 Here we discuss a method for multidimensional FIR filter design via sum-of-squares formulations of spectral mask constraints.The sum-of-squares optimization problem is expressed as a semidefinite program with low-rank structure, by sampling the constraints using discrete sine and cosine transforms.The resulting semidefinite program is then solved by a customized primal-dual interior-point method that exploits low-rank structure.