SPECT Imaging Reconstruction Method Based on Deep Convolutional Neural Network

10/19/2020
by   Charalambos Chrysostomou, et al.
0

In this paper, we explore a novel method for tomographic image reconstruction in the field of SPECT imaging. Deep Learning methodologies and more specifically deep convolutional neural networks (CNN) are employed in the new reconstruction method, which is referred to as "CNN Reconstruction - CNNR". For training of the CNNR Projection data from software phantoms were used. For evaluation of the efficacy of the CNNR method, both software and hardware phantoms were used. The resulting tomographic images are compared to those produced by filtered back projection (FBP) [1], the "Maximum Likelihood Expectation Maximization" (MLEM) [1] and ordered subset expectation maximization (OSEM) [2].

READ FULL TEXT

page 2

page 3

page 4

research
08/09/2021

Deep Convolutional Neural Network for Low Projection SPECT Imaging Reconstruction

In this paper, we present a novel method for tomographic image reconstru...
research
06/08/2017

C-arm Tomographic Imaging Technique for Nephrolithiasis and Detection of Kidney Stones

In this paper, we investigated a C-arm tomographic technique as a new th...
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
08/09/2021

Sinogram Denoise Based on Generative Adversarial Networks

A novel method for sinogram denoise based on Generative Adversarial Netw...
research
04/20/2018

DeepRec: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem

Positron emission tomography (PET) is a cornerstone of modern radiology....
research
11/15/2021

A Fast Convergent Ordered-Subsets Algorithm with Subiteration-Dependent Preconditioners for PET Image Reconstruction

We investigated the imaging performance of a fast convergent ordered-sub...

Please sign up or login with your details

Forgot password? Click here to reset