Elastic motion correction improves Rb-82 Cardiac PET ECG-gated image quality
Bibliographic record
Abstract
23 Objectives: ECG-gated imaging for evaluation of left ventricular (LV) contractile function is an integral part of myocardial perfusion imaging (MPI) but can be challenging with Rb-82 due to the short half-life and low count-statistics. Motion-compensated image reconstruction was reported recently on some latest-generation PET-CT scanners, but has not been optimized for Rb-82 PET MPI. This study evaluated the effect of elastic motion-compensated (MOCO) image reconstruction on ECG-gated Rb-82 PET image quality. Methods: ECG-gated and ungated (static) images were analyzed at rest and stress from N=20 sequential patients referred for Rb-82 MPI (9 MBq/kg) on a PET-CT scanner with ≍200 ps time-of-flight (TOF) resolution. Standard (no-MOCO) ECG-gated images were reconstructed using 6 mm Gaussian filter, and used to estimate contractile and respiratory motion vector fields (MVF). Additional ECG-gated images were then reconstructed using the contractile-MVF (single-MOCO) and combined respiratory- and contractile-MVF (dual-MOCO) information integrated into the iterative reconstruction algorithm (OSEM with 4 iterations and 5 subsets). Static (ungated) images at rest were also reconstructed using 2, 4, 6 mm Gaussian filters for comparison of image quality. Myocardium signal recovery was measured as the maximum activity in the left ventricle (LV) at end-diastole (ED). Background signal and noise were measured as the left atrium blood cavity mean and standard deviation, also at the ED phase. LV myocardium signal-to-noise ratio (SNR) and myocardium-to-blood contrast-to-noise ratio (CNR) values were calculated for the static and ECG-gated images. SNR and CNR were compared between reconstruction methods using paired t-tests. Results: End-diastolic image SNR and CNR increased in 95% (or 55%) of patients at rest using single-MOCO (or dual-MOCO) compared to standard ECG-gated reconstruction. Similarly at stress, SNR and CNR increased in 100% (or 60%) of patients using single-MOCO (or dual-MOCO) reconstruction. Single-MOCO reconstruction significantly improved SNR (+48%) and CNR (+51%), both at stress (+43%) and rest (+56%) (all P P > 0.01), with 40% of scans resulting in lower image quality compared to the standard (no-MOCO) gated reconstruction. The dual-MOCO gated images reconstructed with 6mm filter had image SNR and CNR that were similar to static ungated image reconstructed with 2 mm filtering, whereas the single-MOCO gated images were similar to the static images with 4 mm filtering. Both single- and dual-MOCO images had lower SNR and CNR compared to the static images reconstructed with the same 6 mm filter, suggesting that there was some residual noise in the single-MOCO estimated MVF that could be further improved. Image quality (SNR and CNR) decreased with patient weight (both P < 0.05), likely due to increased attenuation effects despite the use of proportional weight-based dosing, suggesting that larger patients require even higher administered activity to achieve uniform image quality. Conclusions: Single (contractile) motion-compensated image reconstruction improved the end-diastolic image SNR and CNR over standard uncompensated or dual motion-compensated image reconstruction, and is recommended for optimal ECG-gated image quality using Rb-82 on a current-generation TOF PET-CT scanner.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".