Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2024-02-28 eCollection Date: 2024-01-01 DOI:10.1155/2024/8862387
Kwangsung Oh, Piero R Bianco
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引用次数: 0

Abstract

Superresolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. The rate-limiting step in observing a superresolution image in SIM is often the reconstruction speed of the algorithm used to form a single image from as many as nine raw images. Reconstruction algorithms impose a significant computing burden due to an intricate workflow and a large number of often complex calculations to produce the final image. Further adding to the computing burden is that the code, even within the MATLAB environment, can be inefficiently written by microscopists who are noncomputer science researchers. In addition, they do not take into consideration the processing power of the graphics processing unit (GPU) of the computer. To address these issues, we present simple but efficient approaches to first revise MATLAB code, followed by conversion to GPU-optimized code. When combined with cost-effective, high-performance GPU-enabled computers, a 4- to 500-fold improvement in algorithm execution speed is observed as shown for the image denoising Hessian-SIM algorithm. Importantly, the improved algorithm produces images identical in quality to the original.

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将结构光照图像重构代码便捷地转换和优化到 GPU 环境中。
超分辨率结构照明显微镜(SIM)是一种理想的活细胞成像模式,因为它速度相对较快,对细胞造成的光子损伤较小。在 SIM 中观察超分辨率图像的速度极限通常是用于从多达九幅原始图像中生成单幅图像的算法的重建速度。重建算法需要复杂的工作流程和大量复杂的计算才能生成最终图像,这给计算带来了巨大的负担。进一步加重计算负担的是,即使是在 MATLAB 环境中,非计算机科学研究人员的显微镜专家编写的代码也可能效率低下。此外,他们也没有考虑到计算机图形处理器(GPU)的处理能力。为了解决这些问题,我们提出了简单而高效的方法,首先修改 MATLAB 代码,然后转换为 GPU 优化代码。如图像去噪 Hessian-SIM 算法所示,当与具有成本效益的高性能 GPU 计算机结合使用时,算法执行速度可提高 4 到 500 倍。重要的是,改进后的算法生成的图像质量与原始图像相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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