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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
1Department of Medicine (Division of Artificial Intelligence in Medicine), Cedars-Sinai Medical Center, Los Angeles, CA.
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 MBF and MFR.
Results:
The area under the receiver operating characteristic curves (AUC) for significant CAD were comparable between DL-MC MBF, manual-MC 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 ±0.49ml/g/min (mean difference = 0.00) for MFR and ±0.24ml/g/min (mean difference = 0.00) for MBF.
Conclusion:
DL-MC is significantly faster but diagnostically comparable to manual-MC. The quantitative results obtained with DL-MC for 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
A new deep learning (DL) framework automates motion correction for 18F-flurpiridaz PET imaging, improving myocardial blood flow (MBF) and flow reserve (MFR) quantification. This AI approach is faster and as accurate as manual methods.
Area of Science:
- Nuclear Medicine
- Cardiovascular Imaging
- Artificial Intelligence in Medical Imaging
Background:
- Precise quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using 18F-flurpiridaz PET is crucial for diagnosing coronary artery disease (CAD).
- Accurate MBF and MFR measurements heavily depend on effective motion correction (MC) of PET data.
- Current manual frame-by-frame MC methods are time-consuming, require extensive expertise, and introduce significant 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 accuracy of the DL-based MC method against manual MC and standard non-AI automatic MC techniques.
Main Methods:
- A 3D ResNet architecture was utilized to process 3D PET volumes and generate motion vectors for automated MC.
- The DL framework was trained and validated using data from 32 sites of a Phase III clinical trial (NCT01347710), with manual corrections by two experienced operators serving as ground truth.
- Data augmentation with simulated motion vectors was employed to enhance the robustness of the DL model, which was compared against manual and standard non-AI automatic MC methods.
Main Results:
- The deep learning-based motion correction (DL-MC) for MBF and MFR demonstrated diagnostic accuracy comparable to manual MC in detecting significant CAD (AUCs ranging from 0.889 to 0.897 vs. 0.877 for standard non-AI MC, and significantly higher than No-MC at 0.835).
- Quantitative analysis showed excellent agreement between DL-MC and manual MC for both MBF (95% confidence limits ±0.24ml/g/min) and MFR (95% confidence limits ±0.49ml/g/min).
- The DL-MC method was significantly faster than manual correction while maintaining diagnostic comparability.
Conclusions:
- The proposed DL-MC framework provides a fast and automated solution for motion correction in 18F-flurpiridaz PET-MPI.
- DL-MC achieves diagnostic accuracy and quantitative agreement with manual MC, offering a reliable alternative to manual and standard non-AI automatic methods.
- This automated approach has the potential to improve the efficiency and consistency of MBF and MFR quantification in clinical practice.
