Head motion correction based on filtered backprojection for x-ray CT imaging

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

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Noise-resolution tradeoffs in x-ray CT imaging: A comparison of penalized alternating minimization and filtered backprojection algorithms

Medical Physics ◽

10.1118/1.3549757 ◽

2011 ◽

Vol 38 (3) ◽

pp. 1444-1458 ◽

Cited By ~ 36

Author(s):

Joshua D. Evans ◽

David G. Politte ◽

Bruce R. Whiting ◽

Joseph A. O'Sullivan ◽

Jeffrey F. Williamson

Keyword(s):

Ct Imaging ◽

Alternating Minimization ◽

Filtered Backprojection ◽

X Ray

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A filtered backprojection algorithm for axial head motion correction in fan-beam SPECT

Physics in Medicine and Biology ◽

10.1088/0031-9155/40/12/004 ◽

1995 ◽

Vol 40 (12) ◽

pp. 2053-2063 ◽

Cited By ~ 10

Author(s):

Jianying Li ◽

R J Jaszczak ◽

R E Coleman

Keyword(s):

Motion Correction ◽

Head Motion ◽

Filtered Backprojection

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Effect of contrast magnitude and resolution metric on noise-resolution tradeoffs in x-ray CT imaging: a comparison of non-quadratic penalized alternating minimization and filtered backprojection algorithms

10.1117/12.876701 ◽

2011 ◽

Cited By ~ 5

Author(s):

Joshua D. Evans ◽

David G. Politte ◽

Bruce R. Whiting ◽

Joseph A. O'Sullivan ◽

Jeffrey F. Williamson

Keyword(s):

Ct Imaging ◽

Alternating Minimization ◽

Filtered Backprojection ◽

X Ray

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Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2019.2953974 ◽

2020 ◽

Vol 39 (5) ◽

pp. 1636-1645 ◽

Cited By ~ 1

Author(s):

Seokhwan Jang ◽

Seungeon Kim ◽

Mina Kim ◽

Kihong Son ◽

Kyoung-Yong Lee ◽

...

Keyword(s):

Motion Correction ◽

Helical Ct ◽

Ct Scanning ◽

Head Motion ◽

Filtered Backprojection

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

MRS Proceedings ◽

10.1557/proc-503-297 ◽

1997 ◽

Vol 503 ◽

Cited By ~ 1

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.

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Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽

10.3390/en14154595 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4595

Author(s):

Parisa Asadi ◽

Lauren E. Beckingham

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Random Forest ◽

Ct Images ◽

Ct Imaging ◽

Learning Method ◽

Learning Methods ◽

X Ray ◽

Machine Learning Methods ◽

Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

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