Axial Tomography by filtered least squares

We actually start out with the back projection

H^Tb, the right hand side of equation 2, is a vector derived from the observations that is central feature of the least squares method. Noteworthy, in the present context, H^Tb assumes a special significance; it is in fact the back projection! This comes about because H is a projector matrix.