Developments towards the slice-wise three-dimensional reconstruction of the distribution of the contrast perfusion in the myocardial muscle from biplane angiographic views.
In theory, radiographic myocardial perfusion imaging allows a quantitative assessment of the functional significance of a coronary stenosis. However, in the conventional two-dimensional projection images there does not exist a one-two-one relationship between a selected myocardial region of interest (ROI) and one particular coronary segment perfusing that area due to over-projection of myocardial regions in front of and behind the selected ROI perfused by other arterial segments, which may result in measurements which are difficult to interpret or even unreliable. To overcome these problems, we have developed two algorithms to determine the spatial distribution of perfusion levels in slices of the heart, selected approximately perpendicular to the left ventricular long axis, from two orthogonal angiographic views: the Segmental Reconstruction Technique (SRT) and the Network Programming Reconstruction Technique (NPRT). Both techniques require a priori geometric information about the myocardium, which can be obtained from the epicardial coronary tree (epicardial boundaries) and the left ventricular lumen (endocardial boundaries). Using the SRT approach, pie-shaped segments are defined for each slice within the myocardial geometric constraints such that superimposition of these segments when projected in orthogonal biplane views is minimal. The reconstruction process uses a model with identical myocardial geometry and definition of segments. Each segment of the model is assigned a relative perfusion level with unit one if no other a priori information is available. In this case, the model contains geometric information only. In case a priori information about expected segmental perfusion levels is available, a level between zero and one is assigned to each segment. The a priori information on the myocardial perfusion levels can be extracted from either anatomic information about the location and severity of existing coronary arterial obstructions, or from a slice adjacent to the one under reconstruction. Using the NPRT approach perfusion levels are computed for each volume picture element of a slice within the reconstructed myocardial geometry, thus resulting in a much higher spatial resolution than the SRT approach. A priori information of perfusion levels must be included in this approach, again based upon anatomical information, or upon the slice adjacent to the one under reconstruction. The very first slice of a myocardial study will be reconstructed by the SRT approach. Extensive computer simulations for the SRT have proved that the mean difference between the actual and reconstructed segmental perfusion levels, on a scale from 0 to 1, is smaller than 0.45 (SEE = 0.0033, REE = 1.80) for various coronary artery disease states without the use of a priori information on expected perfusion levels. This error becomes smaller than 0.36 (SEE = 0.0026, REE = 1.42), if a priori information in the reconstruction technique is included. Similar computer simulations for the NPRT have proved that these mean differences in geometric segments equal to those defined for the SRT, are smaller than 2.94 (SEE = 0.0308, REE = 0.77) on a scale from 0 to 16, without the use of a priori information on expected perfusion levels, and smaller than 1.72 (SEE = 0.0304, REE = 1.10) on the same scale when a priori information is included. Therefore, it may be concluded that slice-wise three-dimensional reconstruction of perfusion levels is feasible from biplane computer-simulated data, and that a similarity exists for mean perfusion levels in corresponding regions in the simulated and reconstructed slices, for various states of single coronary artery disease.
Dumay
A C
AC
Thoraxcenter, Erasmus University, Rotterdam, The Netherlands.
Zijdenbos
A P
AP
Pinto
I M
IM
Gerbrands
J J
JJ
Roos
C
C
Serruys
P W
PW
Reiber
J H
JH
D000465:Algorithms; D003326:Coronary Circulation; D006321:Heart / Q000033:anatomy & histology* / Q000502:physiology; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008955:Models, Cardiovascular