Research Project: Fiber Demeti ve Yapılandırılmış Aydınlatma Kullanan Yüksek Çözünürlüklü Milroendoskop
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Contributors
Funders
ID
TB.00443
Authors
Morova, Berna
Researcher
Publications
An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction
(Public Library of Science, 2022) Karalar, Elif Nur Fırat; Kiraz, Alper; Morova, Berna; Özgönül, Ekin; Uysallı, Yiğit; Aydın, Musa; Doğan, Buket; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Physics; Graduate School of Sciences and Engineering; KUTTAM (Koç University Research Center for Translational Medicine); School of Medicine; Yes; College of Engineering; College of Sciences; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center; SCHOOL OF MEDICINE
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.
An LED-based structured illumination microscope using a digital micromirror device and GPU-accelerated image reconstruction
(Public Library Science, 2022) Karalar, Elif Nur Fırat; Kiraz, Alper; Morova, Berna; Özgönül, Ekin; Tiryaki, Fatmanur; Uysallı, Yiğit; Aydin, Musa; Dogan, Buket; Department of Physics; Department of Molecular Biology and Genetics; KUTTAM (Koç University Research Center for Translational Medicine); Yes; College of Sciences; Research Center
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 x 1024 px images was achieved in 1.49 s using GPU computation, indicating an enhancement by similar to 28 and similar to 20 in computation time when compared with mono-thread CPU computation and multi-thread OpenMP CPU computation, respectively.
