scholarly journals An Approximate Cone Beam Reconstruction Algorithm for Gantry-Tilted CT Using Tangential Filtering

2006 ◽  
Vol 2006 ◽  
pp. 1-8
Author(s):  
Ming Yan ◽  
Cishen Zhang ◽  
Hongzhu Liang
Keyword(s):  
Image Reconstruction ◽  
Cone Angle ◽  
Three Dimensional ◽  
Ct Scanning ◽  
Reconstruction Algorithm ◽  
Tangential Direction ◽  
Approximate Algorithm ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Fdk Algorithm

FDK algorithm is a well-known 3D (three-dimensional) approximate algorithm for CT (computed tomography) image reconstruction and is also known to suffer from considerable artifacts when the scanning cone angle is large. Recently, it has been improved by performing the ramp filtering along the tangential direction of the X-ray source helix for dealing with the large cone angle problem. In this paper, we present an FDK-type approximate reconstruction algorithm for gantry-tilted CT imaging. The proposed method improves the image reconstruction by filtering the projection data along a proper direction which is determined by CT parameters and gantry-tilted angle. As a result, the proposed algorithm for gantry-tilted CT reconstruction can provide more scanning flexibilities in clinical CT scanning and is efficient in computation. The performance of the proposed algorithm is evaluated with turbell clock phantom and thorax phantom and compared with FDK algorithm and a popular 2D (two-dimensional) approximate algorithm. The results show that the proposed algorithm can achieve better image quality for gantry-tilted CT image reconstruction.

Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

2022 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Yunsong Zhao
Keyword(s):  
Image Reconstruction ◽  
Condition Number ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Edge Preserving ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

A new weighting scheme for arc based circle cone-beam CT reconstruction

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu
Keyword(s):  
Characteristic Function ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Characteristic Functions ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Scanning Angle ◽  
Beam Geometry

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

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.

Three Dimensional CT Reconstruction for the Evaluation and Surgical Planning of Facial Fractures

1986 ◽  
Vol 95 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Peter J. Koltai ◽  
Gary W. Wood
Keyword(s):  
Surgical Planning ◽  
Three Dimensional ◽  
Ct Scanning ◽  
Data Systems ◽  
Ct Reconstruction ◽  
Facial Fractures ◽  
Facial Skeleton ◽  
The Face ◽  
Spatial Imagination ◽  
The Relationship

Despite advances in radiology—including CT scanning—the three-dimensional (3D) nature of facial fractures must still be inferred by the spatial imagination of the physician. A computer system (Insight Phoenix Data Systems, Inc., Albany, N.Y.) uses CT studies as substrate for 3D reconstructions. We have used the insight computer for the evaluation and surgical planning of facial fractures of 16 patients with complex injuries. We present five illustrative cases, directly photographed from the computer monitor. Images can also be manipulated in real time by rotating or planar sectioning (functions best appreciated on video). The ability to cybernetically extract the facial skeleton from living subjects provides precise anatomic data previously unobtainable. The images are valuable for an accurate assessment of the relationship between the injured and uninjured sections of the face. We conclude that 3D reconstruction is an important advance in the treatment of facial fractures.

scholarly journals A CT Reconstruction Algorithm Based on Non-Aliasing Contourlet Transform and Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lu-zhen Deng ◽  
Peng Feng ◽  
Mian-yi Chen ◽  
Peng He ◽  
Quang-sang Vo ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Iteration Process ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Contourlet Transform ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Split Bregman

Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.

Three-dimensional transmitted brightfield imaging: pragmatic data collection and preprocessing considerations

1993 ◽  
Vol 51 ◽  
pp. 276-277
Author(s):  
J.A. Cooper ◽  
S. Bhattacharyya ◽  
J.N. Turner ◽  
T.J. Holmes
Keyword(s):  
Image Reconstruction ◽  
Data Collection ◽  
3D Visualization ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
3D Image ◽  
Serial Sectioning ◽  
Correlative Analysis ◽  
3D Image Reconstruction ◽  
Imaging Research

We have been developing algorithms for 3D image reconstruction of biological specimens with absorbing stains. This is important because there are many absorbing stains which are widely used in conjunction with transmitted light brightfield (TLB) microscopy, yet most of the 3D microscopic imaging research has been directed toward fluorescence microscopy. For instance, horseradish peroxidase (HRP) is used widely in the neurosciences for its many advantages as a tracer and intracellular marker. It is readily injected into individual neurons, transported long distances, and fills both the dendritic and axonal fields, while it may double as an electron microscopy stain for correlative analysis. With such advantages, it is clear that absorbing stains will continue to be widely used. Their utility will furthermore broaden with 3D visualization and quantitation.The main principles behind our methodology are the following. Standard optical serial sectioning data collection is used. The iterative, constrained image reconstruction algorithm is designed to reconstruct the 3D optical density distribution .

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