论文标题
CT超级分辨率通过零射击学习
CT Super Resolution via Zero Shot Learning
论文作者
论文摘要
计算机断层扫描（CT）是一种用于许多重要应用中的高级成像技术。在这里，我们提出了一种基于深度学习（DL）的CT超分辨率（SR）方法，该方法可以将低分辨率（LR）辛图重建为高分辨率（HR）CT图像。该方法协同结合了Sinogram域，图像域中的DeBlur模型以及迭代框架中的SR模型，将基于CT SR算法超级分辨率和基于DeBlur的迭代重建（SACIR）中。我们将CT域知识纳入了Sadir，并将其展开到DL网络（Sadir Net）中。 Sadir Net是一个零射击学习（ZSL）网络，可以在测试时间对单个辛图进行训练和测试。通过CATPHAN700物理幻影和生物HAM的SR CT成像评估了Sadir，并将其性能与基于其他基于DL的方法进行了比较。结果表明，零射击萨迪尔网络确实可以提供与CT SR重建的其他SOTA方法相当的性能，尤其是在训练数据受到限制的情况下。 Sadir方法可以找到用于改进CT分辨率超出硬件限制或降低CT硬件要求的用途。
Computed Tomography (CT) is an advanced imaging technology used in many important applications. Here we present a deep-learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high resolution (HR) CT images. The method synergistically combines a SR model in sinogram domain, a deblur model in image domain, and the iterative framework into a CT SR algorithm super resolution and deblur based iterative reconstruction (SADIR). We incorporated the CT domain knowledge into the SADIR and unrolled it into a DL network (SADIR Net). The SADIR Net is a zero shot learning (ZSL) network, which can be trained and tested with a single sinogram in the test time. The SADIR was evaluated via SR CT imaging of a Catphan700 physical phantom and a biological ham, and its performance was compared to the other state of the art (SotA) DL-based methods. The results show that the zero-shot SADIR-Net can indeed provide a performance comparable to the other SotA methods for CT SR reconstruction, especially in situations where training data is limited. The SADIR method can find use in improving CT resolution beyond hardware limits or lowering requirement on CT hardware.
