maximum flow constraints" are derived. The equation and constraints state the relationship between the volume of contrast medium in each block and the flow components" that are the volumes of contrast medium flowing from/to its adjacent blocks. The initial guess" that is a set of approximately obtained flow components is corrected using these relationships. The corrected flow components are then transformed into blood flow velocities, which are illustrated in the form of a needle diagram. In numerical experiments, the estimation error between the real flow velocity generated artificially and the flow velocity estimated with our algorithm was evaluated under one of the worst conditions. Although the maximum error was fairly large, the estimated flow velocity map was still acceptable for visual inspection of flow velocity pattern. We then applied our algorithm to an abdominal CAG (clinical data). The results showed flow stagnation and reverse flow in the abdominal aneurysm, which are consistent with the presence of a thrombus in the aneurysm. This algorithm may be a useful diagnostic tool in the assessment of vascular disease." />


Estimating the Two-Dimensional Blood Flow Velocity Map from Cineangiograms: Algorithm Using an Initial Guess and Its Application to an Abdominal Aneurysm

Naozo SUGIMOTO  Chikao UYAMA  Tetsuo SUGAHARA  Yoshio YANAGIHARA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.10   pp.1288-1297
Publication Date: 1993/10/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Medical Electronics and Medical Information
Keyword: 
image processing,  computer graphics and pattern recognition,  optical flow,  blood flow velocity,  cineangiogram,  

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Summary: 
To derive blood flow dynamics from cineangiograms (CAG), we have developed an image processing algorithm to estimate a two-dimensional blood fiow velocity map projected on CAG. Each image area of CAG is diveded into blocks, and it is assumed that the movement of the contrast medium between two serial frames is restricted only to adjacent blocks. By this assumption, a fundamental equation" and the maximum flow constraints" are derived. The equation and constraints state the relationship between the volume of contrast medium in each block and the flow components" that are the volumes of contrast medium flowing from/to its adjacent blocks. The initial guess" that is a set of approximately obtained flow components is corrected using these relationships. The corrected flow components are then transformed into blood flow velocities, which are illustrated in the form of a needle diagram. In numerical experiments, the estimation error between the real flow velocity generated artificially and the flow velocity estimated with our algorithm was evaluated under one of the worst conditions. Although the maximum error was fairly large, the estimated flow velocity map was still acceptable for visual inspection of flow velocity pattern. We then applied our algorithm to an abdominal CAG (clinical data). The results showed flow stagnation and reverse flow in the abdominal aneurysm, which are consistent with the presence of a thrombus in the aneurysm. This algorithm may be a useful diagnostic tool in the assessment of vascular disease.