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Proceedings Paper

Study of the radiation dose reduction capability of a CT reconstruction algorithm: LCD performance assessment using mathematical model observers
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Paper Abstract

Radiation dose on patient has become a major concern today for Computed Tomography (CT) imaging in clinical practice. Various hardware and algorithm solutions have been designed to reduce dose. Among them, iterative reconstruction (IR) has been widely expected to be an effective dose reduction approach for CT. However, there is no clear understanding on the exact amount of dose saving an IR approach can offer for various clinical applications. We know that quantitative image quality assessment should be task-based. This work applied mathematical model observers to study detectability performance of CT scan data reconstructed using an advanced IR approach as well as the conventional filtered back-projection (FBP) approach. The purpose of this work is to establish a practical and robust approach for CT IR detectability image quality evaluation and to assess the dose saving capability of the IR method under study. Low contrast (LC) objects imbedded in head size and body size phantoms were imaged multiple times with different dose levels. Independent signal present and absent pairs were generated for model observer study training and testing. Receiver Operating Characteristic (ROC) curves for location known exact and location ROC (LROC) curves for location unknown as well as their corresponding the area under the curve (AUC) values were calculated. Results showed approximately 3 times dose reduction has been achieved using the IR method under study.

Paper Details

Date Published: 28 March 2013
PDF: 8 pages
Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86731Q (28 March 2013); doi: 10.1117/12.2007307
Show Author Affiliations
Jiahua Fan, GE Healthcare (United States)
Hsin-Wu Tseng, GE Healthcare (United States)
College of Optical Sciences, The Univ. of Arizona (United States)
Matthew Kupinski, College of Optical Sciences, The Univ. of Arizona (United States)
Guangzhi Cao, GE Healthcare (United States)
Paavana Sainath, GE Healthcare (United States)
Jiang Hsieh, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 8673:
Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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