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Yulin Zhu, Hanqing Zhao, Tangsheng Wang, Lei Deng, Yupeng Yang, Yuming Jiang, Na Li, Yinping Chan, Jingjing Dai, Chulong Zhang, Yunhui Li, Yaoqin Xie, Xiaokun Liang
Summary: Metal artifacts significantly degrade the quality of CT images, hindering clinical diagnosis and treatment. We propose a region-based correction method using deep learning in the sinogram domain to address this issue.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
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Chemistry, Analytical
Mohamed A. A. Hegazy, Myung Hye Cho, Min Hyoung Cho, Soo Yeol Lee
Summary: Metal artifacts in dental CT images often severely compromise image readability. The direct sinogram correction (DSC) method works well for mild metal artifacts but fails for severe ones. We propose a modified DSC method that reduces severe metal artifacts by segmenting metallic objects and weighting the sinogram correction with metal path length. Our method outperforms the original DSC method in cases of severe metal artifacts, but further studies are needed to validate its applicability under different CT scan conditions and with more patient images.
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Instruments & Instrumentation
Hui Tang, Yu Bing Lin, Guo Yan Sun, Xu Dong Bao
Summary: This study proposed a new Poisson fusion sinogram based metal artifact reduction (FS-MAR) method to reduce secondary artifacts, achieving higher image quality in two testing samples. Experimental results showed that using FS-MAR method generates less image artifacts compared to using the interpolation-based algorithm.
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY
(2021)
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Computer Science, Interdisciplinary Applications
Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Lei Xing
Summary: In this article, a novel metal artifact reduction method is proposed, which leverages the advantages of image domain and sinogram domain-based techniques for enhanced results in radiation therapy, effectively reducing new artifacts in reconstructed CT images.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
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Engineering, Biomedical
Manman Zhu, Qisen Zhu, Yuyan Song, Yi Guo, Dong Zeng, Zhaoying Bian, Yongbo Wang, Jianhua Ma
Summary: This paper presents a physics-informed sinogram completion (PISC) method for metal artifact reduction (MAR) in CT imaging. It effectively reduces metal artifacts and recovers structural textures by completing the original sinogram using linear interpolation and beam-hardening correction, fusing the corrected sinograms with adaptive weights, and applying a post-processing frequency split algorithm. The method demonstrates good performance in correcting metal implants with different shapes and materials and preserving structure details.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
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Chemistry, Analytical
Linlin Zhu, Yu Han, Xiaoqi Xi, Lei Li, Bin Yan
Summary: The traditional U-net, modified and augmented with two sinogram feature losses, enhances the accuracy of metal artifact data. Masking metal traces during artifact reduction improves stability and reliability. The proposed method accurately repairs missing data and reduces metal artifacts in reconstructed images.
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Materials Science, Characterization & Testing
Markus Wedekind, Susana Castillo, Marcus Magnor
Summary: This article introduces a method for correcting ring artifacts in computed tomography (CT) reconstruction. The method compensates for errors in the gain and offset values of each pixel and reduces blur by inferring information from neighboring pixels. Experimental results show that this method effectively mitigates the shortcomings of purely offset-based approaches and approaches using all projections, and can be efficiently implemented.
JOURNAL OF NONDESTRUCTIVE EVALUATION
(2023)
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Radiology, Nuclear Medicine & Medical Imaging
Jannis Dickmann, Christina Sarosiek, Stefanie Goetz, Mark Pankuch, George Coutrakon, Robert P. Johnson, Reinhard W. Schulte, Katia Parodi, Guillaume Landry, George Dedes
Summary: The study successfully reduced image artifacts and improved the accuracy of relative stopping power using a simple empirical artifact correction method on an experimental pCT scanner. Testing on various phantoms showed a significant reduction in artifacts and improved scan image accuracy.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2021)
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Computer Science, Information Systems
Yuelong Li, Tongshun Zhang, Junyu Bi, Jianming Wang
Summary: This paper proposes a newly designed adversarial framework called DD-GAN, which aims to simultaneously recover content defection and tone disorder in images. The method emphasizes the fusion of image inpainting and tone correction through the GAN network, and extensive experiments have been conducted to verify its effectiveness.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
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Engineering, Biomedical
Lequan Yu, Zhicheng Zhang, Xiaomeng Li, Hongyi Ren, Wei Zhao, Lei Xing
Summary: This study presents a novel deep-learning-based approach for metal artifact reduction in x-ray CT images, utilizing a self-supervised cross-domain learning framework to mitigate the influence of metallic implants. By training a neural network with multiple loss functions, the method enhances the image reconstruction quality and demonstrates superior results compared to other methods.
PHYSICS IN MEDICINE AND BIOLOGY
(2021)
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Radiology, Nuclear Medicine & Medical Imaging
Kai Xie, Liugang Gao, Heng Zhang, Sai Zhang, Qianyi Xi, Fan Zhang, Jiawei Sun, Tao Lin, Jianfeng Sui, Xinye Ni
Summary: A Metal Artifacts Region Inpainting Network (MARINet) has been developed to generate normal MRI images in the image domain and improve image quality by leveraging the symmetry of brain MRI images.
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Radiology, Nuclear Medicine & Medical Imaging
Kai Xie, Liugang Gao, Zhengda Lu, Chunying Li, Qianyi Xi, Fan Zhang, Jiawei Sun, Tao Lin, Jianfeng Sui, Xinye Ni
Summary: In this study, the GatedConv generative adversarial network combined with gated convolution and contextual attention was used to inpaint the metal artifact region in MRI images. GatedConv demonstrated better inpainting performance compared to other models, effectively improving image quality.
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Oncology
Fang-Ling Zhang, Ruo-Cheng Li, Xiao-Ling Zhang, Zhao-Hui Zhang, Ling Ma, Lei Ding
Summary: The study found that using the SEMAR algorithm can reduce metal artifacts in knee tumor prostheses, and increase diagnostic confidence for prosthetic complications and tumor recurrence.
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Engineering, Biomedical
Yusuke Morioka, Katsuhiro Ichikawa, Hiroki Kawashima
Summary: This study assessed the effectiveness of a metal artifact reduction (MAR) technique incorporating noise recovery in computed tomography (CT) images. The results showed that noise recovery can reduce artifact index and improve image quality.
PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE
(2023)
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Engineering, Biomedical
Hui Tang, Jiashun Wang, Liang Sun, Shijie Wang, Jun Xiang, Yan Xi, Yang Chen, Yanni Jiang
Summary: This study proposes an efficient and accurate method to remove calcification artifacts and retain calcification information in Digital Breast Tomosynthesis (DBT). By introducing a new segmentation method and an interpolation method, the proposed algorithm effectively reduces calcification artifacts and preserves effective information in the image. This research is significant for more efficient and accurate DBT breast cancer screening.
PHYSICS IN MEDICINE AND BIOLOGY
(2023)
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Radiology, Nuclear Medicine & Medical Imaging
Ricardo Otazo, Mathias Nittka, Mary Bruno, Esther Raithel, Christian Geppert, Soterios Gyftopoulos, Michael Recht, Leon Rybak
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Faysal F. Altahawi, Kevin J. Blount, Nicholas P. Morley, Esther Raithel, Imran M. Omar
SKELETAL RADIOLOGY
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Cesar de Cesar Netto, Lucas F. Fonseca, Benjamin Fritz, Steven E. Stern, Esther Raithel, Mathias Nittka, Lew C. Schon, Jan Fritz
EUROPEAN RADIOLOGY
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Radiology, Nuclear Medicine & Medical Imaging
Vivek Kalia, Benjamin Fritz, Rory Johnson, Wesley D. Gilson, Esther Raithel, Jan Fritz
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Radiology, Nuclear Medicine & Medical Imaging
Lena Sonnow, Wesley D. Gilson, Esther Raithel, Mathias Nittka, Frank Wacker, Jan Fritz
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(2018)
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Radiology, Nuclear Medicine & Medical Imaging
Chengcheng Zhu, Bing Tian, Luguang Chen, Laura Eisenmenger, Esther Raithel, Christoph Forman, Sinyeob Ahn, Gerhard Laub, Qi Liu, Jianping Lu, Jing Liu, Christopher Hess, David Saloner
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
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Radiology, Nuclear Medicine & Medical Imaging
B. Henninger, E. Raithel, C. Kranewitter, M. Steurer, W. Jaschke, C. Kremser
EUROPEAN JOURNAL OF RADIOLOGY
(2018)
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Radiology, Nuclear Medicine & Medical Imaging
Alexandra Grimm, Heiko Meyer, Marcel D. Nickel, Mathias Nittka, Esther Raithel, Oliver Chaudry, Andreas Friedberger, Michael Uder, Wolfgang Kemmler, Harald H. Quick, Klaus Engelke
EUROPEAN JOURNAL OF RADIOLOGY
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Liang Zhu, Xi Wu, Zhaoyong Sun, Zhengyu Jin, Elisabeth Weiland, Esther Raithel, Tianyi Qian, Huadan Xue
INVESTIGATIVE RADIOLOGY
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Mikell Yuhasz, Michael J. Hoch, Mari Hagiwara, Mary T. Bruno, James S. Babb, Esther Raithel, Christoph Forman, Abbas Anwar, J. Thomas Roland, Timothy M. Shepherd
INVESTIGATIVE RADIOLOGY
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Benjamin Fritz, Susanne Bensler, Gaurav K. Thawait, Esther Raithel, Steven E. Stern, Jan Fritz
EUROPEAN RADIOLOGY
(2019)
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Radiology, Nuclear Medicine & Medical Imaging
Filippo Del Grande, Marco Delcogliano, Riccardo Guglielmi, Esther Raithel, Steven E. Stern, Derek F. Papp, Christian Candrian, Jan Fritz
INVESTIGATIVE RADIOLOGY
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Jan Fritz, Shivani Ahlawat, Benjamin Fritz, Gaurav K. Thawait, Steven E. Stern, Esther Raithel, Walter Klyce, Rushyuan J. Lee
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2019)
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Daniel Polak, Stephen Cauley, Susie Y. Huang, Maria Gabriela Longo, John Conklin, Berkin Bilgic, Ned Ohringer, Esther Raithel, Peter Bachert, Lawrence L. Wald, Kawin Setsompop
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Chengcheng Zhu, Lizhen Cao, Zhaoying Wen, Sinyeob Ahn, Esther Raithel, Christoph Forman, Michael Hope, David Saloner
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(2019)