A residual dense network assisted sparse view reconstruction for breast computed tomography

Zhiyang Fu, Hsin Wu Tseng, Srinivasan Vedantham, Andrew Karellas, Ali Bilgin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Projection datasets (300 views, full-scan) from 34 women were reconstructed using the FDK algorithm and served as reference. Sparse-view (100 views, full-scan) projection data were reconstructed using the FDK algorithm. The proposed MS-RDN uses the sparse-view and reference FDK reconstructions as input and label, respectively. Our MS-RDN evaluated with respect to fully sampled FDK reference yields superior performance, quantitatively and visually, compared to conventional compressed sensing methods and state-of-the-art deep learning based methods. The proposed deep learning driven framework can potentially enable low dose breast CT imaging.

Original languageEnglish (US)
Article number21111
JournalScientific reports
Volume10
Issue number1
DOIs
StatePublished - Dec 2020

ASJC Scopus subject areas

  • General

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