Rapid 1 mm isotropic diffusion tensor imaging with denoising and improved parameter estimation for detecting focal hippocampal lesions in temporal lobe epilepsy
Bibliographic record
Abstract
While high resolution diffusion tensor imaging (DTI) at 1 mm isotropic can detect focal lesions of the hippocampus in temporal lobe epilepsy (TLE), faster acquisition times would facilitate potential clinical implementation. The purpose here is to assess different published denoising algorithms to overcome the low signal-to-noise ratio and accelerate 1 mm isotropic DTI of the human hippocampus at 3 T while maintaining diffusivity metric accuracy and image quality for focal lesion detection in TLE. The previously published 5.5 min protocol of 110 diffusion images per slice (10 directions × 10 averages and 10 b = 0 s/mm 2 ) was assessed for subsets of 1–10 averages (same 10 directions) that were denoised using four algorithms that have been applied to other diffusion MRI datasets. In healthy controls, the variance-stabilizing transformation and optimal singular-value manipulation (VST) and Non-Local Spatial and Angular Matching (NLSAM) denoising greatly improved image quality while minimizing voxels with spurious extremes of fractional anisotropy (FA) or mean diffusivity (MD) down to 4 averages (i.e. 40 diffusion images and 4 b = 0 s/mm 2 ) in healthy controls. The identification of focal lesions indicated by elevated MD and alterations of internal micro-architecture with only 4 averages were comparable to the full data set of 10 averages. Therefore, denoising of 1 mm isotropic DTI of the hippocampus enables a clinically feasible scan time of 2.2 min at 3 T that can be used for the detection of focal hippocampal lesions in TLE, as well as other neurological disorders such as multiple sclerosis, dementia and Alzheimer's disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".