Healthcare supply chain management: Towards 3D MRI aorta model with CPU-GPU parallel architecture for medical manufacture

Abstract The healthcare supply chain begins with the production of medical products. Different stages of supply chain flow may have their own objectives. The healthcare supply chain management process can be inefficient and fragmented because supply chain goals are not always matched within the medical requirements. To choose a certain product, healthcare organizations must consider a variety of demands and perspectives. The MRI team contributes a wealth of knowledge to the essential task of improving our clients’ supply chain and logistics performance. MRI is considered the most efficient medical device for the acquisition and treatment of medical imaging. Medical devices and manufacturing are classified as crucial factors for the supply chain in radiology. The objective of this paper is to present an approach that consists of modeling in 3D a segment of a stenosing aorta with a parallel treatment in order to determine the cost and the time of treatment for the reporting, which can be considered a promoter element to optimize the course of supply chain from manufacture to medical industry. Acceleration has sought to reduce the imaging speed in parallel architecture convergence for cardiac MRI. The image computation time is comparatively long owing to the iterative reconstruction process of 3D models. The aim of this paper is to suggest a CPU-GPU parallel architecture based on multicore to increase the speed of mesh generation in a 3D model of a stenosis aorta. A retrospective cardiac MRI scan with 74 series and 3057 images for a 10-year-old patient with congenital valve and valvular aortic stenosis on close MRI and coarctation (operated and dilated) in the sense of shone syndrome. The 3D mesh model was generated in Standard Tessellation Language (STL), as well as the libraries used to operate with Pymesh and Panda, and the time spent in tracing, decomposing, and finalizing the mesh crucially depends on the number of nuclei used in the parallel processing and the mesh quality chosen. A parallel processing based on four processors are required for the 3D shape refinement.To improve the efficiency of image processing algorithms and medical applications acquired in real-time analysis and control, a hybrid architecture (GPU/GPU) was proposed. The response time of parallel processing based on the CPU-GPU architecture used at the mesh level to achieve a 3D model is critically dependent on the number of kernels required.