scholarly journals Estimation of Laminar BOLD Activation Profiles using Deconvolution with a Physiological Point Spread Function

2020 ◽  
Author(s):  
Irati Markuerkiaga ◽  
José P. Marques ◽  
Tara E. Gallagher ◽  
David G. Norris
Keyword(s):  
Point Spread Function ◽  
Gold Standard ◽  
Neuronal Response ◽  
Signal Propagation ◽  
Cortical Layers ◽  
Point Spread ◽  
Spread Function ◽  
High Level ◽  
Laminar Specificity ◽  
Higher Sensitivity

AbstractBackgroundThe specificity of gradient echo (GE)-BOLD laminar fMRI activation profiles is degraded by intracortical veins that drain blood from lower to upper cortical layers, propagating activation signal in the same direction. This work describes an approach to obtain layer specific profiles by deconvolving the measured profiles with a physiological Point Spread Function (PSF).New MethodIt is shown that the PSF can be characterised by a TE-dependent peak to tail (p2t) value that is independent of cortical depth and can be estimated by simulation. An experimental estimation of individual p2t values and the sensitivity of the deconvolved profiles to variations in p2t is obtained using laminar data measured with a multi-echo 3D-FLASH sequence. These profiles are echo time dependent, but the underlying neuronal response is the same, allowing a data-based estimation of the PSF.ResultsThe deconvolved profiles are highly similar to the gold-standard obtained from extremely high resolution 3D-EPI data, for a range of p2t values of 5-9, which covers both the empirically determined value (7.1) and the value obtained by simulation (6.3).Comparison with Existing Method(s)Corrected profiles show a flatter shape across the cortex and a high level of similarity with the gold-standard, defined as a subset of profiles that are unaffected by intracortical veins.ConclusionsWe conclude that deconvolution is a robust approach for removing the effect of signal propagation through intracortical veins. This makes it possible to obtain profiles with high laminar specificity while benefitting from the higher sensitivity and efficiency of GE-BOLD sequences.

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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