Work

A practical approach for quantitative estimates of voxel-by-voxel liver perfusion using DCE imaging and a compartmental model

Year: 2006

Type: article

Abstract: Voxel-by-voxel estimation of liver perfusion using nonlinear least-squares fits of dynamic contrast enhanced computed tomography or magnetic resonance imaging data to a compartmental model is a computational expensive process. In this report, a "linear" least-squares method for estimation of liver perfusion is described. Simulated data and the data from an example case of a patient with intrahepatic cancer are presented. Compared to the nonlinear method, the new method can improve computational speed by a factor of approximately 400, which makes it practical for use in clinical trials.

Source: Medical Physics

DOI: 10.1118/1.2219773

Authors: Yue Cao, Jonathan Alspaugh, Zhou Shen, James M. Balter, Theodore S. Lawrence, Randall K. Ten Haken

Institution: University of Michigan

Language: English

Topic: Medical Imaging Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute