Allometry correction in semi-quantitative analysis of dopamine transporter SPECT
Bibliographic record
Abstract
BACKGROUND: Striatal specific binding ratios (SBR) are widely used to support the interpretation of dopamine transporter SPECT scans. Automatic SBR computation often involves using affine transformations to map the individual SPECT images to an anatomical reference space for ROI analysis using predefined standard masks. This does not account for differences in volumetric scaling between brain structures since, by definition, affine transformations preserve volume ratios. However, striatal volume has been reported to scale proportional to (intracranial volume)0.4, indicating particularly pronounced “negative” allometric scaling. This study aimed to investigate the impact of disregarding allometric scaling on putamen SBR, and to propose an easy-to-implement method to avoid this issue. 656 [123I]FP-CIT SPECT (67.2 ± 11.4y, 44.2% females, 52.1/47.9% with reduced/normal striatal signal according to visual interpretation by an expert reader) were spatially normalized by affine transformation to the anatomical reference space of the Montreal Neurological Institute. Unilateral putamen SBR were estimated using hottest voxels analysis in large putamen masks predefined in the reference space. For conventional hottest voxels analysis, the number of hottest voxels was fixed at 1,250 (equivalent to 10 ml, the mean putamen volume in healthy adults). For allometry correction, the number of hottest voxels was determined separately for each subject as 1,250*DET(1−0.4), where DET is the Jacobian determinant of the affine transformation. To characterize the impact of the allometry correction on diagnostic performance, a data-driven Gaussian mixture model was employed. RESULTS: Linear regression in the visually normal SPECT revealed a positive correlation between uncorrected putamen SBR and DET (Pearson’s R = 0.509, 95%-CI 0.422–0.587). The correlation was significantly (p < 0.00005) weaker when allometry-corrected ROI analysis was used (R = 0.285, 95%-CI 0.180–0.383). The effect size of the distance between reduced and normal putamen SBR as determined by the Gaussian mixture model was significantly larger with than without allometry correction (3.979 versus 3.335, one-sided p < 0.0001). When using the visual expert reading as diagnostic reference standard, the overall accuracy of the dichotomized SBR was significantly improved by allometry correction (from 94.4% to 96.2%, one-sided p = 0.026). CONCLUSION: The diagnostic performance of semi-quantitative SBR analyses involving affine transformations to an anatomical reference space can be enhanced through the application of the proposed allometry correction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".