Few-view ct image reconstruction system

Abstract

A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

What is claimed is: 
     
         1 . A few-view computed tomography (CT) image reconstruction system, the system comprising:
 a generator network configured to receive a few-view sinogram, and to generate a reconstructed image corresponding to the few-view sinogram; and   a discriminator network configured to receive an input image, and to determine whether the received input image corresponds to the reconstructed image or a ground truth image,   wherein the generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN), the generator network is configured to learn a reconstruction process in a point-wise manner, and a trained generator network is configured to reconstruct a few-view CT image directly from a corresponding input few-view sinogram.   
     
     
         2 . The system of  claim 1 , wherein the generator network comprises a point-wise fully-connected layer. 
     
     
         3 . The system of  claim 1 , wherein the generator network is configured to reconstruct the reconstructed image using O(C×N×N v ) parameters, where N is a dimension of the reconstructed image, N v  is a number of projections and C is an adjustable hyper-parameter in the range of 1 to N. 
     
     
         4 . The system of  claim 1 , wherein the WGAN is trained, initially, using image data from an image database comprising a plurality of images. 
     
     
         5 . The system of  claim 1 , wherein an objective function used during training comprises a Wasserstein distance and a gradient penalty. 
     
     
         6 . The system of  claim 1 , wherein an objective function that is configured to optimize the generator network during training comprises an error term, and a structural similarity index term. 
     
     
         7 . The system of  claim 1 , wherein the generator network corresponds to a back propagation network. 
     
     
         8 . A method for few-view computed tomography (CT) image reconstruction, the method comprising:
 receiving, by a generator network, a few-view sinogram;   generating, by the generator network, a reconstructed image corresponding to the few-view sinogram;   receiving, by a discriminator network, an input image; and   determining, by the discriminator network, whether the received input image corresponds to the reconstructed image or a ground truth image,   wherein the generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN), the generator network is configured to learn a reconstruction process in a point-wise manner, and a trained generator network is configured to reconstruct a few-view CT image directly from a corresponding input few-view sinogram.   
     
     
         9 . The method of  claim 8 , wherein the generator network comprises a point-wise fully-connected layer. 
     
     
         10 . The method of  claim 8 , wherein the generator network is configured to reconstruct the reconstructed image using O(C×N×N v ) parameters, where N is a dimension of the reconstructed image, N v  is a number of projections and C is an adjustable hyper-parameter in the range of 1 to N. 
     
     
         11 . The method of  claim 8 , wherein the WGAN is trained, initially, using image data from an image database comprising a plurality of images. 
     
     
         12 . The method of  claim 8 , wherein an objective function used during training comprises a Wasserstein distance and a gradient penalty. 
     
     
         13 . The method of  claim 8 , wherein an objective function that is configured to optimize the generator network during training comprises an error term, and a structural similarity index term. 
     
     
         14 . The method of  claim 8 , wherein the generator network corresponds to a back propagation network. 
     
     
         15 . A computer readable storage device having stored thereon instructions configured for few-view computed tomography (CT) image reconstruction, the instructions that when executed by one or more processors result in the following operations comprising:
 receiving a few-view sinogram;   generating a reconstructed image corresponding to the few-view sinogram;   receiving an input image; and   determining whether the received input image corresponds to the reconstructed image or a ground truth image,   wherein the operations correspond to a Wasserstein generative adversarial network (WGAN), a reconstruction process is learned in a point-wise manner, and a trained generator network is configured to reconstruct a few-view CT image directly from a corresponding input few-view sinogram.   
     
     
         16 . The device of  claim 15 , wherein the generator network comprises a point-wise fully-connected layer. 
     
     
         17 . The device of  claim 15 , wherein the reconstructed image is reconstructed using O(C×N×N v ) parameters, where N is a dimension of the reconstructed image, N v  is a number of projections and C is an adjustable hyper-parameter in the range of 1 to N. 
     
     
         18 . The device of  claim 15 , wherein the instructions that when executed by one or more processors result in the following additional operations comprising: training the WGAN, initially, using image data from an image database comprising a plurality of images. 
     
     
         19 . The device of  claim 15 , wherein an objective function used during training comprises a Wasserstein distance and a gradient penalty. 
     
     
         20 . The device of  claim 15 , wherein an objective function that is configured to optimize the generator network during training comprises an error term, and a structural similarity index term.