A practical approach for quantitative estimates of voxel-by-voxel liver perfusion using DCE imaging and a compartmental model.
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.
Cao
Yue
Y
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA.
Alspaugh
Jonathan
J
Shen
Zhou
Z
Balter
James M
JM
Lawrence
Theodore S
TS
Ten Haken
Randall K
RK
D000465:Algorithms; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging / Q000378:metabolism; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000378:metabolism*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
D003287:Contrast Media