SU‐E‐I‐178: Spatial Analysis of PET Images Using 3D Moment Invariants (3DMI)
Bibliographic record
Abstract
Purpose: We propose to characterize the spatial distribution of PET‐derived voxel statistics within anatomically‐defined Regions Of Interest (ROIs) as a means to characterize disease‐related changes. Methods: We used 3D moment invariants (3DMIs) to characterize the spatial distribution of PET data ([11C]Raclopride, [11C]Tetrabenazine and [18F]Fluorodopa) recorded from subjects with Parkinsonˈs Disease (PD) and healthy controls. 3DMIs are mathematical spatial descriptors designed to be invariant to scaling, translation and rotation. In fMRI studies, assessing the spatial characteristics of voxel‐based statistics has recently been shown to utilized be a powerful and sensitive method to characterize brain activation. Crucially, this allows characterization of the spatial characteristics of activation without the need to warp brain images to a common brain template. Analogously, we propose characterizing the spatial distribution of PET‐derived voxel statistics within anatomically‐defined ROIs using 3DMIs. The 3DMIs used here were a combination of terms describing spatial variance, skewness and kurtosis. Subjects also underwent MRI scans so that the T1‐weighted MRI data could be used to anatomically delineate basal ganglia ROIs to act as binary masks on the PET data. Results: 3DMIs were found to accurately describe the “3D texture” of PET images despite changes in the size and orientation of these regions across subjects. In addition, we were able to find differences in the 3DMIs of PD patients distinct from those of healthy volunteers. These changes suggest that disease‐related variations in the spatial distribution measured using PET can be quantitatively described with the proposed method Conclusions: This method shows great promise to extract additional information from PET data with a wealth of potential applications to disease diagnosis, staging, treatment assessment and more. The quantification of the observed disease‐related changes for PD subjects is currently under way.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".