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

=FEW-VIEW CT IMAGE RECONSTRUCTION SYSTEM=


==Organization Name==

[[:Category:RENSSELAER POLYTECHNIC INSTITUTE|RENSSELAER POLYTECHNIC INSTITUTE]]


[[Category:RENSSELAER POLYTECHNIC INSTITUTE]]

==Inventor(s)==

[[:Category:Huidong Xie of Troy NY (US)|Huidong Xie of Troy NY (US)]][[Category:Huidong Xie of Troy NY (US)]]

[[:Category:Ge Wang of Loudonville NY (US)|Ge Wang of Loudonville NY (US)]][[Category:Ge Wang of Loudonville NY (US)]]

[[:Category:Hongming Shan of Troy NY (US)|Hongming Shan of Troy NY (US)]][[Category:Hongming Shan of Troy NY (US)]]

[[:Category:Wenxiang Cong of Albany NY (US)|Wenxiang Cong of Albany NY (US)]][[Category: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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