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Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN
Shiwei Zhou1, Jinyu Yang2, Krishnateja Konduri3
1Department of Physics, University of Texas at Arlington, Arlington, TX, United States of America.
Insights
RecycleGAN, a spatiotemporal deep learning method, enhances low-dose computed tomography angiography (CTA) image quality by utilizing temporal information. This approach improves denoising performance compared to previous methods for coronary artery disease diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is crucial for diagnosing coronary artery disease.
- Reducing radiation dose in MP-CTA is essential due to the need for wide cardiac phase coverage.
- Current dose reduction techniques involve acquiring only a portion of cardiac phases at full dose.
Purpose of the Study:
- To develop a spatiotemporal deep learning method for enhancing low-dose CTA images.
- To improve image quality at reduced radiation dose phases in MP-CTA.
- To introduce RecycleGAN, an advancement over CycleGAN for temporal denoising.
Main Methods:
- Developed RecycleGAN, a recurrent network-based deep learning model, to translate low-dose to full-dose image sequences.
- Utilized the XCAT phantom program for realistic MP-CTA image sequence generation for training and testing.
- Evaluated RecycleGAN's denoising performance against CycleGAN using simulated and clinical MP-CTA datasets.
Main Results:
- RecycleGAN demonstrated superior denoising performance compared to CycleGAN in simulated MP-CTA images.
- Quantitative metrics and visual inspection confirmed RecycleGAN's enhanced image quality.
- Clinical MP-CTA images further validated the superior denoising capabilities of RecycleGAN.
Conclusions:
- RecycleGAN effectively enhances the quality of low-dose CTA images by leveraging temporal information.
- The proposed spatiotemporal deep learning method offers a promising solution for radiation dose reduction in MP-CTA.
- RecycleGAN represents a significant advancement in denoising low-dose CT images for improved cardiac imaging.
Abstract:
Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is frequently used for diagnosis of coronary artery disease. Radiation dose may become a potential concern as the scan needs to cover a wide range of cardiac phases during a heart cycle. A common method to reduce radiation is to limit the full-dose acquisition to a predefined range of phases while reducing the radiation dose for the rest. Our goal in this study is to develop a spatiotemporal deep learning method to enhance the quality of low-dose CTA images at phases acquired at reduced radiation dose. Recently, we demonstrated that a deep learning method, Cycle-Consistent generative adversarial networks (CycleGAN), could effectively denoise low-dose CT images through spatial image translation without labeled image pairs in both low-dose and full-dose image domains. As CycleGAN does not utilize the temporal information in its denoising mechanism, we propose to use RecycleGAN, which could translate a series of images ordered in time from the low-dose domain to the full-dose domain through an additional recurrent network. To evaluate RecycleGAN, we use the XCAT phantom program, a highly realistic simulation tool based on real patient data, to generate MP-CTA image sequences for 18 patients (14 for training, 2 for validation and 2 for test). Our simulation results show that RecycleGAN can achieve better denoising performance than CycleGAN based on both visual inspection and quantitative metrics. We further demonstrate the superior denoising performance of RecycleGAN using clinical MP-CTA images from 50 patients.
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