An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction

Aydin M., Uysalli Y., Özgönül E., Morova B., Tiryaki F., Firat-Karalar E. N., ...Daha Fazla

PLoS ONE, cilt.17, sa.9 September, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 9 September
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1371/journal.pone.0273990
  • Dergi Adı: PLoS ONE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE, Food Science & Technology Abstracts, Index Islamicus, Linguistic Bibliography, MEDLINE, Pollution Abstracts, Psycinfo, zbMATH, Directory of Open Access Journals
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 Aydin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.When combined with computational approaches, fluorescence imaging becomes one of the most powerful tools in biomedical research. It is possible to achieve resolution figures beyond the diffraction limit, and improve the performance and flexibility of high-resolution imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based superresolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns with various orientations and lateral phase shifts generated using a digital micromirror device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on images without developing any CUDA kernel code is presented. The developed image reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 × 1024 px images was achieved in 1.49 s using GPU computation, indicating an enhancement by*28 and*20 in computation time when compared with mono-thread CPU computation and multi-thread OpenMP CPU computation, respectively.