a modest tissue background.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".