Creation and validation of an I-123 FP-CIT template for statistical image analysis using high-resolution SPECT for parkinsonian patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to create a new template for the anatomical normalization of I-123 FP-CIT SPECT images of Japanese people to evaluate dopamine transporter binding. METHODS: The subjects consisted of 16 normal control subjects (5 females and 11 males; mean age ± SD, 51.6 ± 9.5 years, ranging from 25 to 62 years) and 21 parkinsonian patients (7 females and 14 males; mean age ± SD, 70.7 ± 9.4 years, ranging from 49 to 85 years). All normal control subjects and 21 patients with parkinsonism underwent MRI. A total of 148 MBq of I-123 FP-CIT was intravenously injected as a bolus, and a SPECT scan was started 4 h later. Data were analyzed with the Statistical Parametric Mapping 8 (SPM8) software. At first, I-123 FP-CIT SPECT images were co-registered to MRI images and MRI images were normalized to Montreal Neurological Institute (MNI) space using a gray.nii template. Co-registered I-123 FP-CIT SPECT images were normalized using the predetermined normalization parameters for MRI images. Then, anatomically normalized I-123 FP-CIT SPECT images were divided by background counts individually measured using ROIs set on the cerebral cortices. The I-123 FP-CIT template was created by averaging the normalized SPECT images of the 16 normal control subjects. Thereafter, the averaged MRI images of the 16 normal control subjects were also created. RESULTS: A visual inspection revealed that there were no apparent differences between the I-123 FP-CIT images subjected to the two methods of anatomical normalization in normal control subjects. However, a group comparison by a paired t test using SPM8 revealed that the I-123 FP-CIT binding was significantly higher in the substriatal and temporal regions in I-123 FP-CIT images directly normalized with the I-123 FP-CIT template than in those normalized by the predetermined parameters with MRI, while it was higher in the bilateral frontal cortical regions in the latter than in the former images. CONCLUSION: We successfully created an I-123 FP-CIT template for Japanese people. This template is thought to be useful and reliable for the statistical analysis of I-123 FP-CIT images, although some problems exist in the evaluation of parkinsonian patients. The results of a paired t test using SPM suggest that we should use the same normalization method in statistical image analyses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".