Intrasession and Intersession Repeatability of Diffusion Tensor Imaging in Healthy Human Liver
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the effect of signal to noise ratio (SNR) and number of gradient directions (NGD) on intra- and intersession repeatability of liver diffusion tensor imaging (DTI) metrics. METHODS: At each of 3 liver DTI scan sessions, liver diffusion was assessed in 5 healthy volunteers using a 6-direction DTI scan performed 9 separate times (ie, number of signal averages [NSA]). In addition, 4 combinations of NSA and NGD were acquired (NSA/NGD = 1/30, 3/10, 3/12, and 5/6) to determine the combined effect to DTI metrics, which was based on intersubject variability and intrasession (Vintra) and intersession (Vinter) repeatability. RESULTS: Intersubject variability was less than 20%, whereas Vintra and Vinter repeatability were less than 5% and less than 10%, respectfully. Vinter was not affected by the NGD used. Decreases in Vinter(FA), Vinter(λ1), Vinter(RD), and Vinter(MD) were observed with increasing NSA, and hence SNR. CONCLUSION: Increased SNR may improve intrasession and intersession repeatability of liver DTI metrics. Scan repeatability was not influenced by NGD.
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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 | 0.004 | 0.012 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".