Sinogram Denoise Based on Generative Adversarial Networks

08/09/2021
by   Charalambos Chrysostomou, et al.
0

A novel method for sinogram denoise based on Generative Adversarial Networks (GANs) in the field of SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method Shepp Logan based phantom, with various noise levels added where used. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. As the results show, the proposed method significantly denoise the sinograms and significantly improves the reconstructions. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/09/2021

SPECT Angle Interpolation Based on Deep Learning Methodologies

A novel method for SPECT angle interpolation based on deep learning meth...
research
10/19/2020

SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network

In this paper, we explore a novel method for tomographic image reconstru...
research
10/26/2018

Building Footprint Generation Using Improved Generative Adversarial Networks

Building footprint information is an essential ingredient for 3-D recons...
research
12/17/2018

Latent Dirichlet Allocation in Generative Adversarial Networks

Mode collapse is one of the key challenges in the training of Generative...
research
10/26/2018

CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

Our main motivation is to propose an efficient approach to generate nove...
research
11/28/2019

Decoding Cosmological Information in Weak-Lensing Mass Maps with Generative Adversarial Networks

Galaxy imaging surveys enable us to map the cosmic matter density field ...
research
04/19/2014

Geometric Abstraction from Noisy Image-Based 3D Reconstructions

Creating geometric abstracted models from image-based scene reconstructi...

Please sign up or login with your details

Forgot password? Click here to reset