Diffusion Weighted Image Co-registration: Investigation of Best Practices
Bibliographic record
Abstract
1. Abstract The registration or alignment of diffusion weighted images (DWI) with other imaging modalities is a critical step in neuroimaging analysis. Within-subject T1-DWI co-registration is particularly instrumental. DWI-derived scalar images are commonly used as intermediates for T1-DWI co-registration, and the resulting registration transforms are applied to all other scalar images for analysis. The ideal registration intermediate should register well to T1 and other multimodal images and be practically easy to obtain. It is however, currently unclear which DWI-derived scalar image serves as the best intermediate. We aim to determine the best, practical, intermediate for image co-registration. T1 and DWI images were acquired from 20 healthy subjects. DWIs were acquired with 60 directions. Six DWI-derived scalar images were compared including: 1) fractional anisotropy (FA); 2) generalized FA (GFA); 3) B0 images; 4) mean DWIs with the B0 image (MDWI); 5) anisotropic power (AP) images. AP showed the smallest variability in registration improvements across all the tested DWI derived scalar images, and show the highest average percent changes with CC registration cost function (CC=1.2%, MI=15%). In contrast, the FA and GFA transforms resulted in significantly poorer registration across DWI types. The AP image was the DWI-derived scalar image that provided the most consistent registration to all other images. Practically, it is generated easily and so could be implemented in basic and clinical research pipelines currently using other intermediates. Given these findings, it is recommended that AP images be used for T1–DWI co-registration, and that FA and GFA images in particular be avoided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".