  Dual energy computed tomography (DECT) has become of particular interest in
clinic recent years. The DECT scan comprises two images, corresponding to two
photon attenuation coefficients maps of the objects. Meanwhile, the DECT images
are less accessible sometimes, compared to the conventional single energy CT
(SECT). This motivates us to simulate pseudo DECT (pDECT) images from the SECT
images. Inspired by recent advances in deep learning, we present a deep
learning based framework to yield pDECT images from SECT images, utilizing the
intrinsic characteristics underlying DECT images, i.e., global correlation and
high similarity. To demonstrate the performance of the deep learning based
framework, a cascade deep ConvNet (CD-ConvNet) approach is specifically
presented in the deep learning framework. In the training step, the CD-ConvNet
is designed to learn the non-linear mapping from the measured energy-specific
(i.e., low-energy) CT images to the desired energy-specific (i.e., high-energy)
CT images. In the testing step, the trained CD-ConvNet can be used to yield
desired high-energy CT images from the low-energy CT images, and then produce
accurate basic material maps. Clinical patient data were employed to validate
and evaluate the presented CD-ConvNet approach performance. Both visual and
quantitative results demonstrate the presented CD-ConvNet approach can yield
high quality pDECT images and basic material maps.
