DirectPET: full-size neural network PET reconstruction from sinogram data

Neural network image reconstruction directly from measurement data is a relatively new field of research, which until now has been limited to producing small single-slice images (e.g., ). We proposed a more efficient network design for positron emission tomography called DirectPET, which is capable...

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Bibliographic Details
Published in:Journal of medical imaging (Bellingham, Wash.) Vol. 7; no. 3; p. 032503
Main Authors: Whiteley, William, Luk, Wing K, Gregor, Jens
Format: Journal Article
Language:English
Published: United States 01.05.2020
Subjects:
deep learning
image reconstruction
positron emission tomography
neural network
medical imaging
ISSN:2329-4302
Online Access:Get more information
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Summary:Neural network image reconstruction directly from measurement data is a relatively new field of research, which until now has been limited to producing small single-slice images (e.g., ). We proposed a more efficient network design for positron emission tomography called DirectPET, which is capable of reconstructing multislice image volumes (i.e., ) from sinograms. Large-scale direct neural network reconstruction is accomplished by addressing the associated memory space challenge through the introduction of a specially designed Radon inversion layer. Using patient data, we compare the proposed method to the benchmark ordered subsets expectation maximization (OSEM) algorithm using signal-to-noise ratio, bias, mean absolute error, and structural similarity measures. In addition, line profiles and full-width half-maximum measurements are provided for a sample of lesions. DirectPET is shown capable of producing images that are quantitatively and qualitatively similar to the OSEM target images in a fraction of the time. We also report on an experiment where DirectPET is trained to map low-count raw data to normal count target images, demonstrating the method's ability to maintain image quality under a low-dose scenario. The ability of DirectPET to quickly reconstruct high-quality, multislice image volumes suggests potential clinical viability of the method. However, design parameters and performance boundaries need to be fully established before adoption can be considered.
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ISSN:2329-4302
DOI:10.1117/1.JMI.7.3.032503