Medical imaging applications have growing processing requirements, and scalable multicomputers are needed to support these applications. Scalability -- performance speedup equal to the increased number of processors -- is necessary for a cost-effective multicomputer. We performed tests of performance and scalability on one through 16 processors on a RACE multicomputer using Parallel Application system (PAS) software. Data transfer and synchronization mechanisms introduced a minimum of overhead to the multicomputer's performance. We implemented magnetic resonance (MR) image reconstruction and multiplanar reformatting (MPR) algorithms, and demonstrated high scalability; the 16- processor configuration was 80% to 90% efficient, and the smaller configurations had higher efficiencies. Our experience is that PAS is a robust and high-productivity tool for developing scalable multicomputer applications.