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Cone-beam X-ray computed tomography (CT) is attracting increasing attention due to its applications in medicine, biomedical sciences, material engineering, and nondestructive industrial evaluation. Rapid volumetric image reconstruction is highly desirable in all these fields for prompt visualization and analysis of complex structures of interest. However, in most applications, volumetric image reconstruction is still a very demanding computational task. The cell broadband engine architecture (CBEA) is a novel microprocessor architecture designed to provide power-efficient and cost-effective high-performance processing for some of the world's most demanding applications, including next generation game consoles. Applications that show special promise of benefiting from CBEA are medical imaging, security and surveillance, digital media, entertainment, communications, and selected scientific workloads. We implemented 3-D CT image reconstruction on the CBEA. However, the programming scheme of CBEA is different from single-core architectures. To archive peak performance on CBEA, coding optimizations are needed by exploiting the unique features of the hardware. In this paper, we describe the parallelization of the 3-D image reconstruction algorithm on the CBEA. The results show that the CBEA can achieve significant run time savings.