Applications of Parallel Data Processing for Biomedical Imaging

Abstract

This Chapter explores the use of parallel data processing in biomedical imaging to improve diagnostic performance, reduce processing times, and enable prompt decision-making in clinical settings. It examines imaging modalities like CT, MRI, ... etc and how parallel data processing algorithms enable quick reconstruction of high-resolution pictures. Parallel computing architectures like GPUs and multi-core CPUs are used to increase computational efficiency. Cluster computing and distributed computing are considered scalable solutions for large-scale biomedical imaging datasets. Parallelized adaptive algorithms speed up convergence of iterative reconstruction techniques, at the same time as parallel noise reduction techniques beautify photograph first-rate. The research concludes that parallel information processing is crucial for making better studies and patient care within the field of biomedical imaging and offers significant benefits in terms of speed, efficiency, and the capacity to handle large datasets.