Sci—Fri AM: Imaging — 06: The role of body mass and gender in atlas construction for attenuation correction in PET/MRI
Bibliographic record
Abstract
Attenuation correction (AC) in PET/MRI is difficult as there is no clear relationship between MR signal and 511 keV attenuation coefficients (μ) as there is with CT. One approach is to register a pre-defined atlas of μ to the PET/MRI for AC. However, the design of the atlas may strongly influence the quantitative accuracy of the AC. Here we compare 3 different atlas design approaches and evaluate their performance in an oncology patient population. The 3 strategies were: use of BMI-dependent atlases; use of gender-dependent atlases, and use of a gender- and sex-independent atlas. Seventeen patients were imaged with FDG PET/CT and subsequently scanned with 3T MRI. MR and PET/CT images were coregistered, CT scans converted to μ-maps, and the resulting MRI/μ-map paired data were used to construct 6 atlases: averaged male and female atlases, averaged BMI-specific atlases (obese >30, overweight 25-29.9, Normal 18.5-24.9), and a single atlas comprised of all patients averaged together. The atlases were then used for PET AC for patients not included in the construction of the atlas in a leave-one-out manner. Resulting PET images were compared to each other and to the gold-standard CT-based PET reconstructions across all voxels and tissue-specific regions (soft-tissue, bone, lung). Sex-specific atlases yielded best results (average relative percent error over the 3 VOIs = 0.4509) & BMI-based atlases yielded highest average relative percent error at 0.9340. In all cases, highest errors were in the VOIs located in the livers.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.184 | 0.096 |
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".