Rigid-body motion correction in hybrid PET/MRI using spherical navigator echoes
Bibliographic record
Abstract
Integrated positron emission tomography and magnetic resonance imaging (PET/MRI) is an imaging technology that provides complementary anatomical and functional information for medical diagnostics. Both PET and MRI are highly susceptible to motion artifacts due, in part, to long acquisition times. The simultaneous acquisition of the two modalities presents the opportunity to use MRI navigator techniques for motion correction of both PET and MRI data. For this task, we propose spherical navigator echoes (SNAVs)-3D k-space navigators that can accurately and rapidly measure rigid body motion in all six degrees of freedom. SNAVs were incorporated into turbo FLASH (tfl)-a product fast gradient echo sequence-to create the tfl-SNAV pulse sequence. Acquiring in vivo brain images from a healthy volunteer with both sequences first compared the tfl-SNAV and product tfl sequences. It was observed that incorporation of the SNAVs into the image sequence did not have any detrimental impact on the image quality. The SNAV motion correction technique was evaluated using an anthropomorphic brain phantom. Following a stationary reference image where the tfl-SNAV sequence was acquired along with simultaneous list-mode PET, three identical PET/MRI scans were performed where the phantom was moved several times throughout each acquisition. This motion-up to 11° and 14 mm-resulted in motion artifacts in both PET and MR images. Following SNAV motion correction of the MRI and PET list-mode data, artifact reduction was achieved for both the PET and MR images in all three motion trials. The corrected images have improved image quality and are quantitatively more similar to the ground truth reference 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".