Статті в журналах: "Limited-Angle reconstruction"

1

Jaffe, J. S. "Limited angle reconstruction using stabilized algorithms." IEEE Transactions on Medical Imaging 9, no. 3 (1990): 338–44. http://dx.doi.org/10.1109/42.57772.

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2

Reeds, J. A., and L. A. Shepp. "Limited Angle Reconstruction in Tomography via Squashing." IEEE Transactions on Medical Imaging 6, no. 2 (June 1987): 89–97. http://dx.doi.org/10.1109/tmi.1987.4307808.

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3

Kulikajevas, Audrius, Rytis Maskeliūnas, Robertas Damaševičius, and Marta Wlodarczyk-Sielicka. "Auto-Refining Reconstruction Algorithm for Recreation of Limited Angle Humanoid Depth Data." Sensors 21, no. 11 (May 26, 2021): 3702. http://dx.doi.org/10.3390/s21113702.

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With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solve this, we propose a novel three-staged deep adversarial neural network architecture capable of denoising and refining real-world depth sensor input for full human body posture reconstruction. The proposed network has achieved Earth Mover and Chamfer distances of 0.059 and 0.079 on synthetic datasets, respectively, which indicates on-par experimental results with other approaches, in addition to the ability of reconstructing from maskless real world depth frames. Additional visual inspection to the reconstructed pointclouds has shown that the suggested approach manages to deal with the majority of the real world depth sensor noise, with the exception of large deformities to the depth field.

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4

Schüle, T., C. Schnörr, J. Hornegger, and S. Weber. "A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections." Methods of Information in Medicine 43, no. 04 (2004): 320–26. http://dx.doi.org/10.1055/s-0038-1633875.

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Summary Objectives: We investigate the feasibility of binary-valued 3D tomographic reconstruction using only a small number of projections acquired over a limited range of angles. Methods: Regularization of this strongly ill-posed problem is achieved by (i) confining the reconstruction to binary vessel/non-vessel decisions, and (ii) by minimizing a global functional involving a smoothness prior. Results: Our approach successfully reconstructs volumetric vessel structures from three projections taken within 90°. The percentage of reconstructed voxels differing from ground truth is below 1%. Conclusion: We demonstrate that for particular applications – like Digital Subtraction Angiography – 3D reconstructions are possible where conventional methods must fail, due to a severely limited imaging geometry. This could play an important role for dose reduction and 3D reconstruction using non-conventional technical setups.

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5

Rothkamm, Oliver, Johannes Gürtler, Jürgen Czarske, and Robert Kuschmierz. "Dense U-Net for Limited Angle Tomography of Sound Pressure Fields." Applied Sciences 11, no. 10 (May 17, 2021): 4570. http://dx.doi.org/10.3390/app11104570.

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Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.

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6

Wang, Chengxiang, Li Zeng, Lingli Zhang, Yumeng Guo, and Wei Yu. "An adaptive iteration reconstruction method for limited-angle CT image reconstruction." Journal of Inverse and Ill-posed Problems 26, no. 6 (December 1, 2018): 771–87. http://dx.doi.org/10.1515/jiip-2017-0034.

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Abstract The limited-angle computed tomography (CT) reconstruction problem is an ill-posed inverse problem, and the parameter selection for limited-angle CT iteration reconstruction is a difficult issue in practical application. In this paper, to alleviate the instability of limited-angle CT reconstruction problem and automatize the reconstruction process, we propose an adaptive iteration reconstruction method that the regularization parameter is chosen adaptively via the plot of the normalized wavelet coefficients fitting residual versus that the {\ell_{0}} regularization part. The experimental results show that the reconstructed images using the method with adapted regularization parameter are almost as good as that using the non-adapted parameter method in terms of visual inspection, in addition, our method has an advantage in adaptively choosing the regularization parameter.

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7

Hoyle, C., M. Sutcliffe, P. Charlton, S. Mosey, and I. Cooper. "Limited-angle ultrasonic tomography back-projection imaging." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 1 (January 1, 2021): 20–28. http://dx.doi.org/10.1784/insi.2021.63.1.20.

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Ultrasonic inspection of through-transmission is limited due to the inability to obtain defect depth information. Loss of signal is used as the only indicator, providing lateral defect information. This is often a problem in ultrasonic inspection. Radiographic acquisition techniques, where the X-ray source acts as the transmitter and the detector as the receiver, are conceptionally similar to ultrasonic through-transmission. In the latter, the tomography back-projection method is used to reconstruct images of an object that has been subjected to a minimum of 180° of rotation, to allow for full coverage of the item. In this paper, a novel approach based on back-projection is presented to improve image resolution and defect detectability. Two ultrasonic transducers in through-transmission configuration are utilised to capture data for image processing. The rotation of the transmitter and receiver is not possible in this set-up and, therefore, the reconstruction relies on the artificial generation of a limited rotation. Two probes are aligned either side of the material and are used to gather the ultrasonic signals. These signals are processed before the reconstruction algorithm is applied to them. Various processing and imaging reconstruction algorithms are explored, building on the basic back-projection method to obtain images that are better focused. This technique could be used within materials where there are high attenuation levels and, therefore, traditional pulse-echo is not feasible.

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8

Wang, Jiaxi, Li Zeng, Chengxiang Wang, and Yumeng Guo. "ADMM-based deep reconstruction for limited-angle CT." Physics in Medicine & Biology 64, no. 11 (May 29, 2019): 115011. http://dx.doi.org/10.1088/1361-6560/ab1aba.

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9

Tomitani, T., and M. Hirasawa. "Image reconstruction from limited angle Compton camera data." Physics in Medicine and Biology 47, no. 12 (June 6, 2002): 2129–45. http://dx.doi.org/10.1088/0031-9155/47/12/309.

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10

Qu, Gang-rong, and Ming Jiang. "Landweber iterative methods for angle-limited image reconstruction." Acta Mathematicae Applicatae Sinica, English Series 25, no. 2 (March 17, 2009): 327–34. http://dx.doi.org/10.1007/s10255-008-8132-8.

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11

Liu, Jianhong, Yong Guan, Liang Chen, Haobo Bai, Wenbin Wei, Yangchao Tian, and Gang Liu. "Locating the 'missing wedge' artifacts from limited-angle CT reconstruction." Microscopy and Microanalysis 24, S2 (August 2018): 140–41. http://dx.doi.org/10.1017/s1431927618013089.

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Abstract:'Missing wedge' problem exists in some kind of CT imaging situations, such as electron microscopy, x-ray nano-CT image, etc. Method such as iterative reconstruction algorithms, total variation based method were applied to improve the reconstruction quality, but the 'missing wedge' artifacts are still inevitable. In this paper, a method based on image processing technique was proposed to locate the 'missing wedge' artifacts in CT reconstruction. The result showed good performance on locating the artifacts, which also showed the potential in CT reconstruction and image analysis in nano-CT.

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12

Guo, Jingyu, Hongliang Qi, Yuan Xu, Zijia Chen, Shulong Li, and Linghong Zhou. "Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image." Computational and Mathematical Methods in Medicine 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/5836410.

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Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges.

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13

Ye, Yangbo, Hengyong Yu, and Ge Wang. "Exact Interior Reconstruction from Truncated Limited-Angle Projection Data." International Journal of Biomedical Imaging 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/427989.

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Using filtered backprojection (FBP) and an analytic continuation approach, we prove that exact interior reconstruction is possible and unique from truncated limited-angle projection data, if we assume a prior knowledge on a subregion or subvolume within an object to be reconstructed. Our results show that (i) the interior region-of-interest (ROI) problem and interior volume-of-interest (VOI) problem can be exactly reconstructed from a limited-angle scan of the ROI/VOI and a 180 degree PI-scan of the subregion or subvolume and (ii) the whole object function can be exactly reconstructed from nontruncated projections from a limited-angle scan. These results improve the classical theory of Hamaker et al. (1980).

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14

Zhang, Lingli, Li Zeng, Chengxiang Wang, and Yumeng Guo. "A non-smooth and non-convex regularization method for limited-angle CT image reconstruction." Journal of Inverse and Ill-posed Problems 26, no. 6 (December 1, 2018): 799–820. http://dx.doi.org/10.1515/jiip-2017-0042.

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Abstract Restricted by the practical applications and radiation exposure of computed tomography (CT), the obtained projection data is usually incomplete, which may lead to a limited-angle reconstruction problem. Whereas reconstructing an object from limited-angle projection views is a challenging and ill-posed inverse problem. Fortunately, the regularization methods offer an effective way to deal with that. Recently, several researchers are absorbed in {\ell_{1}} regularization to address such problem, but it has some problems for suppressing the limited-angle slope artifacts around edges due to incomplete projection data. In this paper, in order to surmount the ill-posedness, a non-smooth and non-convex method that is based on {\ell_{0}} and {\ell_{1}} regularization is presented to better deal with the limited-angle problem. Firstly, the splitting technique is utilized to deal with the presented approach called LWPC-ST-IHT. Afterwards, some propositions and convergence analysis of the presented approach are established. Numerical implementations show that our approach is more capable of suppressing the slope artifacts compared with the classical and state of the art iterative reconstruction algorithms.

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15

Wang, Chao, Min Tao, James G. Nagy, and Yifei Lou. "Limited-Angle CT Reconstruction via the $L_1/L_2$ Minimization." SIAM Journal on Imaging Sciences 14, no. 2 (January 2021): 749–77. http://dx.doi.org/10.1137/20m1341490.

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16

Turpin, Léonard, Stéphane Roux, Olivier Caty, and Sébastien Denneulin. "A Phase Field Approach to Limited-angle Tomographic Reconstruction." Fundamenta Informaticae 172, no. 2 (February 8, 2020): 203–19. http://dx.doi.org/10.3233/fi-2020-1901.

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17

Fujieda, I., K. Heiskanen, and V. Perez-Mendez. "Versatility of the CFR algorithm for limited angle reconstruction." IEEE Transactions on Nuclear Science 37, no. 2 (April 1990): 585–88. http://dx.doi.org/10.1109/23.106681.

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18

Yufang, Cai, Fu Fanping, Wang Jue, and Cheng Yan. "Optimization reconstruction of biregular term from limited-angle projections." IOP Conference Series: Earth and Environmental Science 332 (November 5, 2019): 042002. http://dx.doi.org/10.1088/1755-1315/332/4/042002.

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19

Rantala, M., S. Vanska, S. Jarvenpaa, M. Kalke, M. Lassas, J. Moberg, and S. Siltanen. "Wavelet-based reconstruction for limited-angle X-ray tomography." IEEE Transactions on Medical Imaging 25, no. 2 (February 2006): 210–17. http://dx.doi.org/10.1109/tmi.2005.862206.

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20

Rajya Lakshmi, Adidela, Sara Suresh, Prashanth Mutalik Desai, Veerender Aerranagula, N. Mounika, and Namita Kaur. "Image reconstruction techniques using deep learning quality segmentation." MATEC Web of Conferences 392 (2024): 01114. http://dx.doi.org/10.1051/matecconf/202439201114.

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Translational CT (TCT), in developing nations, a low-end computed tomography (CT) technology are relatively common. The limited-angle TCT scanning mode is often used with large-angle scanning to scan items within a narrow angular range, reduce X-ray radiation, scan long objects, and prevent detector discrepancies.. However, this scanning mode greatly reduces the picture quality and diagnostic accuracy due to the added noise and limited-angle distortions. A U-net convolutional neural network-based approach for limited-angle TCT image reconstruction has been created to reconstruct a high-quality image for the limited-angle TCT scanning mode (CNN). The limited-angle TCT projection data are first examined using the SART method, and the resulting picture is then fed into a trained CNN that can reduce artifacts and maintain structures to provide a better reconstructed image. Simulated studies are used to demonstrate the effectiveness of the algorithm designed for the limitedangle TCT scanning mode. In contrast to certain modern techniques, the developed algorithm considerably lowers noise and limited-angle artifacts while maintaining image structures.

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21

Liang, Zhiting, Yong Guan, Gang Liu, Xiangyu Chen, Fahu Li, Pengfei Guo, and Yangchao Tian. "A modified discrete algebraic reconstruction technique for multiple grey image reconstruction for limited angle range tomography." Journal of Synchrotron Radiation 23, no. 2 (February 20, 2016): 606–16. http://dx.doi.org/10.1107/s1600577516000564.

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The `missing wedge', which is due to a restricted rotation range, is a major challenge for quantitative analysis of an object using tomography. With prior knowledge of the grey levels, the discrete algebraic reconstruction technique (DART) is able to reconstruct objects accurately with projections in a limited angle range. However, the quality of the reconstructions declines as the number of grey levels increases. In this paper, a modified DART (MDART) was proposed, in which each independent region of homogeneous material was chosen as a research object, instead of the grey values. The grey values of each discrete region were estimated according to the solution of the linear projection equations. The iterative process of boundary pixels updating and correcting the grey values of each region was executed alternately. Simulation experiments of binary phantoms as well as multiple grey phantoms show that MDART is capable of achieving high-quality reconstructions with projections in a limited angle range. The interesting advancement of MDART is that neither prior knowledge of the grey values nor the number of grey levels is necessary.

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22

Shi, Lei, and Gangrong Qu. "Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving." Journal of X-Ray Science and Technology 30, no. 2 (March 15, 2022): 319–31. http://dx.doi.org/10.3233/xst-211069.

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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.

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23

Huang, Yixing, Shengxiang Wang, Yong Guan, and Andreas Maier. "Limited angle tomography for transmission X-ray microscopy using deep learning." Journal of Synchrotron Radiation 27, no. 2 (February 13, 2020): 477–85. http://dx.doi.org/10.1107/s160057752000017x.

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In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100° limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 × 10−3 µm−1 in the FBP reconstruction to 1.21 × 10−3 µm−1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 × 10−3 µm−1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science.

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24

Wang, Lei, Yong Guan, Zhiting Liang, Liang Guo, Chenxi Wei, Ronghui Luo, Gang Liu, and Yangchao Tian. "A modified equally sloped algorithm based on the total variation algorithm in computed tomography for insufficient data." Journal of Synchrotron Radiation 24, no. 2 (February 16, 2017): 490–97. http://dx.doi.org/10.1107/s160057751700100x.

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Computed tomography (CT) has become an important technique for analyzing the inner structures of material, biological and energy fields. However, there are often challenges in the practical application of CT due to insufficient data. For example, the maximum rotation angle of the sample stage is limited by sample space or image reconstruction from the limited number of views required to reduce the X-ray dose delivered to the sample. Therefore, it is difficult to acquire CT images with complete data. In this work, an iterative reconstruction algorithm based on the minimization of the image total variation (TV) has been utilized to develop equally sloped tomography (EST), and the reconstruction was carried out from limited-angle, few-view and noisy data. A synchrotron CT experiment on hydroxyapatite was also carried out to demonstrate the ability of the TV-EST algorithm. The results indicated that the new TV-EST algorithm was capable of achieving high-quality reconstructions from projections with insufficient data.

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25

Xie, En, Peijun Ni, Rongfan Zhang, and Xiongbing Li. "Limited-Angle CT Reconstruction with Generative Adversarial Network Sinogram Inpainting and Unsupervised Artifact Removal." Applied Sciences 12, no. 12 (June 20, 2022): 6268. http://dx.doi.org/10.3390/app12126268.

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High-quality limited-angle computed tomography (CT) reconstruction is in high demand in the medical field. Being unlimited by the pairing of sinogram and the reconstructed image, unsupervised methods have attracted wide attention from researchers. The reconstruction limit of the existing unsupervised reconstruction methods, however, is to use [0°, 120°] of projection data, and the quality of the reconstruction still has room for improvement. In this paper, we propose a limited-angle CT reconstruction generative adversarial network based on sinogram inpainting and unsupervised artifact removal to further reduce the angle range limit and to improve the image quality. We collected a large number of CT lung and head images and Radon transformed them into missing sinograms. Sinogram inpainting network is developed to complete missing sinograms, based on which the filtered back projection algorithm can output images with most artifacts removed; then, these images are mapped to artifact-free images by using artifact removal network. Finally, we generated reconstruction results sized 512×512 that are comparable to full-scan reconstruction using only [0°, 90°] of limited sinogram projection data. Compared with the current unsupervised methods, the proposed method can reconstruct images of higher quality.

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26

Wu, Chenning, Martin Hutton, and Manuchehr Soleimani. "Limited Angle Electrical Resistance Tomography in Wastewater Monitoring." Sensors 20, no. 7 (March 29, 2020): 1899. http://dx.doi.org/10.3390/s20071899.

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Electrical resistance tomography (ERT) has been investigated in monitoring conductive flows due to its high speed, non-intrusive and no radiation hazard advantages. Recently, we have developed an ERT system for the novel application of smart wastewater metering. The dedicated low cost and high-speed design of the reported ERT device allows for imaging pipes with different flow constituents and monitoring the sewer networks. This work extends the capability of such a system to work with partially filled lateral pipes where the incomplete data issue arises due to the electrodes losing contact with the conductive medium. Although the ERT for such a limited region has been developed for many years, there is no study on imaging content within these limited regions. For wastewater monitoring, this means imaging the wastewater and solid inclusions at the same time. This paper has presented a modified ERT system that has the capacity to image inclusions within the conductive region using limited data. We have adjusted the ERT hardware to register the information of the non-contact electrodes and hence the valid measurements. A limited region image reconstruction method based on Jacobian reformulation is applied to gain robustness when it comes to inclusion recovery in limited data ERT. Both simulation and experimental results have demonstrated an enhanced performance brought by the limited region method in comparison to the global reconstruction.

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27

Wang, Jia, Mingzhe Li, Junxia Cheng, Zhenyan Guo, Dangjuan Li, and Shenjiang Wu. "Exact reconstruction condition for angle-limited computed tomography of chemiluminescence." Applied Optics 60, no. 15 (May 12, 2021): 4273. http://dx.doi.org/10.1364/ao.420223.

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28

Kinoshita, Fujimi, Masamichi Yanagisawa, Hirokazu Turuoka, and Toshiyuki Nakayama. "76. Evaluation of limited angle SPECT reconstruction for myocardial SPECT." Japanese Journal of Radiological Technology 50, no. 2 (1994): 195. http://dx.doi.org/10.6009/jjrt.kj00003534558.

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29

Yao, Lei, and Huabei Jiang. "Photoacoustic image reconstruction from few-detector and limited-angle data." Biomedical Optics Express 2, no. 9 (August 19, 2011): 2649. http://dx.doi.org/10.1364/boe.2.002649.

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30

Wang, Ting, Katsuhiro Nakamoto, Heye Zhang, and Huafeng Liu. "Reweighted Anisotropic Total Variation Minimization for Limited-Angle CT Reconstruction." IEEE Transactions on Nuclear Science 64, no. 10 (October 2017): 2742–60. http://dx.doi.org/10.1109/tns.2017.2750199.

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31

Qu, Gang-rong, Yong-sheng Lan, and Ming Jiang. "An iterative algorithm for angle-limited three-dimensional image reconstruction." Acta Mathematicae Applicatae Sinica, English Series 24, no. 1 (January 2008): 157–66. http://dx.doi.org/10.1007/s10255-007-7006-9.

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32

Xue, Xiao, Shusen Zhao, Yunsong Zhao, and Peng Zhang. "Image reconstruction for limited-angle computed tomography with curvature constraint." Measurement Science and Technology 30, no. 12 (September 17, 2019): 125401. http://dx.doi.org/10.1088/1361-6501/ab3c72.

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33

Kudo, Hiroyuki, and Tsuneo Saito. "A Tomographic Image Reconstruction from Limited View Angle Projection Data." Systems and Computers in Japan 19, no. 7 (1988): 56–64. http://dx.doi.org/10.1002/scj.4690190706.

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34

Shen, Enxiang, Yuxin Wang, Jie Yuan, and Paul L. Carson. "Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal." Applied Sciences 12, no. 22 (November 16, 2022): 11627. http://dx.doi.org/10.3390/app122211627.

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Breast cancer is the most common cancer in women and the second most common cancer in the world. Digital breast tomosynthesis (DBT) is an effective medical imaging method. It can reduce the overlap of breast tissue in reconstructed images, which is beneficial to the early detection of breast cancer. DBT uses projection data from a limited range of angles and the simultaneous algebraic reconstruction technique (SART) based reconstruction method. Since the detector’s field of view (FOV) is limited, the updates of the large projection angles in SART cannot include all the voxels of the imaging target, which causes truncated projection artifacts (TPA) at the edges of the image. In this work, we use the images reconstructed by SART to perform re-projection on the virtually expanded detector panel and use a gradient calculation method to compensate for missing projection data to ensure that each update can include all the voxels. Experiments on simulation and human breast demonstrated that TPA can be effectively reduced while retaining the detailed tissue structure, thus improving the image quality at the border and recovering the obscured structural information. It might provide a better imaging result for the consequential clinical diagnosis.

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35

Isernhagen, C. F., D. Schäfer, M. Grass, and T. M. Buzug. "Three-dimensional anisotropic regularization for limited angle tomography." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 283–85. http://dx.doi.org/10.1515/cdbme-2015-0070.

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AbstractLimited angle tomography is a challenging task in medical imaging. Due to practical limitations during the image acquisition, the sinogram is recorded incompletely and thus the quality of the reconstruction is deteriorated by streak artifacts. These artifacts are characterized by fast changes of the local intensity gradients and increase the total variation (TV). Generally, an energy functional is optimized which leads to a minimized Total Variation Minimization (TVM). As an outcome, noise and artifacts are reduced while edges are preserved. Anyway, often the orientation of the streak artifacts is not considered at all. Therefore, anisotropic regularization is used to reduce noise and distortions under specific directions.

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36

Li, Cai, Wang, Zhang, Tang, Li, Liang, and Yan. "Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging." Sensors 19, no. 18 (September 12, 2019): 3941. http://dx.doi.org/10.3390/s19183941.

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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.

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37

Lu, Fan, Haruka Inamoto, Shuto Takeishi, Shingo Tamaki, Sachie Kusaka, and Isao Murata. "Development of a New Image Reconstruction Method Using Bayesian Estimation with Limited View-Angle Projection Data for BNCT-SPECT." Applied Sciences 14, no. 20 (October 15, 2024): 9411. http://dx.doi.org/10.3390/app14209411.

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Boron Neutron Capture Therapy (BNCT) is an emerging radiation treatment for cancer, and its challenges are being explored. Systems capable of capturing real-time observations of this treatment’s effectiveness, particularly BNCT-SPECT methods that measure gamma rays emitted instantaneously from outside the body during nuclear reactions and that reconstruct images using Single Photon Emission Computed Tomography (SPECT) techniques, remain unavailable. BNCT-SPECT development is hindered by two main factors, the first being the projection angle. Unlike conventional SPECT, the projection angle range which is achievable by rotating a detector array cannot exceed approximately 90 degrees. Consequently, Fourier-based image reconstruction methods, requiring projections from at least 180 degrees, do not apply to BNCT-SPECT. The second limitation is the measurement time. Given these challenges, we developed a new sequential approximation image reconstruction method using Bayesian estimation, which is effective under the stringent BNCT-SPECT conditions. We also compared the proposed method with the existing Maximum Likelihood-Expectation Maximization (ML-EM) image reconstruction method. Numerical experiments were conducted by obtaining BNCT-SPECT projection data from true images and reconstructing images using both the proposed and ML-EM methods from the resulting sinograms. Performance comparisons were conducted using a dedicated program applying Bayesian estimation and this showed promise as a new image reconstruction method useful under BNCT-SPECT conditions.

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38

Kulikajevas, Audrius, Rytis Maskeliunas, Robertas Damasevicius, and Tomas Krilavicius. "Auto-Refining 3D Mesh Reconstruction Algorithm From Limited Angle Depth Data." IEEE Access 10 (2022): 87083–98. http://dx.doi.org/10.1109/access.2022.3143467.

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39

Yu, Wei, та Li Zeng. "ℓ0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography". PLOS ONE 10, № 7 (9 липня 2015): e0130793. http://dx.doi.org/10.1371/journal.pone.0130793.

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40

Li, Qing, Tao Wang, RunRui Li, Yan Qiang, Bin Zhang, Jijie Sun, JuanJuan Zhao, and Wei Wu. "TLIR: Two-layer iterative refinement model for limited-angle CT reconstruction." Biomedical Signal Processing and Control 100 (February 2025): 107058. http://dx.doi.org/10.1016/j.bspc.2024.107058.

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41

Darenfed, S., and D. D. Verhoeven. "TOMOGRAPHIC RECONSTRUCTION OF TRANSPARENT OBJECTS FROM A LIMITED ANGLE OF VIEW." Transactions of the Canadian Society for Mechanical Engineering 16, no. 3-4 (September 1992): 351–62. http://dx.doi.org/10.1139/tcsme-1992-0019.

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42

Chen, Zhiqiang, Xin Jin, Liang Li, and Ge Wang. "A limited-angle CT reconstruction method based on anisotropic TV minimization." Physics in Medicine and Biology 58, no. 7 (March 8, 2013): 2119–41. http://dx.doi.org/10.1088/0031-9155/58/7/2119.

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43

Jianhua Luo, Wanqing Li, and Yuemin Zhu. "Reconstruction From Limited-Angle Projections Based on $\delta-u$ Spectrum Analysis." IEEE Transactions on Image Processing 19, no. 1 (January 2010): 131–40. http://dx.doi.org/10.1109/tip.2009.2032893.

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44

Sidky, Emil Y., Chien-Min Kao, and Xiaochuan Pan. "Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 14, no. 2 (January 2006): 119–39. http://dx.doi.org/10.3233/xst-2006-00155.

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In practical applications of tomographic imaging, there are often challenges for image reconstruction due to under-sampling and insufficient data. In computed tomography (CT), for example, image reconstruction from few views would enable rapid scanning with a reduced x-ray dose delivered to the patient. Limited-angle problems are also of practical significance in CT. In this work, we develop and investigate an iterative image reconstruction algorithm based on the minimization of the image total variation (TV) that applies to divergent-beam CT. Numerical demonstrations of our TV algorithm are performed with various insufficient data problems in fan-beam CT. The TV algorithm can be generalized to cone-beam CT as well as other tomographic imaging modalities.

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45

BaniOdeh, Doaa, and Mohammad Hjouj. "An advanced approach to reconstruct CT images from limited-angle projections, reducing radiation dose and tube load." Journal of Physics: Conference Series 2701, no. 1 (February 1, 2024): 012027. http://dx.doi.org/10.1088/1742-6596/2701/1/012027.

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Abstract The process of reconstructing CT scan images from limited angle projections is critical and requires strict adherence to the ALARA principle. This principle is designed to minimize radiation exposure while maintaining image quality. Our study utilized filter back-projection (FBP) and algebraic iterative reconstruction (IR) algorithms to reconstruct brain CT images from 200 projection lines and a 100 × 100 matrix size. By combining the results of a MATLAB function with the insights of a radiologist, we can produce high-quality images that decrease radiation dose and tube load. Our findings reveal that the algebraic method is superior to the filter back-projection in preserving image quality when utilizing limited-angle projections.

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46

Bieberle, M., and U. Hampel. "Level-set reconstruction algorithm for ultrafast limited-angle X-ray computed tomography of two-phase flows." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2043 (June 13, 2015): 20140395. http://dx.doi.org/10.1098/rsta.2014.0395.

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Tomographic image reconstruction is based on recovering an object distribution from its projections, which have been acquired from all angular views around the object. If the angular range is limited to less than 180° of parallel projections, typical reconstruction artefacts arise when using standard algorithms. To compensate for this, specialized algorithms using a priori information about the object need to be applied. The application behind this work is ultrafast limited-angle X-ray computed tomography of two-phase flows. Here, only a binary distribution of the two phases needs to be reconstructed, which reduces the complexity of the inverse problem. To solve it, a new reconstruction algorithm (LSR) based on the level-set method is proposed. It includes one force function term accounting for matching the projection data and one incorporating a curvature-dependent smoothing of the phase boundary. The algorithm has been validated using simulated as well as measured projections of known structures, and its performance has been compared to the algebraic reconstruction technique and a binary derivative of it. The validation as well as the application of the level-set reconstruction on a dynamic two-phase flow demonstrated its applicability and its advantages over other reconstruction algorithms.

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47

Goy, Alexandre, Girish Rughoobur, Shuai Li, Kwabena Arthur, Akintunde I. Akinwande, and George Barbastathis. "High-resolution limited-angle phase tomography of dense layered objects using deep neural networks." Proceedings of the National Academy of Sciences 116, no. 40 (September 16, 2019): 19848–56. http://dx.doi.org/10.1073/pnas.1821378116.

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We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

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48

Xiao, Yu, Wang Zhong, Yongcun Li, Xiaofang Hu, and Feng Xu. "A new limited-angle CT reconstruction algorithm based on the local anisotropic total variation restoration of continuity." Journal of Instrumentation 17, no. 12 (December 1, 2022): P12018. http://dx.doi.org/10.1088/1748-0221/17/12/p12018.

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Abstract A new limited-angle computed tomography(CT) reconstruction algorithm based on local detail information restoration in the CT image projection domain was proposed in this paper. The lost projection information could be repaired with the new algorithm called local anisotropic total variation repair method of sinogram (LATV). According to the continuity of local gradient in sinogram, local high frequency characteristics can be improved which are blurred by conventional TVM. A series of numerical reconstruction experiments were conducted to validate the new algorithm. The overall quality of images reconstructed by new algorithm was better than conventional TVM algorithm when quantitatively evaluated. Then, a local region with rich details was selected in order to evaluate the reconstruction effect of the internal details. From the perspective of three image quality evaluation parameters, it was shown that the local high frequency details were well repaired. Finally, the reconstruction quality was evaluated by actual CT experimental data. The new method also performed better than conventional method. Therefore, the new LATV algorithm proposed in this paper may be a new method suitable for limited-angle CT reconstruction problems.

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49

Khaled, Alia S., and Thomas J. Beck. "Successive binary algebraic reconstruction technique: An algorithm for reconstruction from limited angle and limited number of projections decomposed into individual components." Journal of X-Ray Science and Technology 21, no. 1 (2013): 9–24. http://dx.doi.org/10.3233/xst-130363.

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50

Majee, Soumendu, Thilo Balke, Craig Kemp, Gregery Buzzard, and Charles Bouman. "Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction." IEEE Transactions on Computational Imaging 7 (2021): 448–62. http://dx.doi.org/10.1109/tci.2021.3074881.

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