Estimation of regional myocardial mass at risk based on distal arterial lumen volume and length using 3D micro-CT images

The determination of regional myocardial mass at risk distal to a coronary occlusion provides valuable prognostic information for a patient with coronary artery disease. The coronary arterial system follows a design rule which allows for the use of arterial branch length and lumen volume to estimate regional myocardial mass at risk. Image processing techniques, such as segmentation, skeletonization and arterial network tracking, are presented for extracting anatomical details of the coronary arterial system using micro-computed tomography (micro-CT). Moreover, a method of assigning tissue voxels to their corresponding arterial branches is presented to determine the dependent myocardial region. The proposed micro-CT technique was utilized to investigate the relationship between the sum of the distal coronary arterial branch lengths and volumes to the dependent regional myocardial mass using a polymer cast of a porcine heart. The correlations of the logarithm of the total distal arterial lengths (L) to the logarithm of the regional myocardial mass (M) for the left anterior descending (LAD), left circumflex (LCX) and right coronary (RCA) arteries were log(L)=0.73log(M)+0.09 (R=0.78), log(L)=0.82log(M)+0.05 (R=0.77) and log(L)=0.85log(M)+0.05 (R=0.87), respectively. The correlation of the logarithm of the total distal arterial lumen volumes (V) to the logarithm of the regional myocardial mass for the LAD, LCX and RCA were log(V)=0.93log(M)-1.65 (R=0.81), log(V)=1.02log(M)-1.79 (R=0.78) and log(V)=1.17log(M)-2.10 (R=0.82), respectively. These morphological relations did not change appreciably for diameter truncations of 600-1400microm. The results indicate that the image processing procedures successfully extracted information from a large 3D dataset of the coronary arterial tree to provide prognostic indications in the form of arterial tree parameters and anatomical area at risk.

[1]  Stanley R Sternberg,et al.  Grayscale morphology , 1986 .

[2]  P D Clayton,et al.  The effects of motion on quantitative vessel measurements. , 1985, Medical physics.

[3]  E. Ritman,et al.  Regional myocardial volume perfused by the coronary artery branch: estimation in vivo. , 1986, Circulation.

[4]  D. L. Pope,et al.  Three-dimensional reconstruction of moving arterial beds from digital subtraction angiography. , 1987, Computers and biomedical research, an international journal.

[5]  E L Ritman,et al.  Myocardial volume perfused by coronary artery branches. A three-dimensional X-ray computed tomographic evaluation in pigs. , 1992, Investigative radiology.

[6]  Claudio Napoli,et al.  Simvastatin Preserves the Structure of Coronary Adventitial Vasa Vasorum in Experimental Hypercholesterolemia Independent of Lipid Lowering , 2002, Circulation.

[7]  J. Lowe,et al.  Experimental infarct size as a function of the amount of myocardium at risk. , 1978, The American journal of pathology.

[8]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[9]  B. Ripley.  Tests of 'Randomness' for Spatial Point Patterns , 1979 .

[10]  J. Prothero,et al.  Scaling of blood parameters in mammals , 1980 .

[11]  K R Hoffmann,et al.  Determination of 3D imaging geometry and object configurations from two biplane views: an enhancement of the Metz-Fencil technique. , 1995, Medical physics.

[12]  Sabee Molloi,et al.  In vivo validation of the design rules of the coronary arteries and their application in the assessment of diffuse disease. , 2002, Physics in medicine and biology.

[13]  Attila Kuba,et al.  A Parallel 3D 12-Subiteration Thinning Algorithm , 1999, Graph. Model. Image Process..

[14]  Jiang Wu,et al.  Three-dimensional reconstruction of coronary arteries using more than two projections , 1990, Medical Imaging: Image Processing.

[15]  James H. Brown,et al.  A General Model for the Origin of Allometric Scaling Laws in Biology , 1997, Science.

[16]  E L Ritman,et al.  Three-dimensional microcomputed tomography of renal vasculature in rats. , 1998, Hypertension.

[17]  Diederick E Grobbee,et al.  Coronary Collaterals: An Important and Underexposed Aspect of Coronary Artery Disease , 2003, Circulation.

[18]  K Doi,et al.  Quantitative evaluation of vessel tracking techniques on coronary angiograms. , 1999, Medical physics.

[19]  Xiaoyi Jiang,et al.  Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Gunilla Borgefors,et al.  Distance transformations in digital images , 1986, Comput. Vis. Graph. Image Process..

[21]  Martin Neumann,et al.  Voronoi Polyhedra Analysis of Optimized Arterial Tree Models , 2003, Annals of Biomedical Engineering.

[22]  R. Jennings,et al.  Myocardial necrosis induced by temporary occlusion of a coronary artery in the dog. , 1960, Archives of pathology.

[23]  J. Bassingthwaighte,et al.  Fractal descriptions for spatial statistics , 2006, Annals of Biomedical Engineering.

[24]  Sung-Jea Ko,et al.  Center weighted median filters and their applications to image enhancement , 1991 .

[25]  Ghassan S. Kassab,et al.  Quantification of Coronary Artery Lumen Volume by Digital Angiography: In Vivo Validation , 2001, Circulation.

[26]  Mair Zamir,et al.  Accurate segmentation for quantitative analysis of vascular trees in 3D micro-CT images , 2002, SPIE Medical Imaging.

[27]  G S Kassab,et al.  On the design of the coronary arterial tree: a generalization of Murray's law , 1999 .

[28]  S Molloi,et al.  Absolute volumetric blood flow measurements using dual-energy digital subtraction angiography. , 1993, Medical physics.

[29]  E L Ritman,et al.  Altered Myocardial Microvascular 3D Architecture in Experimental Hypercholesterolemia , 2000, Circulation.

[30]  S M Jorgensen,et al.  3D architecture of myocardial microcirculation in intact rat heart: a study with micro-CT. , 1997, Advances in experimental medicine and biology.

[31]  C. Seiler,et al.  Basic structure-function relations of the epicardial coronary vascular tree. Basis of quantitative coronary arteriography for diffuse coronary artery disease. , 1991 .

[32]  W.E. Higgins,et al.  Extraction of the hepatic vasculature in rats using 3-D micro-CT images , 2000, IEEE Transactions on Medical Imaging.

[33]  Jong Beom Ra,et al.  A locally adaptive region growing algorithm for vascular segmentation , 2003, Int. J. Imaging Syst. Technol..

[34]  M. Marcus,et al.  Transmural variation in the relationship between myocardial infarct size and risk area. , 1982, The American journal of physiology.

[35]  D. Holdsworth,et al.  Micro-CT in small animal and specimen imaging , 2002 .

[36]  Roland T. Chin,et al.  Analysis of Thinning Algorithms Using Mathematical Morphology , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  G. Kassab.  Scaling laws of vascular trees: of form and function. , 2006, American journal of physiology. Heart and circulatory physiology.

[38]  W. R. Stahl,et al.  Scaling of respiratory variables in mammals. , 1967, Journal of applied physiology.

[39]  Andreas Wahle,et al.  Progression of coronary atherosclerosis quantified by analysis of 3-D reconstruction of left coronary arteries. , 2002, Atherosclerosis.

[40]  R E Ideker,et al.  Effect of coronary occlusion site on ischaemic bed size and collateral blood flow in dogs. , 1981, Cardiovascular research.

[41]  G S Kassab,et al.  Morphometry of pig coronary arterial trees. , 1993, The American journal of physiology.

[42]  William E. Higgins,et al.  Symmetric region growing , 2003, IEEE Trans. Image Process..

[43]  William E. Higgins,et al.  Multi-generational analysis and visualization of the vascular tree in 3D micro-CT images , 2002, Comput. Biol. Medicine.

[44]  E L Ritman,et al.  Microcomputed tomography of kidneys following chronic bile duct ligation. , 2000, Kidney international.

[45]  Ghassan S. Kassab,et al.  Large-Scale 3-D Geometric Reconstruction of the Porcine Coronary Arterial Vasculature Based on Detailed Anatomical Data , 2005, Annals of Biomedical Engineering.

[46]  R E Ideker,et al.  Myocardial Infarct Size and Location in Relation to the Coronary Vascular Bed at Risk in Man , 1981, Circulation.

[47]  C. Seiler,et al.  Measurement from arteriograms of regional myocardial bed size distal to any point in the coronary vascular tree for assessing anatomic area at risk. , 1993, Journal of the American College of Cardiology.

[48]  Jonathan Lessick,et al.  Multidetector-row computed tomography for noninvasive coronary imaging , 2006, Expert review of cardiovascular therapy.