scholarly journals A CHEOPS white dwarf transit search

2021 ◽  
Vol 651 ◽  
pp. L12
Author(s):  
Brett M. Morris ◽  
Kevin Heng ◽  
Alexis Brandeker ◽  
Andrew Swan ◽  
Monika Lendl
Keyword(s):  
Stellar Evolution ◽  
Point Spread Function ◽  
White Dwarf ◽  
Metal Pollution ◽  
White Dwarfs ◽  
Point Spread ◽  
Spread Function ◽  
Data Products ◽  
Red Giant

White dwarf spectroscopy shows that nearly half of white dwarf atmospheres contain metals that must have been accreted from planetary material that survived the red giant phases of stellar evolution. We can use metal pollution in white dwarf atmospheres as flags, signalling recent accretion, in order to prioritize an efficient sample of white dwarfs to search for transiting material. We present a search for planetesimals orbiting six nearby white dwarfs with the CHaracterising ExOPlanet Satellite (CHEOPS). The targets are relatively faint for CHEOPS, 11 mag < G < 12.8 mag. We used aperture photometry data products from the CHEOPS mission as well as custom point-spread function photometry to search for periodic variations in flux due to transiting planetesimals. We detect no significant variations in flux that cannot be attributed to spacecraft systematics, despite reaching a photometric precision of < 2 ppt in 60 s exposures on each target. We simulate observations to show that the small survey is sensitive primarily to Moon-sized transiting objects with periods between 3 h < P < 10 h, with radii of R ≳ 1000 km.

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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