P2‐019: The effect of head positioning and repositioning on SIENA‐generated measures of brain volume change: Results from actual Z‐shifts
Bibliographic record
Abstract
Accurate, reliable quantification of brain volume change (BVC) in AD patients is important. This can be affected, however, by local-volume changes related to MRI-scanner-gradient nonlinearities and inconsistent positioning of subjects within the scanner (particularly along the Z-axis). Furthermore, standard canthomeatal (CM) alignment results in the cerebrum being centered several cms away from isocenter (Fig-A). Use actual MRI data to quantify and correct the effect of Z-shift-associated gradient-distortion (GD) on SIENA-generated measures of %-BVC (PBVC). High-resolution, volumetric T1-weighted MRI data were acquired in 9 normal adults on a Siemens Sonata 1.5T scanner after: (i) CM-alignment (CM, Fig-A), (ii) moving the scanner bed 50-mm out of the magnet (Z50), and (iii) accurate-as-possible CM repositioning (Repos). A GD field was generated using spherical harmonic expansion to map coordinates from an "ideal" coordinate system (a Lego-DUPLO® phantom) to the imaging coordinate system of the scanner (Fig-B). SIENA v2.5 was used to quantify the amount of PBVC observed between each subject's: (i) CM and Z50 scans, and (ii) CM and Repos scans; both before and after correcting for the observed GD. Relative to CM images, the mean (range) MRI-measured Z-shift was 4.3-mm (-9.0 to 21.1) for Repos images, and -49.2-mm (-50.9 to -48.4) for Z50 images. As shown in Fig-C, Repos-vs.-CM SIENAs had: (i) a median (mean) absolute-error (AE) of 0.15% (0.17%), precision similar to the original SIENA validation studies; and (ii) a max-AE of only 0.35%. Z50-vs.-CM SIENAs had: (i) a higher max-AE of 0.81%, and (ii) a significantly-higher mean-AE of 0.40% (p = 0.003). Correcting for GD: (i) reduced the Z50-vs.-CM max-AE to 0.23%; and (ii) significantly reduced the Z50-vs.-CM mean-AE to 0.15% (p = 0.001), which did not differ from the Repos-vs.-CM mean-AE (p = 0.969). As shown in Fig-D, a strong relationship was found between Z50-vs.-CM PBVC and CM-image Z-location (r = 0.81). Z-shift-associated GDs can have significant effects on observed SIENA-PBVC values, with Z-shifts of similar magnitude having different effects depending on where they center. Accordingly, inadvertent Z-shifts should be avoided or corrected. Consistently aligning the centers of the cerebrum and magnet may decrease the effects of GD on observed BVC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".