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May, 2022
具有记忆效率的可逆学习原始对偶方法的3D螺旋CT重建
3D helical CT reconstruction with memory efficient invertible Learned Primal-Dual method
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Buda Bajić, Ozan Öktem, Jevgenija Rudzusika
TL;DR
将可逆的学习型基础-对偶 (iLPD) 神经网络架构应用于螺旋三维计算机断层扫描 (CT) 并将几何学和数据分成适合存储的部分，将图像分割成相应的子体积。我们通过实验从现实螺旋几何学模拟的断层图数据中实现了这一点。
Abstract
helical acquisition geometry
is the most common geometry used in
computed tomography
(CT) scanners for medical imaging. We adapt the invertible Learned Primal-Dual (iLPD)
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