Compensation of Image Degradations in Emission Computed Tomography by Deep Learning

Abstract

To reduce image artifacts originating from statistical noise and photon attenuation in emission CT, iterative image reconstruction algorithms have been widely used. However, these methods require high computational costs. Furthermore, in recent years, attenuation correction methods using anatomical CT/MRI images have been investigated, but these methods tend to require rather complicated computations. In this paper, we propose use of convolutional neural network (CNN) for the image correction in the emission CT. In this method, we input an FBP reconstructed PET/SPECT image with no smoothing and no attenuation correction into the CNN, which is processed by the CNN to obtain a final corrected image. We also investigated to input a pair of an FBP degraded image and a corresponding CT image (multimodality image) into the CNN. The learning of CNN was performed by using a set of images constructed through simulating image formation process of the emission CT. The simulation results demonstrate that the CNN-based method enables noise reduction and attenuation correction simultaneously in a short time. Also, the results demonstrate that inputting a CT image in addition to an emission CT image further improves the correction of low frequency artifact.