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Radon Inversion via Deep Learning | IEEE Journals & Magazine | IEEE Xplore

Radon Inversion via Deep Learning


Abstract:

The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly i...Show More

Abstract:

The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel reconstruction framework for Radon inversion with deep learning (DL) techniques. For simplicity, the proposed framework is denoted as iRadonMAP, i.e., inverse Radon transform approximation. Specifically, we construct an interpretable neural network that contains three dedicated components. The first component is a fully connected filtering (FCF) layer along the rotation angle direction in the sinogram domain, and the second one is a sinusoidal back-projection (SBP) layer, which back-projects the filtered sinogram data into the spatial domain. Next, a common network structure is added to further improve the overall performance. iRadonMAP is first pretrained on a large number of generic images from the ImageNet database and then fine-tuned with clinical patient data. The experimental results demonstrate the feasibility of the proposed iRadonMAP framework for Radon inversion.
Published in: IEEE Transactions on Medical Imaging ( Volume: 39, Issue: 6, June 2020)
Page(s): 2076 - 2087
Date of Publication: 06 January 2020

ISSN Information:

PubMed ID: 31944948

Funding Agency:


References

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