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A practical approach for quantitative estimates of voxel‐by‐voxel liver perfusion using DCE imaging and a compartmental model
Author(s) -
Cao Yue,
Alspaugh Jonathan,
Shen Zhou,
Balter James M.,
Lawrence Theodore S.,
Ten Haken Randall K.
Publication year - 2006
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.2219773
Subject(s) - voxel , magnetic resonance imaging , computer science , nonlinear system , liver perfusion , non linear least squares , medical imaging , contrast (vision) , nuclear medicine , perfusion , mathematics , artificial intelligence , algorithm , radiology , estimation theory , medicine , physics , quantum mechanics
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 ∼ 400 , which makes it practical for use in clinical trials.