GP.05 Intraoperative acquisition of diffusion tensor imaging in cranial neurosurgery: readout-segmented DTI versus standard single-shot DTI
Bibliographic record
Abstract
Background: Diffusion-tensor imaging (DTI) tractography is commonly used in neurosurgical practice, but is largely limited to the preoperative setting. This is due primarily to image degradation caused by susceptibility artifact when conventional single-shot (SS) echo-planar imaging DTI is acquired for open cranial, surgical position intraoperative DTI (iDTI). A novel, artifact-resistant, readout-segmented (RS) DTI has not yet been evaluated in the intraoperative MRI (iMRI) environment. Our objective was to evaluate the performance of RS-DTI versus SS-DTI for intraoperative white matter imaging. Methods: Pre- and intraoperative 3T, T1-weighted and DTI (RS-iDTI and SS-iDTI) in 22 adults undergoing intraaxial iMRI resections (low-grade glioma: 14, 64%; high-grade glioma: 7, 32%; cortical dysplasia: 1). Regional susceptibility artifact, anatomical deviation relative to T1WI, and tractographic output were compared between iDTI sequences. Results: RS-iDTI resulted in less regional susceptibility artifact and mean anatomic deviation (RS-iDTI: 2.7±0.2 mm versus SS-iDTI 7.5±0.4 mm; p<0.0001). Tractographic failure occurred in 8/22 (36%) patients for SS-iDTI whereas RS-iDTI permitted successful reconstruction in 4 of these 8. Maximal tractographic differences between DTI sequences were substantial (mean 9.7±5.7 mm). Conclusions: Readout-segmented EPI enables higher quality and more accurate DTI for surgically relevant tractography of major white matter tracts in intraoperative, open cranium, neurosurgical applications at 3T.
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 | 0.002 | 0.004 |
| 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) | 0.005 | 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".