Open AccessObserver Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction
Author(s) -
Joel G. Fletcher,
Lifeng Yu,
Zhoubo Li,
Armando Manduca,
Daniel J. Blezek,
David M. Hough,
Sudhakar K. Venkatesh,
Gregory C. Brickner,
Joseph G. Cernigliaro,
Amy K. Hara,
Jeff L. Fidler,
David S. Lake,
Maria M. Shiung,
David Lewis,
Shuai Leng,
Kurt E. Augustine,
Rickey E. Carter,
David R. Holmes,
Cynthia H. McCollough
Publication year - 2015
Publication title -
radiology
Language(s) - English
Resource type - Journals
eISSN - 1527-1315
pISSN - 0033-8419
DOI - 10.1148/radiol.2015141991
Subject(s) - medicine , nuclear medicine , radiology , iterative reconstruction , radon transform , image noise , receiver operating characteristic , malignancy , mathematics , artificial intelligence , image (mathematics) , computer science , mathematical analysis
To determine if lower-dose computed tomographic (CT) scans obtained with adaptive image-based noise reduction (adaptive nonlocal means [ANLM]) or iterative reconstruction (sinogram-affirmed iterative reconstruction [SAFIRE]) result in reduced observer performance in the detection of malignant hepatic nodules and masses compared with routine-dose scans obtained with filtered back projection (FBP).