In vivo free-breathing DTI & 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
In vivo cardiac diffusion using either the Intra-Voxel Incoherent Motion (IVIM) model or the Diffusion Tensor Imaging (DTI) model shows promise to provide new insights into cardiac disease processes. However, due to the combined challenge of respiratory and heart motion [ 1 , 2 ], currently proposed acquisition methods may not be applicable in patients due to long acquistion time. In this study, we propose to use the motion information provided by a navigator to prospectively update in real time the position of the diffusion weighted slices, offering an efficient free-breathing strategy for rapid and improved cardiac diffusion acquisition using a single-shot spin-Echo EPI sequence. The proposed technique was performed on 10 volunteers on 1.5T scanner to quantify the reproducibility of the diffusion parameters ( FA , MD , f , D and D* ). Double oblique short-axis DW images were acquired with a 128 × 80 pixel matrix and rectangular FoV of 350 × 220mm2. Short axis DW-slices (6-mm-thick) were obtained with an in-plane resolution of 2.7 × 2.7mm2 interpolated in-plane to 1.35 x 1.35mm2; the bandwidth was 2442Hz/pixel, and GRAPPA factor 2 was used. Multiple TDs shifted every 10ms were acquired for PCAtMIP reconstruction. DTI acquisitions parameters were: interleaved 10 contiguous slices; TE=45ms; TR= number of slices x heart rate (e.g., 10s for a heart rate equal to 60 bpm); and 12 diffusion directions with a b-value of 350s/mm2 and 10 TDs for a total acquisition time of 22min. The IVIM acquisition parameters were 5 interleaved slices with a gap of 100%; TE=42ms; TR= 5s; 6 diffusion directions with b-values 0, 15, 30, 50, 75, 100, 200, 300, 400s/mm2 and 5 TDs were acquired for total time of 20min. Inter-measurement reproducibility among repeated measures was assessed by the Lin concordance coefficient (LinCC) and the complementary Bland and Altman method with LinCC coefficient ρ c , r (a measure of precision) and C b (a measure of accuracy). With a 100% breathing cycle scanning efficiency, the slice-following strategy is a powerful head-foot respiratory motion management solution for SE-EPI cardiac diffusion Vascular fraction f and the diffusion coefficients D and D* were determined to be 35.6±11.9x10 mm2/s for D* , 1.23±0.19x10 mm2/s for D and 0.115±0.017 for f . From the DTI protocol, the Mean Diffusivity ( MD ) was 1.57±0.13x10 mm2/s and the Fractional Anisotropy ( FA ) 0.35±0.04. MD ( ρ c =0.69, r = 0.70, C b =0.99), D ( ρ c =0.92, r =0.99, C b =0.93) and f ( ρ c =0.69, r =0.75, C b =0.91) was highly reproducible exept for D* ( ρ c =-0.214, r =-0.229, C b =0.93) and FA ( ρ c =0.075, r =0.12, C b =0.56) but which remained accurate. The slice-followed SE-EPI cardiac diffusion sequence is a promising solution for clinical implementation, allowing unprecedent acquisition speed that could be tailored to specific needs.
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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 | 0.002 |
| 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".