Research on the Point Spread Function of Microscope Based on the Zernike Polynomials

2009 ◽  
Vol 29 (1) ◽  
pp. 169-175
Author(s):  
唐玉科 Tang Yuke ◽  
何小海 He Xiaohai ◽  
陶青川 Tao Qingchuan
Keyword(s):  
Point Spread Function ◽  
Zernike Polynomials ◽  
Point Spread ◽  
Spread Function

scholarly journals Wavefront Parameters Recovering by Using Point Spread Function

2020 ◽  
pp. short47-1-short47-7
Author(s):  
Olga Kalinkina ◽  
Tatyana Ivanova ◽  
Julia Kushtyseva
Keyword(s):  
Optical System ◽  
Point Spread Function ◽  
Optical Systems ◽  
Adjustment Process ◽  
Substantial Part ◽  
Zernike Polynomials ◽  
Reconstruction Algorithms ◽  
Point Spread ◽  
Spread Function

At various stages of the life cycle of optical systems, one of the most important tasks is quality of optical system elements assembly and alignment control. The different wavefront reconstruction algorithms have already proven themselves to be excellent assistants in this. Every year increasing technical capacities opens access to the new algorithms and the possibilities of their application. The paper considers an iterative algorithm for recovering the wavefront parameters. The parameters of the wavefront are the Zernike polynomials coefficients. The method involves using a previously known point spread function to recover Zernike polynomials coefficients. This work is devoted to the research of the defocusing influence on the convergence of the algorithm. The method is designed to control the manufacturing quality of optical systems by point image. A substantial part of the optical systems can use this method without additional equipment. It can help automate the controlled optical system adjustment process.

Point spread function based feature optimization and fusion for underwater objet recognition

2013 ◽  
Vol 26 (11) ◽  
pp. 944-952 ◽  
Author(s):  
Huibin Wang ◽  
Rong Zhang ◽  
Zhe Chen ◽  
Lizhong Xu ◽  
Jie Shen
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Feature Optimization

Determination of the Point Spread Function of a Computer-Generated Lens Formed by a Phase Light Modulator

2020 ◽  
Vol 128 (7) ◽  
pp. 1036-1040 ◽  
Author(s):  
N. G. Stsepuro ◽  
G. K. Krasin ◽  
M. S. Kovalev ◽  
V. N. Pestereva
Keyword(s):  
Point Spread Function ◽  
Light Modulator ◽  
Point Spread ◽  
Spread Function

Accurate point spread function (PSF) estimation for coded aperture cameras

2014 ◽  
Author(s):  
Jingyu Yang ◽  
Bin Jiang ◽  
Jinlong Ma ◽  
Yi Sun ◽  
Ming Di
Keyword(s):  
Point Spread Function ◽  
Coded Aperture ◽  
Point Spread ◽  
Spread Function ◽  
Psf Estimation

Aberration retrieval from the intensity point-spread function in the focal region using the extended Nijboer–Zernike approach

2005 ◽  
Vol 52 (12) ◽  
pp. 1695-1728 ◽  
Author(s):  
C. Van der Avoort * ◽  
J. J. M. Braat ◽  
P. Dirksen ◽  
A. J. E. M. Janssen
Keyword(s):  
Point Spread Function ◽  
Focal Region ◽  
Point Spread ◽  
Spread Function

scholarly journals Effect of Point-Spread Function Reconstruction for Indeterminate PSMA-RADS-3A Lesions on PSMA-Targeted PET Imaging of Men with Prostate Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 665
Author(s):  
Wajahat Khatri ◽  
Hyun Woo Chung ◽  
Rudolf A. Werner ◽  
Jeffrey P. Leal ◽  
Kenneth J. Pienta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Soft Tissue ◽  
Point Spread Function ◽  
Prospective Trial ◽  
Blood Pool ◽  
True Positive ◽  
Point Spread ◽  
Standardized Uptake Values ◽  
Spread Function

Purpose: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is emerging as an important modality for imaging patients with prostate cancer (PCa). As with any imaging modality, indeterminate findings will arise. The PSMA reporting and data system (PSMA-RADS) version 1.0 codifies indeterminate soft tissue findings with the PSMA-RADS-3A moniker. We investigated the role of point-spread function (PSF) reconstructions on categorization of PSMA-RADS-3A lesions. Methods: This was a post hoc analysis of an institutional review board approved prospective trial. Around 60 min after the administration of 333 MBq (9 mCi) of PSMA-targeted 18F-DCFPyL, patients underwent PET/computed tomography (CT) acquisitions from the mid-thighs to the skull vertex. The PET data were reconstructed with and without PSF. Scans were categorized according to PSMA-RADS version 1.0, and all PSMA-RADS-3A lesions on non-PSF images were re-evaluated to determine if any could be re-categorized as PSMA-RADS-4. The maximum standardized uptake values (SUVs) of the lesions, mean SUVs of blood pool, and the ratios of those values were determined. Results: A total of 171 PSMA-RADS-3A lesions were identified in 30 patients for whom both PSF reconstructions and cross-sectional imaging follow-up were available. A total of 13/171 (7.6%) were re-categorized as PSMA-RADS-4 lesions with PSF reconstructions. A total of 112/171 (65.5%) were found on follow-up to be true positive for PCa, with all 13 of the re-categorized lesions being true positive on follow-up. The lesions that were re-categorized trended towards having higher SUVmax-lesion and SUVmax-lesion/SUVmean-blood-pool metrics, although these relationships were not statistically significant. Conclusions: The use of PSF reconstructions for 18F-DCFPyL PET can allow the appropriate re-categorization of a small number of indeterminate PSMA-RADS-3A soft tissue lesions as more definitive PSMA-RADS-4 lesions. The routine use of PSF reconstructions for PSMA-targeted PET may be of value at those sites that utilize this technology.

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