Here, S is the spectral matrix, c B is the column-spanning exponential basis matrix, r B is the row- spanning exponential basis matrix, and N contains the noise. Two-dimensional Small Basis Parameterization is almost identical to the one-dimensional case. Fixedrate basis matrices, c B and r B , are constructed using unit intensity relaxation curves having geometrically incremented relaxation rates. The increments are chosen according to the resolution, and ranges are chosen to span the column and row spaces of the data matrix, just as with the one-dimensional case. Their inverses are used to find a least-squares solution to Equation (1.13) using standard methods. For Orthonormal Basis Parameterization, the procedure is the same except the basis matrices are orthonormalized first using SVD, just as done in the one-dimensional case. For both small basis and orthonormal basis cases, the result is a matrix of coefficients stored in the spectral matrix.