scholarly journals Iterative reconstruction of projection images from a microlens-based optical detector

2011 ◽  
Vol 19 (13) ◽  
pp. 11932 ◽  
Author(s):  
Liji Cao ◽  
Jörg Peter
Keyword(s):  
Iterative Reconstruction ◽  
Optical Detector ◽  
Projection Images

Imaging of platinum-shadowed macromolecular complexes in dark field

1984 ◽  
Vol 42 ◽  
pp. 146-147
Author(s):  
D.W. Andrews ◽  
F.P. Ottensmeyer
Keyword(s):  
Thin Film ◽  
Three Dimensional ◽  
Average Diameter ◽  
Dark Field ◽  
Bright Field ◽  
Field Mode ◽  
Macromolecular Complexes ◽  
Average Mass ◽  
Topological Features ◽  
Projection Images

Shadowing with heavy metals has been used for many years to enhance the topological features of biological macromolecular complexes. The three dimensional features present in directionaly shadowed specimens often simplifies interpretation of projection images provided by other techniques. One difficulty with the method is the relatively large amount of metal used to achieve sufficient contrast in bright field images. Thick shadow films are undesirable because they decrease resolution due to an increased tendency for microcrystalline aggregates to form, because decoration artefacts become more severe and increased cap thickness makes estimation of dimensions more uncertain.The large increase in contrast provided by the dark field mode of imaging allows the use of shadow replicas with a much lower average mass thickness. To form the images in Fig. 1, latex spheres of 0.087 μ average diameter were unidirectionally shadowed with platinum carbon (Pt-C) and a thin film of carbon was indirectly evaporated on the specimen as a support.

Semi-automated methods for 3-D reconstruction of macromolecular complexes

1995 ◽  
Vol 53 ◽  
pp. 42-43
Author(s):  
B. Carragher ◽  
M. Whittaker
Keyword(s):  
Three Dimensional ◽  
Time Saving ◽  
Macromolecular Complexes ◽  
Symmetry Properties ◽  
Dimensional Reconstruction ◽  
Experienced Operator ◽  
Projection Images ◽  
The Individual ◽  
Natural Symmetry

Techniques for three-dimensional reconstruction of macromolecular complexes from electron micrographs have been successfully used for many years. These include methods which take advantage of the natural symmetry properties of the structure (for example helical or icosahedral) as well as those that use single axis or other tilting geometries to reconstruct from a set of projection images. These techniques have traditionally relied on a very experienced operator to manually perform the often numerous and time consuming steps required to obtain the final reconstruction. While the guidance and oversight of an experienced and critical operator will always be an essential component of these techniques, recent advances in computer technology, microprocessor controlled microscopes and the availability of high quality CCD cameras have provided the means to automate many of the individual steps.During the acquisition of data automation provides benefits not only in terms of convenience and time saving but also in circumstances where manual procedures limit the quality of the final reconstruction.

scholarly journals Influence of iterative reconstruction and dose levels on metallic artifact reduction: A phantom study within four CT systems

2019 ◽  
Vol 100 (5) ◽  
pp. 269-277 ◽  
Author(s):  
J. Greffier ◽  
A. Larbi ◽  
J. Frandon ◽  
P.A. Daviau ◽  
J.P. Beregi ◽  
...  
Keyword(s):  
Iterative Reconstruction ◽  
Phantom Study ◽  
Artifact Reduction ◽  
Metallic Artifact ◽  
Dose Levels

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

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.

scholarly journals Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3652
Author(s):  
Cory Juntunen ◽  
Isabel M. Woller ◽  
Yongjin Sung
Keyword(s):  
Fourier Transform ◽  
3D Imaging ◽  
Optical Tomography ◽  
Three Dimensional ◽  
High Spectral Resolution ◽  
Functional Information ◽  
Fluorescent Beads ◽  
Different Depths ◽  
Projection Images ◽  
Imaging Performance

Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.

scholarly journals Physics‐Based Iterative Reconstruction for Dual Source and Flying Focal Spot Computed Tomography

2021 ◽  
Author(s):  
Xiao Wang ◽  
Robert D. MacDougall ◽  
Peng Chen ◽  
Charles A. Bouman ◽  
Simon K. Warfield
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Focal Spot ◽  
Dual Source
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