1
|
Wang Y, Chen Z, Gao H, Deng Y, Zhang L. An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization. Med Phys 2024; 51:4121-4132. [PMID: 38452276 DOI: 10.1002/mp.16988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/03/2024] [Accepted: 01/29/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preserving details. PURPOSE This study aims to develop an effective method for removing ring artifacts from reconstructed CT images. METHODS The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correction process. RESULTS Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CT imaging. CONCLUSIONS The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patient care.
Collapse
Affiliation(s)
- Yuang Wang
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Hewei Gao
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Yifan Deng
- Department of Engineering Physics, Tsinghua University, Beijing, China
| | - Li Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, China
| |
Collapse
|
2
|
Kazimirov D, Polevoy D, Ingacheva A, Chukalina M, Nikolaev D. Adaptive automated sinogram normalization for ring artifacts suppression in CT. OPTICS EXPRESS 2024; 32:17606-17643. [PMID: 38858941 DOI: 10.1364/oe.522941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/13/2024] [Indexed: 06/12/2024]
Abstract
Ring artifacts pose a major barrier to obtaining precise reconstruction in computed tomography (CT). The presence of ring artifacts complicates the use of automatic means of processing CT reconstruction results, such as segmentation, correction of geometric shapes, alignment of reconstructed volumes. Although there are numerous efficient methods for suppressing ring artifacts, many of them appear to be manual. Along with this, a large proportion of the automatic methods cope unsatisfactorily with the target task while requiring computational capacity. The current work introduces a projection data preprocessing method for suppressing ring artifacts that constitutes a compromise among the outlined aspects - automaticity, high efficiency and computational speed. Derived as the automation of the classical sinogram normalization method, the proposed method specific advantages consist in adaptability in relation to the filtered sinograms and the edge-preservation property proven within the experiments on both synthetic and real CT data. Concerning the challenging open-access data, the method has performed superior quality comparable to that of the advanced methods: it has demonstrated 70.4% ring artifacts suppression percentage (RASP) quality metric. In application to our real laboratory CT data, the proposed method allowed us to gain significant refinement of the reconstruction quality which has not been surpassed by a range of compared manual ring artifacts suppression methods.
Collapse
|
3
|
Fu T, Wang Y, Zhang K, Zhang J, Wang S, Huang W, Wang Y, Yao C, Zhou C, Yuan Q. Deep-learning-based ring artifact correction for tomographic reconstruction. JOURNAL OF SYNCHROTRON RADIATION 2023; 30:620-626. [PMID: 36897392 PMCID: PMC10161896 DOI: 10.1107/s1600577523000917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/01/2023] [Indexed: 05/06/2023]
Abstract
X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.
Collapse
Affiliation(s)
- Tianyu Fu
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Yan Wang
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Kai Zhang
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Jin Zhang
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Shanfeng Wang
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Wanxia Huang
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Yaling Wang
- CAS Key Laboratory for Biomedical Effects of Nanomedicines and Nanosafety and CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, People's Republic of China
| | - Chunxia Yao
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Chenpeng Zhou
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| | - Qingxi Yuan
- Beijing Synchrotron Radiation Facility, X-ray Optics and Technology Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing 010000, People's Republic of China
| |
Collapse
|
4
|
Stainsby AV, DeKoninck PLJ, Crossley KJ, Thiel A, Wallace MJ, Pearson JT, Kashyap AJ, Croughan MK, Allison BA, Hodges R, Thio M, Flemmer AW, McGillick EV, Te Pas AB, Hooper SB, Kitchen MJ. Effect of prenatal diaphragmatic hernia on pulmonary arterial morphology. Anat Rec (Hoboken) 2023. [PMID: 36688449 DOI: 10.1002/ar.25159] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 10/28/2022] [Accepted: 12/19/2022] [Indexed: 01/24/2023]
Abstract
Congenital diaphragmatic hernia (CDH) is a major cause of severe lung hypoplasia and pulmonary hypertension in the newborn. While the pulmonary hypertension is thought to result from abnormal vascular development and arterial vasoreactivity, the anatomical changes in vascular development are unclear. We have examined the 3D structure of the pulmonary arterial tree in rabbits with a surgically induced diaphragmatic hernia (DH). Fetal rabbits (n = 6) had a left-sided DH created at gestational day 23 (GD23), delivered at GD30, and briefly ventilated; sham-operated litter mates (n = 5) acted as controls. At postmortem the pulmonary arteries were filled with a radio-opaque resin before the lungs were scanned using computed tomography (CT). The 3D reconstructed images were analyzed based on vascular branching hierarchy using the software Avizo 2020.2. DH significantly reduced median number of arteries (2,579 (8440) versus 576 (442), p = .017), artery numbers per arterial generation, mean total arterial volume (43.5 ± 8.4 vs. 19.9 ± 3.1 μl, p = .020) and mean total arterial cross-sectional area (82.5 ± 2.3 vs. 28.2 ± 6.2 mm2 , p =.036). Mean arterial radius was increased in DH kittens between the eighth and sixth branching generation and mean arterial length between the sixth and 28th branching generation. A DH in kittens resulted in threefold reduction in pulmonary arterial cross-sectional area, primarily due to reduced arterial branching. Thus, the reduction in arterial cross-sectional area could be a major contributor to pulmonary hypertension infants with CDH.
Collapse
Affiliation(s)
- Andrew V Stainsby
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Philip L J DeKoninck
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Division of Obstetrics and Fetal Medicine, Department of Obstetrics and Gynaecology, Erasmus MC University Medical Center - Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Kelly J Crossley
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Alison Thiel
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
| | - Megan J Wallace
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - James T Pearson
- National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Physiology, Victoria Heart Institute and Monash Biomedicine Institute, Monash University, Melbourne, Australia
| | - Aidan J Kashyap
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | | | - Beth A Allison
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Ryan Hodges
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Marta Thio
- Newborn Research Centre, The Royal Women's Hospital, Melbourne, Australia
- The Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Australia
| | - Andreas W Flemmer
- Division of Neonatology Dr. von Hauner Children's Hospital and Perinatal Center, LMU University Hospital, Munich, Germany
| | - Erin V McGillick
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Arjan B Te Pas
- Division of Neonatology, Department of Pediatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Stuart B Hooper
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- Department of Obstetrics and Gynaecology, Monash University, Melbourne, Australia
| | - Marcus J Kitchen
- Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia
- School of Physics and Astronomy, Monash University, Melbourne, Australia
| |
Collapse
|
5
|
Li Y, Han S, Zhao Y, Li F, Ji D, Zhao X, Liu D, Jian J, Hu C. Synchrotron microtomography image restoration via regularization representation and deep CNN prior. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107181. [PMID: 36257200 DOI: 10.1016/j.cmpb.2022.107181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/29/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Synchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously. METHODS There are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images. RESULTS Our proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods. CONCLUSIONS The proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.
Collapse
Affiliation(s)
- Yimin Li
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Shuo Han
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Yuqing Zhao
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Fangzhi Li
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
| | - Dongjiang Ji
- School of Science, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Xinyan Zhao
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China; Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis and National Clinical Research Center of Digestive Disease, Beijing 100050, China
| | - Dayong Liu
- Tianjin Medical University school of stomatology, Tianjin 300070, China
| | - Jianbo Jian
- Department of Radiation Oncology, Tianjin Medical University General Hospital, Tianjin 300070, China
| | - Chunhong Hu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China.
| |
Collapse
|
6
|
Mäkinen Y, Marchesini S, Foi A. Ring artifact and Poisson noise attenuation via volumetric multiscale nonlocal collaborative filtering of spatially correlated noise. JOURNAL OF SYNCHROTRON RADIATION 2022; 29:829-842. [PMID: 35511015 PMCID: PMC9070695 DOI: 10.1107/s1600577522002739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 03/10/2022] [Indexed: 06/14/2023]
Abstract
X-ray micro-tomography systems often suffer from high levels of noise. In particular, severe ring artifacts are common in reconstructed images, caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. Furthermore, the projections commonly contain high levels of Poissonian noise arising from the photon-counting detector. This work presents a 3-D multiscale framework for streak attenuation through a purposely designed collaborative filtering of correlated noise in volumetric data. A distinct multiscale denoising step for attenuation of the Poissonian noise is further proposed. By utilizing the volumetric structure of the projection data, the proposed fully automatic procedure offers improved feature preservation compared with 2-D denoising and avoids artifacts which arise from individual filtering of sinograms.
Collapse
Affiliation(s)
| | - Stefano Marchesini
- SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
| | | |
Collapse
|
7
|
Faragó T, Gasilov S, Emslie I, Zuber M, Helfen L, Vogelgesang M, Baumbach T. Tofu: a fast, versatile and user-friendly image processing toolkit for computed tomography. JOURNAL OF SYNCHROTRON RADIATION 2022; 29:916-927. [PMID: 35511025 PMCID: PMC9070706 DOI: 10.1107/s160057752200282x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/14/2022] [Indexed: 06/01/2023]
Abstract
Tofu is a toolkit for processing large amounts of images and for tomographic reconstruction. Complex image processing tasks are organized as workflows of individual processing steps. The toolkit is able to reconstruct parallel and cone beam as well as tomographic and laminographic geometries. Many pre- and post-processing algorithms needed for high-quality 3D reconstruction are available, e.g. phase retrieval, ring removal and de-noising. Tofu is optimized for stand-alone GPU workstations on which it achieves reconstruction speed comparable with costly CPU clusters. It automatically utilizes all GPUs in the system and generates 3D reconstruction code with minimal number of instructions given the input geometry (parallel/cone beam, tomography/laminography), hence yielding optimal run-time performance. In order to improve accessibility for researchers with no previous knowledge of programming, tofu contains graphical user interfaces for both optimization of 3D reconstruction parameters and batch processing of data with pre-configured workflows for typical computed tomography reconstruction. The toolkit is open source and extensive documentation is available for both end-users and developers. Thanks to the mentioned features, tofu is suitable for both expert users with specialized image processing needs (e.g. when dealing with data from custom-built computed tomography scanners) and for application-specific end-users who just need to reconstruct their data on off-the-shelf hardware.
Collapse
Affiliation(s)
- Tomáš Faragó
- Institute for Photon Science and Synchrotron Radiation, Karlsruhe Institute of Technology (KIT), Herrmann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Sergey Gasilov
- Canadian Light Source, 44 Innovation Blvd, Saskatoon, Canada S7N 2V3
| | - Iain Emslie
- Canadian Light Source, 44 Innovation Blvd, Saskatoon, Canada S7N 2V3
| | - Marcus Zuber
- Institute for Photon Science and Synchrotron Radiation, Karlsruhe Institute of Technology (KIT), Herrmann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Kaiserstrasse 12, 76131 Karlsruhe, Germany
| | - Lukas Helfen
- Institute for Photon Science and Synchrotron Radiation, Karlsruhe Institute of Technology (KIT), Herrmann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Institut Laue-Langevin, 71 Avenue des Martyrs, CS 20156, 38042 Grenoble Cedex 9, France
| | - Matthias Vogelgesang
- Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Tilo Baumbach
- Institute for Photon Science and Synchrotron Radiation, Karlsruhe Institute of Technology (KIT), Herrmann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
- Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Kaiserstrasse 12, 76131 Karlsruhe, Germany
| |
Collapse
|
8
|
Li Y, Zhao Y, Ji D, Lv W, Xin X, Zhao X, Liu D, Ouyang Z, Hu C. Sparse-domain regularized stripe decomposition combined with guided-image filtering for ring artifact removal in propagation-based x-ray phase-contrast CT. Phys Med Biol 2021; 66. [PMID: 33878737 DOI: 10.1088/1361-6560/abf9de] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 04/20/2021] [Indexed: 11/11/2022]
Abstract
Propagation-based x-ray phase-contrast computed tomography (PB-PCCT) images often suffer from severe ring artifacts. Ring artifacts are mainly caused by the nonuniform response of detector elements, and they can degrade image quality and affect the subsequent image processing and quantitative analyses. To remove ring artifacts in PB-PCCT images, a novel method combined sparse-domain regularized stripe decomposition (SDRSD) method with guided image filtering (GIF) was proposed. In this method, polar coordinate transformation was utilized to convert the ring artifacts to stripe artifacts. And then considering the directional and sparse properties of the stripe artifacts and the continuity characteristics of the sample, the SDRSD method was designed to remove stripe artifacts. However, for the SDRSD method, the presence of noise may destroy the edges of the stripe artifacts and lead to incomplete decomposition. Hence, a simple and efficient smoothing technique, namely GIF, was employed to overcome this issue. The simulations and real experiments demonstrated that the proposed method could effectively remove ring artifacts as well as preserve the structures and edges of the samples. In conclusion, the proposed method can serve as an effective tool to remove ring artifacts in PB-PCCT images, and it has high potential for promoting the biomedical and preclinical applications of PB-PCCT.
Collapse
Affiliation(s)
- Yimin Li
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Yuqing Zhao
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Dongjiang Ji
- School of Science, Tianjin University of Technology and Education, Tianjin 300222, People's Republic of China
| | - Wenjuan Lv
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Xiaohong Xin
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Xinyan Zhao
- Liver Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, People's Republic of China.,Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis and National Clinical Research Center of Digestive Disease, Beijing 100050, People's Republic of China
| | - Dayong Liu
- Tianjin Medical University School of Stomatology, Tianjin, 300070, People's Republic of China
| | - Zhaoguang Ouyang
- Tianjin Medical University School of Stomatology, Tianjin, 300070, People's Republic of China
| | - Chunhong Hu
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| |
Collapse
|
9
|
Mäkinen Y, Marchesini S, Foi A. Ring artifact reduction via multiscale nonlocal collaborative filtering of spatially correlated noise. JOURNAL OF SYNCHROTRON RADIATION 2021; 28:876-888. [PMID: 33949995 PMCID: PMC8127377 DOI: 10.1107/s1600577521001910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
X-ray micro-tomography systems often suffer severe ring artifacts in reconstructed images. These artifacts are caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. In this work, these streaks are modeled in the sinogram domain as additive stationary correlated noise upon logarithmic transformation. Based on this model, a streak removal procedure is proposed where the Block-Matching and 3-D (BM3D) filtering algorithm is applied across multiple scales, achieving state-of-the-art performance in both real and simulated data. Specifically, the proposed fully automatic procedure allows for attenuation of streak noise and the corresponding ring artifacts without creating major distortions common to other streak removal algorithms.
Collapse
Affiliation(s)
| | - Stefano Marchesini
- SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
| | | |
Collapse
|
10
|
Complete Ring Artifacts Reduction Procedure for Lab-Based X-ray Nano CT Systems. SENSORS 2021; 21:s21010238. [PMID: 33401506 PMCID: PMC7795796 DOI: 10.3390/s21010238] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 02/07/2023]
Abstract
In this article, we introduce a new ring artifacts reduction procedure that combines several ideas from existing methods into one complex and robust approach with a goal to overcome their individual weaknesses and limitations. The procedure differentiates two types of ring artifacts according to their cause and character in computed tomography (CT) data. Each type is then addressed separately in the sinogram domain. The novel iterative schemes based on relative total variations (RTV) were integrated to detect the artifacts. The correction process uses the image inpainting, and the intensity deviations smoothing method. The procedure was implemented in scope of lab-based X-ray nano CT with detection systems based on charge-coupled device (CCD) and scientific complementary metal-oxide-semiconductor (sCMOS) technologies. The procedure was then further tested and optimized on the simulated data and the real CT data of selected samples with different compositions. The performance of the procedure was quantitatively evaluated in terms of the artifacts’ detection accuracy, the comparison with existing methods, and the ability to preserve spatial resolution. The results show a high efficiency of ring removal and the preservation of the original sample’s structure.
Collapse
|
11
|
Yang Y, Zhang D, Yang F, Teng M, Du Y, Huang K. Post-processing method for the removal of mixed ring artifacts in CT images. OPTICS EXPRESS 2020; 28:30362-30378. [PMID: 33115040 DOI: 10.1364/oe.401088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
Ring artifacts seriously deteriorate the quality of CT images. Intensity-dependence of detector responses will result in intensity-dependent ring artifacts and time-dependence of CT hardware systems will result in time-dependent ring artifacts. However, only the intensity-dependent ring artifacts are taken into consideration in most post-processing methods. Therefore, the purpose of this study is to propose a general post-processing method, which has a significant removal effect on the intensity-dependent ring artifacts and the time-dependent ring artifacts. First in the proposed method, transform raw CT images into polar coordinate images, and the ring artifacts will manifest as stripe artifacts. Secondly, obtain structure images by smoothing the polar coordinate images and acquire texture images containing some details and stripe artifacts by subtracting the structure images from the polar coordinate images. Third, extract the stripe artifacts from the texture images using mean extraction and texture classification, and obtain the extracted ring artifacts by transforming the extracted stripe artifacts from polar coordinates into Cartesian coordinates. Finally, obtain corrected CT images by subtracting the extracted ring artifacts from the raw CT images, and iterate the corrected CT images in above steps until the ring artifacts extracted in the last iteration are weak enough. Simulation and real data show that the proposed method can remove the intensity-dependent ring artifacts and the time-dependent ring artifacts effectively while preserving image details and spatial resolution. In particular, real data prove that the method is suitable for new CT systems such as the photon counting CT.
Collapse
|
12
|
Rizvi S, Cao J, Zhang K, Hao Q. Deringing and denoising in extremely under-sampled Fourier single pixel imaging. OPTICS EXPRESS 2020; 28:7360-7374. [PMID: 32225966 DOI: 10.1364/oe.385233] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 01/16/2020] [Indexed: 06/10/2023]
Abstract
Undersampling in Fourier single pixel imaging (FSI) is often employed to reduce imaging time for real-time applications. However, the undersampled reconstruction contains ringing artifacts (Gibbs phenomenon) that occur because the high-frequency target information is not recorded. Furthermore, by employing 3-step FSI strategy (reduced measurements with low noise suppression) with a low-grade sensor (i.e., photodiode), this ringing is coupled with noise to produce unwanted artifacts, lowering image quality. To improve the imaging quality of real-time FSI, a fast image reconstruction framework based on deep convolutional autoencoder network (DCAN) is proposed. The network through context learning over FSI artifacts is capable of deringing, denoising, and recovering details in 256 × 256 images. The promising experimental results show that the proposed deep-learning-based FSI outperforms conventional FSI in terms of image quality even at very low sampling rates (1-4%).
Collapse
|