Читать книгу Machine Learning for Tomographic Imaging - Professor Ge Wang - Страница 33

As we discussed in the first chapter, a reconstructed CT image suffers from degraded image quality in the case of low-dose scanning. How to maintain or improve the diagnostic performance is the key issue associated with low-dose CT. Inspired by compressive sensing theory, the sparse constraint in terms of dictionary learning is developed as an effective way for a sparse representation. Recently, a dictionary learning-based approach for low-dose x-ray CT was proposed by Qiong Xu et al (2012), in which a redundant dictionary is incorporated into the statistical reconstruction framework. The dictionary can be either predetermined before image reconstruction or adaptively defined during an image reconstruction process. We will describe both the modes for dictionary learning-based low-dose CT in the following.