High‐resolution dynamic CE‐MRA of the thorax enabled by iterative TWIST reconstruction
Bibliographic record
Abstract
PURPOSE: To evaluate the clinical benefit of using a new iterative reconstruction technique fully integrated on a standard clinical scanner and reconstruction system using a TWIST acquisition for high-resolution dynamic three-dimensional contrast-enhanced MR angiography (CE-MRA). METHODS: Low-dose, high-resolution TWIST datasets of 11 patients were reconstructed using both standard GRAPPA-based reconstruction for reference and iterative reconstruction, which reduces the temporal footprint of reconstructed images. Image quality of both techniques was assessed by two experienced readers, as well as quantitatively evaluated using a time-signal curve analysis. RESULTS: Image quality scores consistently and significantly improved by using iterative reconstruction compared with the standard approach. Most notably, the delineation of small to mid-size vasculature improved from a mean Likert score between "nondiagnostic" and "poor" for standard to between "good" and "excellent" for iterative reconstruction. The full width at half maximum of the contrast agent bolus computed from the time-signal curve was also reduced by iterative reconstruction, allowing for more precise bolus timing. CONCLUSION: Iterative reconstruction can substantially improve high-resolution dynamic CE-MRA image quality, most notably in small to mid-size vasculature. Dynamic CE-MRA with iterative reconstruction could become an alternative to conventional static 3D CE-MRA, thus simplifying the clinical workflow. Magn Reson Med 77:833-840, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".