20240041412. FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM simplified abstract (RENSSELAER POLYTECHNIC INSTITUTE)

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FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM

Organization Name

RENSSELAER POLYTECHNIC INSTITUTE

Inventor(s)

Huidong Xie of Troy NY (US)

Ge Wang of Loudonville NY (US)

Hongming Shan of Troy NY (US)

Wenxiang Cong of Albany NY (US)

FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240041412 titled 'FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM

Simplified Explanation

The abstract describes a system for few-view computed tomography (CT) image reconstruction. The system consists of a preprocessing module, a first generator network, and a discriminator network. The preprocessing module applies a ramp filter to an input sinogram to obtain a filtered sinogram. The first generator network learns a filtered back-projection operation using the filtered sinogram and generates a first reconstructed image as output, which corresponds to the input sinogram. The discriminator network determines whether a received image corresponds to the first reconstructed image or a ground truth image. The generator and discriminator networks are part of a Wasserstein Generative Adversarial Network (WGAN) and are optimized using an objective function based on Wasserstein distance and gradient penalty.

  • The system reconstructs CT images using a few-view approach.
  • A preprocessing module applies a ramp filter to the input sinogram to obtain a filtered sinogram.
  • The first generator network learns a filtered back-projection operation and generates a reconstructed image.
  • The discriminator network determines if a received image corresponds to the reconstructed image or a ground truth image.
  • The generator and discriminator networks are part of a WGAN and are optimized using an objective function based on Wasserstein distance and gradient penalty.

Potential applications of this technology:

  • Medical imaging: The system can be used in medical CT scanners to reconstruct high-quality images with fewer views, reducing radiation exposure for patients.
  • Industrial inspection: The system can be applied in non-destructive testing to reconstruct images of objects with limited views, improving inspection accuracy.

Problems solved by this technology:

  • Limited-view CT reconstruction: The system addresses the challenge of reconstructing high-quality CT images using a limited number of views, which is useful in scenarios where complete views are not feasible.
  • Image quality and accuracy: By incorporating a WGAN and optimizing with Wasserstein distance and gradient penalty, the system improves the quality and accuracy of reconstructed images compared to traditional methods.

Benefits of this technology:

  • Reduced radiation exposure: Few-view CT reconstruction reduces the number of views required, resulting in lower radiation exposure for patients during imaging procedures.
  • Improved efficiency: The system enables faster image reconstruction by utilizing a few-view approach, increasing the efficiency of CT scanning.
  • Enhanced image quality: By incorporating a WGAN and optimizing with Wasserstein distance and gradient penalty, the system produces high-quality CT images with improved accuracy and reduced artifacts.


Original Abstract Submitted

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.

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