Work

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography

Year: 2014

Type: article

Abstract: One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

Source: Journal of medical imaging

DOI: 10.1117/1.jmi.1.3.031007

Authors: Emil Y. Sidky, David N. Kraemer, Erin G. Roth, Christer Ullberg, Ingrid S. Reiser, Xiaochuan Pan

Institutions: University of Chicago, University of Chicago Medical Center, XCounter (Sweden)

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: hybrid

Funders: National Science Foundation, National Institutes of Health, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering