A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm

2018 ◽  
Vol 26 (2) ◽  
pp. 189-208 ◽  
Author(s):  
Julia Mascolo-Fortin ◽  
Dmitri Matenine ◽  
Louis Archambault ◽  
Philippe Després
2012 ◽  
Vol 229-231 ◽  
pp. 1819-1822 ◽  
Author(s):  
Kui Dong Huang ◽  
Ding Hua Zhang ◽  
Ming Jun Li ◽  
Hua Zhang

In the three dimensional reconstruction of cone-beam CT, a fast reconstruction method based on the smallest three dimensional convex hull was proposed for actual scan data. First, according to the definition of the minimum three dimensional convex hull, the minimum three dimensional convex hull of the detected object was obtained in the actual scan using a segmentation algorithm based on the projected images, and then with the Z-line data first algorithm, the image reconstruction area was limited to the minimum three dimensional convex hull to enhance the cone-beam CT reconstruction speed by reducing the redundant computing. The experimental results show that this method can effectively reduce the memory consumption, and significantly improve the reconstruction speed and reduce the noise surrounding the object imaging area.


2014 ◽  
Vol 64 (12) ◽  
pp. 1907-1911
Author(s):  
Uikyu Je ◽  
Hyosung Cho ◽  
Minsik Lee ◽  
Jieun Oh ◽  
Yeonok Park ◽  
...  

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu

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.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 7104-7116 ◽  
Author(s):  
Xiubin Dai ◽  
Jianan Bai ◽  
Tianliang Liu ◽  
Lizhe Xie

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