  X-ray Computed Tomography (CT) is an important tool in medical imaging to
obtain a direct visualization of patient anatomy. However, the x-ray radiation
exposure leads to the concern of lifetime cancer risk. Low-dose CT scan can
reduce the radiation exposure to patient while the image quality is usually
degraded due to the appearance of noise and artifacts. Numerous studies have
been conducted to regularize CT image for better image quality. Yet, exploring
the underlying manifold where real CT images residing on is still an open
problem. In this paper, we propose a fully data-driven manifold learning
approach by incorporating the emerging deep-learning technology. An
encoder-decoder convolutional neural network has been established to map a CT
image to the inherent low-dimensional manifold, as well as to restore the CT
image from its corresponding manifold representation. A novel reconstruction
algorithm assisted by the leant manifold prior has been developed to achieve
high quality low-dose CT reconstruction. In order to demonstrate the
effectiveness of the proposed framework, network training, testing, and
comprehensive simulation study have been performed using patient abdomen CT
images. The trained encoder-decoder CNN is capable of restoring high-quality CT
images with average error of ~20 HU. Furthermore, the proposed manifold prior
assisted reconstruction scheme achieves high-quality low-dose CT
reconstruction, with average reconstruction error of < 30 HU, more than five
times and two times lower than that of filtered back projection method and
total-variation based iterative reconstruction method, respectively.
