< X, Y >  = < [x1; x2; ...; xn], [y1; y2; ...; yn] >,  
              = [x1; x2; ...; xn]' * [y1; y2; ...; yn],  
              = SUM_(i=1)^(n) xi * yi,   
              = (x1 * y1) + (x2 * y2) + ... + (xn * yn).
    (a + ib)' = (a - ib).
If A satisfies the following relation,
    < A * X, Y > = < X, AT * Y >,  
then,
    AT is transpose of A.
(1) 2D matrix
If A is defined as follow,
    A in R ^ (M, N),
then,
    AT in R ^ (N, M).
If A(x) is  defined as follow,
    A(x) = x(i+1) - x(i),
then AT(y) is that,
    AT(y) = y(i) - y(i+1).
If A(x) is Fourier transform,
    A(x) = fftn(x)/numel(x),
then AT(y) is Inverse Fourier transform,
    AT(y) = ifftn(y).
(4) Radon transform
If A(x) is Radon transform called by 'Projection',
    A(x) = radon(x, THETA)
    
    where, THETA is degrees vector.
then AT(y) is Inverse Radon transform without Filtration called by 'Backprojection',
    AT(y) = iradon(y, THETA, 'none', N)/(pi/(2*length(THETA))).
    
    where, 'none' is filtration option and N is image size.