Impact of Missing Attenuation Correction of Attachable MR Coil Components on PET Quantification in Simultaneous PET/MR Scans
Bibliographic record
Abstract
We describe the impact of missing attenuation correction of attachable MR coil components such as the fMRI mirror and coil spacer on PET quantification in simultaneous PET/MR scans. As the vendor provided attenuation correction (AC) does not contain any of the attachable components, CT scans of MR coils with different attachable component(s) were conducted to generate a more accurate (optimal) AC for PET reconstructions. Since it is not practical to perform CT scans for all possible positions of the attachable and adjustable components such as the mirror, the position of the attachable components was fixed based on volunteers' feedbacks to limit the number of CT scans. A uniform PET phantom was scanned within the 12 channel (ch) head and neck unit (HNU) coil, and another uniform phantom was scanned within the 48ch head coil with the mirror and/or spacer attached, on the GE SIGNA PET/MR. Data were reconstructed using TOF-OSEM with 4 iterations and 28 subsets using the complete/optimal AC as well as the sub-optimal AC (i.e. missing attachable component(s)). For the HNU coil, up to 5 % underestimation in activity concentration was observed due to the missing mirror AC. In general, when the mirror is missing in the AC, the closer the voxels to the mirror, the worse the underestimation bias. For the 48ch head coil, up to 10 % underestimation was observed due to the missing mirror in the AC. As the spacer introduced a shift in position of the upper part of the coil, the sub-optimal AC produced a +/- 10 % bias pattern on the transaxial slices covered by the spacer. Sub-optimal AC also leads to artificial non-uniformity in the reconstructed PET images.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".