Iterative reconstruction with correction of the spatially variant fan-beam collimator response in neurotransmission SPET imaging

AbstractIn this work we demonstrate customized depolarization spatial patterns by imaging a dynamical time-dependent pixelated retarder. A proof-of-concept of the proposed method is presented, where a liquid–crystal spatial light modulator is used as a spatial retarder that emulates a controlled spatially variant depolarizing sample by addressing a time-dependent phase pattern. We apply an imaging Mueller polarimetric system based on a polarization camera to verify the effective depolarization effect. Experimental validation is provided by temporal integration on the detection system. The effective depolarizance results are fully described within a simple graphical approach which agrees with standard Mueller matrix decomposition methods. The potential of the method is discussed by means of three practical cases, which include non-reported depolarization spatial patterns, including exotic structures as a spirally shaped depolarization pattern.

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Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

European Radiology ◽

10.1007/s00330-020-07668-x ◽

2021 ◽

Author(s):

Luuk J. Oostveen ◽

Frederick J. A. Meijer ◽

Frank de Lange ◽

Ewoud J. Smit ◽

Sjoert A. Pegge ◽

...

Keyword(s):

Deep Learning ◽

White Matter ◽

Image Quality ◽

Iterative Reconstruction ◽

Quality Characteristics ◽

Model Based ◽

Hybrid Iterative Reconstruction ◽

Contrast Ct ◽

Lower Noise ◽

Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

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Generation of terahertz vector beams using dielectric metasurfaces via spin-decoupled phase control

Nanophotonics ◽

10.1515/nanoph-2020-0112 ◽

2020 ◽

Vol 9 (10) ◽

pp. 3393-3402 ◽

Cited By ~ 3

Author(s):

Yuehong Xu ◽

Huifang Zhang ◽

Quan Li ◽

Xueqian Zhang ◽

Quan Xu ◽

...

Keyword(s):

High Performance ◽

Special Kind ◽

Phase Control ◽

High Resolution Imaging ◽

Plasmon Excitation ◽

Bessel Beams ◽

Dynamic Phase ◽

Vector Beams ◽

Resolution Imaging ◽

Spatially Variant

AbstractCylindrical vector beams (CVBs), being a special kind of beams with spatially variant states of polarizations, are promising in photonics applications, including high-resolution imaging, plasmon excitation, optical trapping, and laser machining. Recently, generating CVBs using metasurfaces has drawn enormous interest owing to their highly designable, multifunctional, and integratable features. However, related studies remain unexplored in the terahertz regime. Here, a generic method for efficiently generating terahertz CVBs carrying orbital angular momentums (OAMs) is proposed and experimentally demonstrated using transmission-type spatial-variant dielectric metasurfaces, which is realized by designing the interference between the two circularly polarized transmission components. This method is based on spin-decoupled phase control allowed by simultaneously manipulating the dynamic phase and geometric phase of each structure, endowing more degree of freedom in designing the vector beams. Two types of metasurfaces which respectively generate polarization-dependent terahertz vector vortex beams (VVBs) and vector Bessel beams (VBBs) are experimentally characterized. The proposed method opens a new window to generate versatile vector beams, providing new capabilities in developing novel, compact, and high-performance devices applicable to broad electromagnetic spectral regimes.

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