Large-field-of-view 3D localization microscopy by spatially variant point spread function generation

Accurate characterization of the microscopic point spread function (PSF) is crucial for high-performance localization microscopy (LM). Traditionally, LM assumes a spatially invariant PSF to simplify the modeling of the imaging system. However, for large field of view (FOV) imaging, it becomes important to account for the spatially variant nature of the PSF. Despite efforts in characterizing field dependence, such as interpolating Zernike-polynomial-based pupil functions, there is still a demand for an efficient and accurate method. In this work, we introduce a spatially variant 3D PSF generator (PPG3D), based on Principal Component Analysis, accompanied by a localizer for LM. Our simulations and experimental results demonstrate PPG3D’s effectiveness with various PSFs, including astigmatism, double-helix, tetrapod, etc. This enables, through single molecule localization microscopy, super-resolution imaging of mitochondria and microtubules with high fidelity over a large FOV. A comparison of PPG3D with three other PSF generators for 3D LM reveals a three-fold improvement in accuracy and operates approximately a hundred times faster.