1 research outputs found
Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network
Energy-resolving computed tomography (ErCT) has the ability to acquire
energy-dependent measurements simultaneously and quantitative material
information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging
system is usually equipped with an advanced photon counting detector, which is
expensive and technically complex. Therefore, clinical ErCT scanners are not
yet commercially available, and they are in various stage of completion. This
makes the researchers less accessible to the ErCT images. In this work, we
investigate to produce ErCT images directly from existing energy-integrating CT
(EiCT) images via deep neural network. Specifically, different from other
networks that produce ErCT images at one specific energy, this model employs a
unified generative adversarial network (uGAN) to concurrently train EiCT
datasets and ErCT datasets with different energies and then performs
image-to-image translation from existing EiCT images to multiple ErCT image
outputs at various energy bins. In this study, the present uGAN generates ErCT
images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images
at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image
slices and show that the present uGAN model can produce promising ErCT
estimation results compared with the ground truth qualitatively and
quantitatively.Comment: 5 pages, 3 figures, Accepted by MIC/NSS 201