Euclidean distance matrices have a very nice property! They have a very low rank which is independent of number of points generating them! This property allows us to complete distance matrices using low-rank matrix completion methods. We use this to calibrate ultrasound tomography devices in the presence of noise and several uncertainties. The emphasis of this paper is a specific device used mostly for breast cancer detection. We show how the process improves the performance of calibration with a set of simulations.