SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.

ABSTRACT:

Purpose

The problem of image denoising in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a fundamental challenge. Although various image processing techniques have been presented, they may degrade the contrast of denoised images. The proposed idea in this study is to use a deep neural network as the denoising procedure during the iterative reconstruction process rather than the post-reconstruction phase. This method could decrease the background coefficient of variation (COV_bkg) of the final reconstructed image, which represents the amount of random noise, while improving the contrast-to-noise ratio (CNR).

Methods

In this study, a generative adversarial network is used, where its generator is trained by a two-phase approach. In the

SUBMITTER: Yousefzadeh F 

PROVIDER: S-EPMC11461437 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature