Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 3; no. 2; pp. 109 - 119
• Main Authors: Lee, Hoyeon, Lee, Jongha, Kim, Hyeongseok, Cho, Byungchul, Cho, Seungryong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Commercialization; Computed tomography; Convolution; Data sampling; Deep learning; Image processing; Image reconstruction; Interpolation; Iterative algorithms; Iterative methods; low-dose computed tomography (CT); Machine learning; Mathematical analysis; Medical imaging; Missing data; Neural networks; Scanners; sparse-view CT; Synthesis; Training; view interpolation

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option t...