SU‐F‐I‐58: Image Quality Comparisons of Different Motion Magnitudes and TR Values in MR‐PET
Bibliographic record
Abstract
Purpose: The aim of this work is to evaluate the accuracy and sensitivity of a respiratory‐triggered MR‐PET protocol in detecting four different sized lesions at two different magnitudes of motion, with two different TR values, using a novel PET‐MR‐CT compatible respiratory motion phantom. Methods: The eight‐compartment torso phantom was setup adjacent to the motion stage, which moved four spherical compartments (28, 22, 17, 10 mm diameter) in two separate (1 and 2 cm) linear motion profiles, simulating a 3.5 second respiratory cycle. Scans were acquired on a 3T MR‐PET system (Biograph mMR; Siemens Medical Solutions, Germany). MR measurements were taken with: 1) Respiratory‐triggered T2‐weighted turbo spin echo (BLADE) sequence in coronal orientation, and 2) Real‐time balanced steady‐state gradient echo sequence (TrueFISP) in coronal and sagittal planes. PET was acquired simultaneously with MR. Sphere geometries and motion profiles were measured and compared with ground truths for T2 BLADE‐TSE acquisitions and real time TrueFISP images. PET quantification and geometry measurements were taken using standardized uptake values, voxel intensity plots and were compared with known values, and examined alongside MR‐based attenuation maps. Contrast and signal‐to‐noise ratios were also compared for each of the acquisitions as functions of motion range and TR. Results: Comparison of lesion diameters indicate the respiratory triggered T2 BLADE‐TSE was able to maintain geometry within −2 mm for 1 cm motion for both TR values, and within −3.1 mm for TR = 2000 ms at 2 cm motion. Sphere measurements in respiratory triggered PET images were accurate within +/− 5 mm for both ranges of motion for 28, 22, and 17 mm diameter spheres. Conclusion: Hybrid MR‐PET systems show promise in imaging lung cancer in non‐compliant patients, with their ability to acquire both modalities simultaneously. However, MR‐based attenuation maps are still susceptible to motion derived artifacts and pose the potential to affect PET accuracy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".