Introduction: Myocardial SPECT imaging is usually performed acquiring 32 views in 180 degree with equal steps of 5.625 degrees. Acquiring more images requires spending more time or injection of more activity to the patients. An idea to improve the quality reconstructed images without acquiring extra images is producing the extra images interpolating the data between adjacent PROJECTIONs. The aim of present study was investigation the feasibility of this idea.Methods: Obviously such investigation cannot be performed on real patient's data. Therefore, data were simulated using NCAT digital phantom and SimSET Monte Carlo code. The imaging was performed as usual, acquiring 32 views from right anterior oblique to left posterior oblique. The data were interpolated to construct 5 images between adjacent PROJECTIONs convert it into 187 PROJECTIONs. The simulation was performed again acquiring 187 images as the reference. The conventional, interpolated and reference data set were reconstructed and compared for improvement and degradation in quality of final images. The above procedure was repeated for phantoms representing different types of heart disease, different cardiac size and different count densities.Results: The results showed that Hermit INTERPOLATION technique produces better quality images comparing to other INTERPOLATION methods tested. Results also confirmed that streak artifacts decreases, signal to noise ratio and contrast increased due to increasing the number of samples.Conclusion: These results indicate that the physical properties of reconstructed images improve significantly.This directly must improve the lesions delectability of images. However the matter is still under investigation.

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In this paper we consider multivariate Lagrange mean-value INTERPOLATION problem, where INTERPOLATION parameters are integrals over spheres. We have concentric spheres. Indeed, we consider the problem in three variables when it is not correct.

Sometimes observations in statistical analysis are not independent and typically are correlated with their spatial locations. In spatial statistics there are some methods to interpolate this kind of data. In this paper some of interpolators for spatial data will be studied. Then they will be evaluated and compared by means of mean square errors criteria. Finally it will be shown that kriging acts better than other methods such as spline, inverse distance, for INTERPOLATION of spatial data.