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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".