This paper presents a parallelized algorithm for the deblurring of images obtained from Magnetic Resonance Imaging. Images generated by MRI may suffer a loss of clarity due to inhomogeneities in the magnetic field. One of the techniques for removing this blurring artifact is the demodulation of the data for each pixel of the image using the value of the magnetic field near that point in space. This method consists of acquiring a local field map, finding the best fit to a linear map and using it to deblur the image distortions due to local frequency variations. This is a very computation intensive operation and presents appreciable gains through parallelization. This paper presents the implementation of this algorithm using two popular message-passing platforms, PVM and MPI and discusses the pros and cons of each. A message-passing paradigm was chosen for parallelization since the target architecture is a network of workstations. Performance figures are presented for various problem and network sizes and future improvements are discussed.