تقرير
Active MR k-space Sampling with Reinforcement Learning
| العنوان: | Active MR k-space Sampling with Reinforcement Learning |
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| المؤلفون: | Pineda, Luis, Basu, Sumana, Romero, Adriana, Calandra, Roberto, Drozdzal, Michal |
| المصدر: | LNCS vol. 12262 (2020) 23-33 |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors. Comment: Presented at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-59713-9_3 |
| URL الوصول: | http://arxiv.org/abs/2007.10469 |
| رقم الأكسشن: | edsarx.2007.10469 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-59713-9_3 |
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