Patient body motion correction for dynamic cardiac <scp>PET</scp>‐<scp>CT</scp> by attenuation‐emission alignment according to projection consistency conditions
Bibliographic record
Abstract
INTRODUCTION: Patient body motion is known to cause large deviations in the determination of myocardial blood flow (MBF) with errors exceeding 300%. Accurate correction for patient whole-body motion is still a largely unsolved problem in cardiac positron emission tomography (PET) imaging. OBJECTIVE: This study evaluated the efficacy of using Natterer's formulation of the Helgason-Ludwig consistency conditions on the two-dimensional Radon transform to align computed tomography to PET projection data in multiple time frames of a dynamic sequence for the purpose of frame-by-frame correction of rigid whole-body motion. METHODS: The correction algorithm was evaluated with digital NCAT phantoms using realistic noise added by the analytical simulator. Count rates used in the simulation were derived from clinical patient data. In addition, a proof of concept test using measured data with a cardiac torso phantom was conducted. RESULTS: Motion correction resulted in significant improvement in the accuracy of MBF estimates, especially for high count-rate acquisitions. Maximum errors for 2 cm of motion dropped from 325% to 25% and from 250% to 25% using global and regional partial-volume correction, respectively. Median MBF errors dropped from 33% to 4.5% and 27% to 3.8%, respectively. Importantly, the correction algorithm performed equally well to compensate for body motion in both early and late time frames. CONCLUSION: Cardiac PET-CT data used for attenuation correction (CTAC) alignment using projection consistency conditions was effective for reducing errors in MBF measurements due to simulated patient motion, and can be integrated into the image reconstruction workflow.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".