Highly Efficient and Scalable Framework for High-Speed Super-Resolution Microscopy

• Published in: IEEE access Vol. 9; pp. 97053 - 97067
• Main Authors: Do, Quan, Acuna, Sebastian, Kristiansen, Jon Ivar, Agarwal, Krishna, Ha, Phuong Hoai
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; C++ languages; Commodities; Computational nanoscopy; Electromagnetism, acoustics, optics: 434; Elektromagnetisme, akustikk, optikk: 434; Fluorescence; Fysikk: 430; image enhancement; Informasjons- og kommunikasjonsteknologi: 550; Information and communication technology: 550; Matematikk og Naturvitenskap: 400; Mathematics and natural science: 400; Matlab; Microscopy; Music; optimization; Parallel programming; Physics: 430; Python; Signal classification; super-resolution imaging; Superresolution; Technology: 500; Teknologi: 500; VDP; Windows
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3094840

The multiple signal classification algorithm (MUSICAL) is a statistical super-resolution technique for wide-field fluorescence microscopy. Although MUSICAL has several advantages, such as its high resolution, its low computational performance has limited its exploitation. This paper aims to analyze the performance and scalability of MUSICAL for improving its low computational performance. We first optimize MUSICAL for performance analysis by using the latest high-performance computing libraries and parallel programming techniques. Thereafter, we provide insights into MUSICAL's performance bottlenecks. Based on the insights, we develop a new parallel MUSICAL in C++ using Intel Threading Building Blocks and the Intel Math Kernel Library. Our experimental results show that our new parallel MUSICAL achieves a speed-up of up to 30.36x on a commodity machine with 32 cores with an efficiency of 94.88%. The experimental results also show that our new parallel MUSICAL outperforms the previous versions of MUSICAL in Matlab, Java, and Python by 30.43x, 2.63x, and 1.69x, respectively, on commodity machines.