In vivo free‐breathing DTI and IVIM of the whole human heart using a real‐time slice‐followed SE‐EPI navigator‐based sequence: A reproducibility study in healthy volunteers
Bibliographic record
Abstract
PURPOSE: In this study, we proposed an efficient free-breathing strategy for rapid and improved cardiac diffusion-weighted imaging (DWI) acquisition using a single-shot spin-echo echo planar imaging (SE-EPI) sequence. METHODS: A real-time slice-following technique during free-breathing was combined with a sliding acquisition-window strategy prior Principal Component Analysis temporal Maximum Intensity Projection (PCAtMIP) postprocessing of in-plane co-registered diffusion-weighted images. This methodology was applied to 10 volunteers to quantify the performance of the motion correction technique and the reproducibility of diffusion parameters. RESULTS: The slice-following technique offers a powerful head-foot respiratory motion management solution for SE-EPI cDWI with the advantage of a 100% duty cycle scanning efficiency. The level of co-registration was further improved using nonrigid motion corrections and was evaluated with a co-registration index. Vascular fraction f and the diffusion coefficients D and D* were determined to be 0.122 ± 0.013, 1.41 ± 0.09 × 10(-3) mm(2) /s and 43.6 ± 9.2 × 10(-3) mm(2) /s, respectively. From the multidirectional dataset, the measured mean diffusivity was 1.72 ± 0.09 × 10(-3) mm(2) /s and the fractional anisotropy was 0.36 ± 0.02. CONCLUSION: The slice-following DWI SE-EPI sequence is a promising solution for clinical implementation, offering a robust improved workflow for further evaluation of DWI in cardiology. Magn Reson Med 76:70-82, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".