Accelerated iterative image reconstruction in three-dimensional optoacoustic tomography

2015 ◽  
Author(s):  
Fatima Anis ◽  
Kun Wang ◽  
Richard Su ◽  
Sergey A. Ermilov ◽  
Alexander A. Oraevsky ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Iterative Image Reconstruction ◽  
Optoacoustic Tomography

Evaluation of three-dimensional iterative image reconstruction in virtual monochromatic imaging at 40 kilo-electron volts: phantom and clinical studies to assess the image noise and image quality in comparison with other reconstruction techniques

2020 ◽  
Vol 93 (1110) ◽  
pp. 20190675
Author(s):  
Takuya Ishikawa ◽  
Shigeru Suzuki ◽  
Yoshiaki Katada ◽  
Tomoko Takayanagi ◽  
Rika Fukui ◽  
...  
Keyword(s):  
Clinical Study ◽  
Image Reconstruction ◽  
Image Quality ◽  
Three Dimensional ◽  
Image Noise ◽  
Reconstruction Technique ◽  
Iterative Image Reconstruction ◽  
Virtual Monochromatic Imaging ◽  
Monochromatic Imaging

Objective: The purpose of this study was to evaluate the image quality in virtual monochromatic imaging (VMI) at 40 kilo-electron volts (keV) with three-dimensional iterative image reconstruction (3D-IIR). Methods: A phantom study and clinical study (31 patients) were performed with dual-energy CT (DECT). VMI at 40 keV was obtained and the images were reconstructed using filtered back projection (FBP), 50% adaptive statistical iterative reconstruction (ASiR), and 3D-IIR. We conducted subjective and objective evaluations of the image quality with each reconstruction technique. Results: The image contrast-to-noise ratio and image noise in both the clinical and phantom studies were significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.05). The standard deviation and noise power spectra of the reconstructed images decreased in the order of 3D-IIR to 50% ASiR to FBP, while the modulation transfer function was maintained across the three reconstruction techniques. In most subjective evaluations in the clinical study, the image quality was significantly better with 3D-IIR than with 50% ASiR, and with 50% ASiR than with FBP (all, p < 0.001). Regarding the diagnostic acceptability, all images using 3D-IIR were evaluated as being fully or probably acceptable. Conclusions: The quality of VMI at 40 keV is improved by 3D-IIR, which allows the image noise to be reduced and structural details to be maintained. Advances in knowledge: The improvement of the image quality of VMI at 40 keV by 3D-IIR may increase the subjective acceptance in the clinical setting.

scholarly journals Evaluation of three-dimensional iterative image reconstruction in C-arm-based interventional cone-beam CT

Medicine ◽  
2019 ◽  
Vol 98 (13) ◽  
pp. e14947
Author(s):  
Shigeru Suzuki ◽  
Yoshiaki Katada ◽  
Tomoko Takayanagi ◽  
Haruto Sugawara ◽  
Takuya Ishikawa ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Three Dimensional ◽  
Cone Beam ◽  
Iterative Image Reconstruction

scholarly journals Accelerating image reconstruction in three-dimensional optoacoustic tomography on graphics processing units

2013 ◽  
Vol 40 (2) ◽  
pp. 023301 ◽  
Author(s):  
Kun Wang ◽  
Chao Huang ◽  
Yu-Jiun Kao ◽  
Cheng-Ying Chou ◽  
Alexander A. Oraevsky ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Graphics Processing Units ◽  
Three Dimensional ◽  
Optoacoustic Tomography ◽  
Graphics Processing

Three-dimensional photoacoustic imaging by sparse-array detection and iterative image reconstruction

2008 ◽  
Vol 13 (5) ◽  
pp. 054052 ◽  
Author(s):  
Pinhas Ephrat ◽  
Lynn Keenliside ◽  
Adam Seabrook ◽  
Frank S. Prato ◽  
Jeffrey J. L. Carson
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Imaging ◽  
Three Dimensional ◽  
Iterative Image Reconstruction ◽  
Sparse Array ◽  
Array Detection

Investigation of iterative image reconstruction in optoacoustic tomography

2012 ◽  
Author(s):  
Kun Wang ◽  
Richard Su ◽  
Alexander A. Oraevsky ◽  
Mark A. Anastasio
Keyword(s):  
Image Reconstruction ◽  
Iterative Image Reconstruction ◽  
Optoacoustic Tomography

Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

1974 ◽  
Vol 32 ◽  
pp. 330-331
Author(s):  
R. A. Crowther
Keyword(s):  
Image Reconstruction ◽  
Finite Number ◽  
Density Distribution ◽  
Electron Micrographs ◽  
Mathematical Problem ◽  
Three Dimensional ◽  
Ideal Case ◽  
Dimensional Image ◽  
General Object ◽  
The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

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