Motion Compensation for Industrial Computed Tomography

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Edward Angus ◽  
Yuntao An ◽  
Gary S. Schajer
Keyword(s):  
Computed Tomography ◽  
Density Distribution ◽  
Motion Control ◽  
Radon Transform ◽  
Motion Artifacts ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Object Rotation ◽  
Density Vector ◽  
Radiographic Data

X-ray computed tomography (CT) is a powerful tool for industrial inspection. However, the harsh conditions encountered in some production environments make accurate motion control difficult, leading to motion artifacts in CT applications. A technique is demonstrated that removes motion artifacts by using an iterative-solver CT reconstruction method that includes a bulk Radon transform shifting step to align radiographic data before reconstruction. The paper uses log scanning in a sawmill as an example application. We show how for a known nominal object density distribution (circular prismatic in the case of a log), the geometric center and radius of the log may be approximated from its radiographs and any motion compensated for. This may then be fed into a previously developed iterative reconstruction CT scheme based on a polar voxel geometry and useful for describing logs. The method is validated by taking the known density distribution of a physical phantom and producing synthetic radiographs in which the axis of object rotation does not coincide with the center of field of view for a hypothetical scanner geometry. Reconstructions could then be made on radiographs that had been corrected and compared to those that had not. This was done for progressively larger offsets between these two axes and the reduction in voxel density vector error studied. For CT applications in industrial settings in which precise motion control is impractical or too costly, radiographic data shifting and scaling based on predictive models for the Radon transform appears to be a simple but effective technique.

scholarly journals Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3941 ◽  
Author(s):  
Li ◽  
Cai ◽  
Wang ◽  
Zhang ◽  
Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Image Domain ◽  
Adversarial Network ◽  
Domain Loss ◽  
Limited Angle

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

Computed tomography reconstruction on distributed storage using hybrid regularization approach

2019 ◽  
Vol 33 (06) ◽  
pp. 1950063 ◽  
Author(s):  
Shailendra Tiwari ◽  
Kavkirat Kaur ◽  
Yadunath Pathak ◽  
Shivendraa Shivani ◽  
Kuldeep Kaur
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Distributed Storage ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Noise Robustness ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction

Computed Tomography (CT) is considered as a significant imaging tool for clinical diagnoses. Due to low-dose radiation in CT, the projection data is highly affected by Gaussian noise which may lead to blurred images, staircase effect, loss of basic fine structure and detailed information. Therefore, there is a demand for an approach that can eliminate noise and can provide high-quality images. To achieve this objective, this paper presents a new statistical image reconstruction method by proposing a suitable regularization approach. The proposed regularization is a hybrid approach of Complex Diffusion and Shock filter as a prior term. To handle the problem of prominent Gaussian noise as well as ill-posedness, the proposed hybrid regularization is further combined with the standard Maximum Likelihood Expectation Maximization (MLEM) reconstruction algorithm in an iterative manner and has been referred to as the proposed CT-Reconstruction (CT-R) algorithm here after. Besides, considering the large sizes of image data sets for medical imaging, distributed storage for images have been employed on Hadoop Distributed File System (HDFS) and the proposed MLEM algorithms have been deployed for improved performance.The proposed method has been evaluated on both the simulated and real test phantoms. The final results are compared with the other standard methods and it is observed that the proposed method has many desirable properties such as better noise robustness, less computational cost and enhanced denoising effect.

scholarly journals Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1122
Author(s):  
Jessica Graef ◽  
Bernd A. Leidel ◽  
Keno K. Bressem ◽  
Janis L. Vahldiek ◽  
Bernd Hamm ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiopulmonary Resuscitation ◽  
Chest Compression ◽  
Image Acquisition ◽  
Circulatory Arrest ◽  
Motion Artifacts ◽  
Current Standard ◽  
Ct Protocol ◽  
Life Threatening ◽  
Reconstruction Time

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

scholarly journals Using micro-computed tomography to reveal the anatomy of adult Diaphorina citri Kuwayama (Insecta: Hemiptera, Liviidae) and how it pierces and feeds within a citrus leaf

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javier Alba-Tercedor ◽  
Wayne B. Hunter ◽  
Ignacio Alba-Alejandre
Keyword(s):  
Computed Tomography ◽  
Significant Advance ◽  
Micro Ct ◽  
Diaphorina Citri ◽  
Ct Reconstruction ◽  
Internal Anatomy ◽  
Micro Computed Tomography ◽  
Citrus Leaf ◽  
Teaching Aids ◽  
Adult Feeding

AbstractThe Asian citrus psyllid (ACP), Diaphorina citri, is a harmful pest of citrus trees that transmits Candidatus Liberibacter spp. which causes Huanglongbing (HLB) (citrus greening disease); this is considered to be the most serious bacterial disease of citrus plants. Here we detail an anatomical study of the external and internal anatomy (excluding the reproductive system) using micro-computed tomography (micro-CT). This is the first complete 3D micro-CT reconstruction of the anatomy of a psylloid insect and includes a 3D reconstruction of an adult feeding on a citrus leaf that can be used on mobile devices. Detailed rendered images and videos support first descriptions of coxal and scapus antennal glands and sexual differences in the internal anatomy (hindgut rectum, mesothoracic ganglion and brain). This represents a significant advance in our knowledge of ACP anatomy, and of psyllids in general. Together the images, videos and 3D model constitute a unique anatomical atlas and are useful tools for future research and as teaching aids.

Dynamics of the right atrio-ventricular junction in human: a spatial high-resolution study with multiphase computed tomography

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Jouan ◽  
I Masari ◽  
V Bliah ◽  
G Soulat ◽  
D Craiem ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiac Cycle ◽  
Cardiac Computed Tomography ◽  
Reconstruction Method ◽  
Imaging Data ◽  
High Definition ◽  
Vertical Projection ◽  
Horizontal Projection ◽  
Cross Sectional ◽  
The Right

Abstract Introduction In order to improve knowledge of the tricuspid valve (TV) function and its coupling with the right atrio-ventricular junction (RAVJ) and right ventricle (RV), new four-dimensional high-definition imagery methods are mandatory (3D+t). Purpose Using an innovative reconstruction method based on multiphase cardiac computed tomography imaging (4D-MCCTI), we finely analyzed the morphological & dynamical features of tricuspid annulus (TA) and RAVJ components in order to assess new functional parameters of TV and RV functions. Methods Volume imaging data sets through time were obtained from 4D-MCCTI of 30 subjects (sex ratio 1, mean age 57±11y.) with no rhythm, valvular or ventricular abnormalities on echocardiography and implemented in a custom software for 3D semi-automated delineation of 18 points around TA perimeter. Coordinates of these points in each of the 10 time-phases within an RR interval were used to calculate specific geometrical features of TA such as 3D/2D areas, perimeters, 360°-diameters and vertical deformation. Subsequently, RV and Right Atrium (RA) inner contours were also delineated (Figure). Results TA shape was elliptical in horizontal projection with a mean eccentricity index (EcI) of 0.58±0.12; and saddle-shapped in vertical projection with a horn nearby the antero-septal commissure. This feature remained throughout the cardiac cycle but TA was more planar (minimal TA-height: 4.47±1.04 mm) and circular (minimal EcI=0.44±0.14) in mid-diastole when TA-3Darea and TA-3Dperimeter reached a maximum of 6.98±1.21 cm2/m2 and 7.41±0.91 cm, respectively. Correlation between TA-3Darea, TA-2Darea and latero-septal diameter (LSD) were R2=0.99 and R2=0.73, respectively. LSD was minimal in early-systole (18.83±3.04 mm/m2) and maximal in mid-diastole (20.04±3.05 mm/m2). Correlation of TA-3Darea with RV and RA cross-sectional areas were R2=0.82 and R2=0.71, respectively. Conversely, there was no significant correlation between TA, RV and RA concentric contractions. Conclusions Our method for 4D-MTCCI analysis has allowed confirming the shape and dynamics function of RAVJ throughout the cardiac cycle in healthy subjects, and giving new reference parameters for TV and RV evaluation. Software multiplanar view of TA Funding Acknowledgement Type of funding source: None

Motion Artifacts Detection from Computed Tomography Images

2020 ◽  
pp. 347-359
Author(s):  
Xiaoyu Sun ◽  
Feng Huang ◽  
Guanjun Lai ◽  
Dan Yu ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Motion Artifacts ◽  
Computed Tomography Images

scholarly journals Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography

2019 ◽  
Vol 10 (4) ◽  
pp. 1660 ◽  
Author(s):  
Fangyan Liu ◽  
Xiaojing Gong ◽  
Lihong V. Wang ◽  
Jingjing Guan ◽  
Liang Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Dictionary Learning ◽  
Sparse Sampling ◽  
Reconstruction Method

Projection-to-image transform frame: a lightweight block reconstruction network (LBRN) for computed tomography

2021 ◽  
Author(s):  
Genwei Ma ◽  
Xing Zhao ◽  
Yining Zhu ◽  
Huitao Zhang
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Real Data ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Back Projection ◽  
Image Block ◽  
Metal Artifacts ◽  
Reconstruction Process ◽  
Memory Space

Abstract To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which, respectively, correspond to the filter and back-projection of FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest (ROI), metal artifacts reduction and real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.

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