Material depth reconstruction method of multi-energy X-ray images using neural network.
With the advent of technology, multi-energy X-ray imaging is promising technique that can reduce the patient's dose and provide functional imaging. Two-dimensional photon-counting detector to provide multi-energy imaging is under development. In this work, we present a material decomposition method using multi-energy images. To acquire multi-energy images, Monte Carlo simulation was performed. The X-ray spectrum was modeled and ripple effect was considered. Using the dissimilar characteristics in energy-dependent X-ray attenuation of each material, multiple energy X-ray images were decomposed into material depth images. Feedforward neural network was used to fit multi-energy images to material depth images. In order to use the neural network, step wedge phantom images were used for training neuron. Finally, neural network decomposed multi-energy X-ray images into material depth image. To demonstrate the concept of this method, we applied it to simulated images of a 3D head phantom. The results show that neural network method performed effectively material depth reconstruction.
College of Medicine, BK21, Seoul National University, South Korea.
Woo-Jin
WJ
Lee
Dae-Seung
DS
Kim
Sung-Won
SW
Kang
Won-Jin
WJ
Yi
D003198:Computer Simulation; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D008444:Maxillary Sinus Neoplasms / Q000000981:diagnostic imaging; D008954:Models, Biological; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*