Interpolation is used because of the virtual sources, if the sampling density which returns us the values is not high enough, it generates significant sidelobes in the point of spread, which results in a significant downgrade of the final image resolution.
This process cannot be overcome in any way (except by raising sampling density, which may not be possible) unless you use interpolation. This construct allows you to generate the intermediate missing points from the samples without increasing the sampling density. The interpolation which can be used in ultrasound application is an enormous variety with different outcomes with respect to the techniques chosen. The most popularly studied interpolation schemes can be grouped under five categories:
Nearest Neighbor (as shown in the application of apodization on valid samples selected)
1.Linear
2.Spline
3.Matched Filter
4.Polynomial
Definitely, the best choices (but at the cost of a high number of computational resources) are ‘Matched Filter’ and ‘Spline’. Linear Interpolation suffers from high sidelobes energy, resulting in a poor final contrast (which is essential for a good quality image in ultrasound beamforming). Polynomial interpolation (especially for high rank ones) suffers from Runge’s phenomena and thus it might yield some singular points. Thus in this case, (the Spline) is chosen to be suitable interpolation method for ultrasound imaging.