Deep Learning Based Joint PET Image Reconstruction and Motion Estimation
| العنوان: | Deep Learning Based Joint PET Image Reconstruction and Motion Estimation |
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| المؤلفون: | Tiantian Li, Mengxi Zhang, Wenyuan Qi, Evren Asma, Jinyi Qi |
| المصدر: | IEEE Trans Med Imaging IEEE transactions on medical imaging, vol 41, iss 5 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Optimization, Positron emission tomography, Image Processing, Physics::Medical Physics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Bioengineering, Motion estimation, Article, Strain, Motion, Computer-Assisted, Engineering, Deep Learning, Clinical Research, Information and Computing Sciences, Image Processing, Computer-Assisted, Humans, Electrical and Electronic Engineering, motion correction, joint estimation, Radiological and Ultrasound Technology, Logic gates, image reconstruction, Computer Science Applications, Nuclear Medicine & Medical Imaging, PET, Computer Science::Computer Vision and Pattern Recognition, Positron-Emission Tomography, Biomedical Imaging, Artifacts, Estimation, Algorithms, Software |
| الوصف: | Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. However, conventional motion estimation methods based on registration of a pair of images are sensitive to noise. The goal of this study is to develop a robust joint estimation method that incorporates a deep learning (DL)-based image registration approach for motion estimation. We propose a joint estimation framework by incorporating a learned image registration network into a regularized PET image reconstruction. The joint estimation was formulated as a constrained optimization problem with moving gated images related to a fixed image via the deep neural network. The constrained optimization problem is solved by the alternating direction method of multipliers (ADMM) algorithm. The effectiveness of the algorithm was demonstrated using simulated and real data. We compared the proposed DL-ADMM joint estimation algorithm with a monotonic iterative joint estimation. Motion compensated reconstructions using pre-calculated deformation fields by DL-based (DL-MC recon) and iterative (iterative-MC recon) image registration were also included for comparison. Our simulation study shows that the proposed DL-ADMM joint estimation method reduces bias compared to the ungated image without increasing noise and outperforms the competing methods. In the real data study, our proposed method also generated higher lesion contrast and sharper liver boundaries compared to the ungated image and had lower noise than the reference gated image. |
| وصف الملف: | application/pdf |
| تدمد: | 1558-254X 0278-0062 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f943794cb763be526beee978894a314 https://doi.org/10.1109/tmi.2021.3136553 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5f943794cb763be526beee978894a314 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1558254X 02780062 |
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