Wang Yina, Quan Tingwei, Zeng Shaoqun, Huang Zhen-Li
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.
Opt Express. 2012 Jul 2;20(14):16039-49. doi: 10.1364/OE.20.016039.
Developing methods for high-density localization of multiple emitters is a promising approach for enhancing the temporal resolution of localization microscopy while maintaining a desired spatial resolution, but the widespread use of this approach is thus far mainly obstructed by the slow image analysis speed. Here we present a high-density localization method based on the combination of Graphics Processing Unit (GPU) parallel computation, multiple-emitter fitting, and model recommendation via Bayesian Information Criterion (BIC). This method, called PALMER, exhibits satisfactory localization accuracy comparable with the previous reported SSM_BIC method, while executes more than two orders of magnitudes faster. Meanwhile, compared to the conventional localization microscopy which is based on sparse emitter localization, high-density localization microscopy based the PALMER method allows a speed gain of up to ~14-fold in obtaining a super-resolution image with the same Nyquist resolution.
开发用于多个发射器高密度定位的方法是一种很有前景的方法，可在保持所需空间分辨率的同时提高定位显微镜的时间分辨率，但迄今为止，这种方法的广泛应用主要受到图像分析速度缓慢的阻碍。在此，我们提出一种基于图形处理单元（GPU）并行计算、多发射器拟合以及通过贝叶斯信息准则（BIC）进行模型推荐相结合的高密度定位方法。这种方法称为PALMER，其定位精度与先前报道的SSM_BIC方法相当，令人满意，同时执行速度快两个数量级以上。此外，与基于稀疏发射器定位的传统定位显微镜相比，基于PALMER方法的高密度定位显微镜在获得具有相同奈奎斯特分辨率的超分辨率图像时，速度提升可达约14倍。
