Hepatic tumor enhancement in computed tomography: combined models of liver perfusion and dynamic imaging

The objective of this study is to show how computational modeling can be used to increase our understanding of liver enhancement in dynamic computer tomography. It relies on two models: (1). a vascular model, based on physiological rules, is used to generate the 3D hepatic vascular network; (2). the physical process of CT acquisition allows to synthesize timed-stamped series of images, aimed at tracking the propagation of a contrast material through the vessel network and the parenchyma. The coupled models are used to simulate the enhancement of a hyper-vascular tumor at different acquisition times, showing a maximum conspicuity during the arterial phase.

[1]  W D Foley,et al.  Dynamic hepatic CT. , 1989, Radiology.

[2]  C. Reinhold,et al.  Helical CT of the liver: value of an early hepatic arterial phase. , 1995, Radiology.

[3]  Russell Ross An Introduction to Vascular Biology , 1998, Nature Medicine.

[4]  David L. Cohn,et al.  OPTIMAL SYSTEMS: I. THE VASCULAR SYSTEM , 1954 .

[5]  R C Nelson,et al.  Contrast-enhanced spiral CT of the liver: effect of different amounts and injection rates of contrast material on early contrast enhancement. , 1994, AJR. American journal of roentgenology.

[6]  R C Nelson,et al.  Timing of parenchymal enhancement on dual-phase dynamic helical CT of the liver: how long does the hepatic arterial phase predominate? , 1996, AJR. American journal of roentgenology.

[7]  C Cherniak,et al.  Modeling the large-scale geometry of human coronary arteries. , 2000, Canadian journal of physiology and pharmacology.

[8]  K. H. Lee,et al.  Prediction of optimal injection protocol for tumor detection in contrast-enhanced dynamic hepatic CT using simulation of lesion-to-liver contrast difference. , 2000, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[9]  David Blackwell,et al.  On Optimal Systems , 1954 .

[10]  M. Zamir On fractal properties of arterial trees. , 1999, Journal of theoretical biology.

[11]  J A Brink,et al.  Aortic and hepatic contrast medium enhancement at CT. Part II. Effect of reduced cardiac output in a porcine model. , 1998, Radiology.

[12]  J A Brink,et al.  Aortic and hepatic contrast medium enhancement at CT. Part I. Prediction with a computer model. , 1998, Radiology.

[13]  T Togawa,et al.  Optimal branching structure of the vascular tree. , 1972, The Bulletin of mathematical biophysics.

[14]  S Aharinejad,et al.  The influence of optimization target selection on the structure of arterial tree models generated by constrained constructive optimization , 1995, The Journal of general physiology.

[15]  W Schreiner,et al.  A three-dimensional model for arterial tree representation, generated by constrained constructive optimization , 1999, Comput. Biol. Medicine.

[16]  D A Bluemke,et al.  Spiral CT of the liver. , 1993, AJR. American journal of roentgenology.

[17]  Jean-Louis Coatrieux,et al.  Fast algorithm for 3-D vascular tree modeling , 2003, Comput. Methods Programs Biomed..

[18]  R. Baron,et al.  Understanding and optimizing use of contrast material for CT of the liver. , 1994, AJR. American journal of roentgenology.

[19]  Marek Kretowski,et al.  Toward a better understanding of texture in vascular CT scan simulated images , 2001, IEEE Transactions on Biomedical Engineering.

[20]  Johanne Bezy-Wendling,et al.  A 3D DYNAMIC MODEL OF VASCULAR TREES , 1999 .

[21]  B. V. Van Beers,et al.  Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model. , 2000, Clinical science.

[22]  G. Dodd,et al.  Investigation of contrast enhancement in CT of the liver: the need for improved methods. , 1993, AJR. American journal of roentgenology.

[23]  M M Walkey,et al.  Dynamic hepatic CT: how many years will it take 'til we learn? , 1991, Radiology.