Open AccessSparse‐View CT Image Recovery Using Two‐Step Iterative Shrinkage‐Thresholding Algorithm
Author(s) -
Chae Byung Gyu,
Lee Sooyeul
Publication year - 2015
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.15.0115.0401
Subject(s) - shrinkage , thresholding , algorithm , projection (relational algebra) , mathematics , iterative reconstruction , radon transform , iterative method , matrix (chemical analysis) , regularization (linguistics) , computer vision , twist , sparse matrix , artificial intelligence , image (mathematics) , computer science , geometry , physics , statistics , materials science , composite material , quantum mechanics , gaussian
We investigate an image recovery method for sparse‐view computed tomography (CT) using an iterative shrinkage algorithm based on a second‐order approach. The two‐step iterative shrinkage‐thresholding (TwIST) algorithm including a total variation regularization technique is elucidated to be more robust than other first‐order methods; it enables a perfect restoration of an original image even if given only a few projection views of a parallel‐beam geometry. We find that the incoherency of a projection system matrix in CT geometry sufficiently satisfies the exact reconstruction principle even when the matrix itself has a large condition number. Image reconstruction from fan‐beam CT can be well carried out, but the retrieval performance is very low when compared to a parallel‐beam geometry. This is considered to be due to the matrix complexity of the projection geometry. We also evaluate the image retrieval performance of the TwIST algorithm using measured projection data.