Evaluation of z-axis resolution and image noise for nonconstant velocity spiral CT data reconstructed using a weighted 3D filtered backprojection (WFBP) reconstruction algorithm

2010 ◽  
Vol 37 (2) ◽  
pp. 897-906 ◽  
Author(s):  
Jodie A. Christner ◽  
Karl Stierstorfer ◽  
Andrew N. Primak ◽  
Christian D. Eusemann ◽  
Thomas G. Flohr ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Spiral Ct ◽  
Image Noise ◽  
Filtered Backprojection ◽  
Ct Data

scholarly journals Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader’s confidence

2019 ◽  
Vol 8 (6) ◽  
pp. 205846011985626
Author(s):  
Oliver S Grosser ◽  
Juri Ruf ◽  
Dennis Kupitz ◽  
Damian Czuczwara ◽  
David Loewenthal ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Bone Structure ◽  
Reconstruction Algorithm ◽  
Image Noise ◽  
Ct Reconstruction ◽  
Quality Image ◽  
Iterative Ct Reconstruction ◽  
Ct Data

Background Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT). Purpose To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment. Material and Methods The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures. Results The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP ( P ≤ 0.0001). While detectability increased in only 2/16 structures ( P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction ( P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced ( P = 0.003). Conclusion An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

Computerized Modeling Based on Spiral CT Data for Noninvasive Determination of Aortic Stent-Graft Length

2002 ◽  
Vol 9 (4) ◽  
pp. 520-528 ◽  
Author(s):  
Albert Rott ◽  
Thomas Boehm ◽  
Joachim Söldner ◽  
Jürgen R. Reichenbach ◽  
Jürgen Heyne ◽  
...  
Keyword(s):  
Stent Graft ◽  
Spiral Ct ◽  
Aortic Stent Graft ◽  
Ct Data ◽  
Graft Length

scholarly journals Multislice Spiral Computed Tomography under Hybrid Iterative Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer and Ceramide Glycosylation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jun Liu ◽  
Xiaolong Jiang
Keyword(s):  
Optical Density ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithm ◽  
Spiral Ct ◽  
Glucosylceramide Synthase ◽  
Multislice Spiral Ct ◽  
Iterative Reconstruction Algorithm ◽  
Hybrid Iterative Reconstruction ◽  
Group 2 ◽  
Group 1

This study was to discuss the application of multislice spiral computed tomography (CT) in the staging diagnosis of bladder cancer and the effect of ceramide glycosylation. The hybrid iterative reconstruction algorithm was applied. Immunohistochemistry and western blot were used to detect the normal bladder tissues (30 cases) of GCS in group 1 (100 cases) and group 2. The scanned images of all the research objects were obtained, the images with the iterative reconstruction algorithm were reconstructed, and statistical analysis on the CT value under the algorithm was conducted. The results showed that the image quality, blood vessel sharpness, average image score, signal-to-noise ratio, and radiation dose after the spiral CT and iterative reconstruction algorithm all increased, while the noise value decreased. The optical density value of glucosylceramide synthase in group 2 patients increased by 71%, and the optical density value of group 1 increased by 29%. The optical density expression of glucosylceramide synthase in group 1 patients was significantly higher than that in the control group, and there was a statistical difference between the two ( P < 0.05 ). Among the results of multislice spiral CT for tumor staging, the lesions larger than 5 cm and in the range of 1.1–2 cm in diameter were more sensitive. In 41 patients, there were multiple lesions. A total of 142 cancer lesions were found. The diameter of the tissue ranged from 0.5 to 6.8 cm, with an average diameter of 2.03 ± 0.35 cm. The optical density of glucosylceramide synthase in the group 1 was 5526, and the optical density in group 2 was 2576. The OD expression of GCS in group 1 was greatly higher in contrast to that in group 2, and there was a statistical difference between the two groups ( P < 0.05 ). The multislice spiral CT examination under this algorithm found that the diagnosis and staging accuracy of lesions with a diameter greater than 5 cm and tumor diameters in the range of 1.1 to 2 cm was higher. The image processed by the hybrid iterative reconstruction algorithm had good effect, high definition, and accuracy.

scholarly journals Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ayman El-Baz ◽  
Ahmed Elnakib ◽  
Mohamed Abou El-Ghar ◽  
Georgy Gimel'farb ◽  
Robert Falk ◽  
...  
Keyword(s):  
Template Matching ◽  
Automatic Detection ◽  
Spiral Ct ◽  
Lung Nodules ◽  
Data Sets ◽  
Genetic Optimization ◽  
Anatomical Structures ◽  
Lung Abnormalities ◽  
Ct Data ◽  
2D And 3D

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

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