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Published on: August 6, 2013
Dynamic frame-by-frame motion correction for 18F-flurpiridaz PET-MPI using convolution neural network
Meghana Urs1, Aditya Killekar1, Valerie Builoff1
1Artificial Intelligence in Medicine Research Center, Departments of Biomedical Sciences, Medicine, and Cardiology, Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Los Angeles, CA, 90048, USA.
Purpose:
Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in 18F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of 18F-flurpiridaz PET.
Methods:
The method employs a 3D-ResNet based architecture that takes 3D PET volumes and outputs motion vectors. It was validated using 5-fold cross-validation on data from 32-sites of a Phase-III clinical trial (NCT01347710). Manual corrections from two experienced operators served as ground truth, and data augmentation using simulated vectors enhanced training robustness. The study compared the DL approach to both manual and standard non-AI automatic MC methods, assessing agreement and diagnostic accuracy using minimal segmental stress MBF and MFR.
Results:
The area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC stress MBF, manual-MC stress MBF from Operators (AUC = 0.897, 0.892 and 0.889, respectively; p > 0.05), standard non-AI automatic MC (AUC = 0.877; p > 0.05) and significantly higher than No-MC (AUC = 0.835; p < 0.05). Similar findings were observed with MFR. The 95% confidence limits for agreement with the operator were ± 0.49 (mean difference = 0.00) for MFR and ± 0.24 ml/g/min (mean difference = 0.00) for stress MBF.
Conclusion:
DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for stress MBF and MFR are in excellent agreement with those manually corrected by experienced operators compared to standard non-AI automatic MC in patients undergoing 18F-flurpiridaz PET-MPI.
Insights
Deep learning motion correction (DL-MC) offers a faster and diagnostically equivalent alternative to manual methods for 18F-flurpiridaz PET myocardial blood flow (MBF) and flow reserve (MFR) quantification. This automated approach achieves excellent agreement with expert manual corrections, improving efficiency in cardiac PET imaging.
Area of Science:
- Nuclear Medicine
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
Background:
- Accurate quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using 18F-flurpiridaz PET is crucial for diagnosing coronary artery disease (CAD).
- Motion artifacts in PET imaging significantly impact the precision of MBF and MFR quantification.
- Current manual motion correction (MC) methods are time-consuming, operator-dependent, and require extensive expertise, leading to inter-observer variability.
Purpose of the Study:
- To develop and validate a deep learning (DL) framework for automated motion correction (MC) in 18F-flurpiridaz PET imaging.
- To compare the diagnostic performance and quantitative agreement of DL-based MC with manual MC and standard non-AI automatic MC methods.
Main Methods:
- A 3D-ResNet architecture was utilized to generate motion vectors from 3D PET volumes.
- The DL framework was trained and validated using data from a Phase-III clinical trial (NCT01347710), with manual corrections by experienced operators serving as ground truth.
- Data augmentation with simulated motion vectors enhanced the robustness of the DL model. Performance was evaluated against manual MC and standard non-AI automatic MC techniques.
Main Results:
- The area under the ROC curve (AUC) for detecting significant CAD was comparable between DL-MC (0.897), manual MC (0.892, 0.889), and superior to no MC (0.835).
- DL-MC demonstrated diagnostic accuracy comparable to standard non-AI automatic MC (AUC 0.877).
- Quantitative analysis showed excellent agreement for MFR (95% confidence limits ±0.49) and stress MBF (±0.24 ml/g/min) between DL-MC and manual MC.
Conclusions:
- Deep learning-based motion correction (DL-MC) provides a significantly faster alternative to manual MC for 18F-flurpiridaz PET.
- DL-MC achieves diagnostic performance comparable to manual MC and superior to no MC in assessing significant CAD.
- The quantitative MBF and MFR results from DL-MC show excellent agreement with expert manual corrections, making it a reliable tool for PET-MPI.
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