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Xin L, Zhuo W, Liu Q, Xie T, Zaidi H. Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study. Z Med Phys 2024; 34:408-418. [PMID: 36336554 DOI: 10.1016/j.zemedi.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 09/18/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. METHODS Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. RESULTS Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20-33, 33-47, 47-58, 58-69, 69-80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. CONCLUSIONS A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.
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Affiliation(s)
- Lin Xin
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Weihai Zhuo
- Institute of Radiation Medicine, Fudan University, Shanghai, China
| | - Qian Liu
- School of Biomedical Engineering, Hainan University, Haikou, China.
| | - Tianwu Xie
- Institute of Radiation Medicine, Fudan University, Shanghai, China; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva Neuroscience Center, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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Li Y, Feng J, Xiang J, Li Z, Liang D. AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1605-1618. [PMID: 38133967 DOI: 10.1109/tmi.2023.3344712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.
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Zhang C, Chen GH. Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning. Med Phys 2024; 51:946-963. [PMID: 38063251 PMCID: PMC10993302 DOI: 10.1002/mp.16880] [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: 04/15/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied to address the interior reconstruction problems for centered regions of interest (ROIs) with fixed sizes. Developing a method to enable interior tomography with arbitrarily located ROIs with nearly arbitrary ROI sizes inside a scanning field of view (FOV) remains an open question. PURPOSE To develop a new pathway to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes using a single trained deep neural network model. METHODS The method consists of two steps. First, an analytical weighted backprojection reconstruction algorithm was developed to perform domain transform from divergent fan-beam projection data to an intermediate image feature space,B ( x ⃗ ) $B(\vec{x})$ , for an arbitrary size ROI at an arbitrary location inside the FOV. Second, a supervised learning technique was developed to train a deep neural network architecture to perform deconvolution to obtain the true imagef ( x ⃗ ) $f(\vec{x})$ from the new feature spaceB ( x ⃗ ) $B(\vec{x})$ . This two-step method is referred to as Deep-Interior for convenience. Both numerical simulations and experimental studies were performed to validate the proposed Deep-Interior method. RESULTS The results showed that ROIs as small as a diameter of 5 cm could be accurately reconstructed (similarity index 0.985 ± 0.018 on internal testing data and 0.940 ± 0.025 on external testing data) at arbitrary locations within an imaging object covering a wide variety of anatomical structures of different body parts. Besides, ROIs of arbitrary size can be reconstructed by stitching small ROIs without additional training. CONCLUSION The developed Deep-Interior framework can enable interior tomographic reconstruction from divergent fan-beam projections for short-scan and super-short-scan acquisitions for small ROIs (with a diameter larger than 5 cm) at an arbitrary location inside the scanning FOV with high quantitative reconstruction accuracy.
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Affiliation(s)
- Chengzhu Zhang
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Guang-Hong Chen
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
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Li G, Huang X, Huang X, Zong Y, Luo S. PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts. ENTROPY (BASEL, SWITZERLAND) 2024; 26:101. [PMID: 38392356 PMCID: PMC10887623 DOI: 10.3390/e26020101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024]
Abstract
The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics.
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Affiliation(s)
- Guang Li
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xinhai Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Xinyu Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yuan Zong
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shouhua Luo
- School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
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Wang Z, Cui J, Liu Y, Li S, Li Z, Wang S. SHCT: segmented helical computed tomography based on multiple slant source-translation. OPTICS EXPRESS 2023; 31:27223-27238. [PMID: 37710802 DOI: 10.1364/oe.497081] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/15/2023] [Indexed: 09/16/2023]
Abstract
Micro-computed tomography (Micro-CT) is inevitably required to inspect long large objects with high resolution. It is well known that helical CT solves the so-called "long object" problem, but it requires that the measured object be strictly located in the lateral field of view (FOV). Therefore, developing a novel scanning method to extend the FOV in both the lateral and axial directions (i.e., the large helical FOV) is necessary. Recently, due to the application of linearly distributed source arrays and the characteristics of easy extension of the FOV and engineering implementation, straight-line scanning systems have attracted much attention. In this paper, we propose a segmented helical computed tomography (SHCT) based on multiple slant source-translation. SHCT can readily extend the helical FOV by adjusting the source slant translation (SST) length, pitch (or elevation of the SST trajectory), and number of scanning circles. In SHCT, each projection view is truncated laterally and axially, but the projection data set within the cylindrical FOV region is complete. To ensure reconstruction efficiency and avoid the lateral truncation, we propose a generalized backprojection-filtration (G-BPF) algorithm for SHCT approximate reconstruction. Experimental results verify the effectiveness of the proposed SHCT methods for imaging large and long objects. As the pitch decreases, the proposed SHCT methods can reconstruct competitive, high-quality volumes.
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Xia Y, Zhang L, Xing Y, Chen Z, Gao H. Generalized-equiangular geometry CT: Concept and shift-invariant FBP algorithms. Med Phys 2023; 50:5150-5165. [PMID: 37379056 DOI: 10.1002/mp.16560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/05/2023] [Accepted: 05/19/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND With advanced x-ray source and detector technologies being continuously developed, non-traditional CT geometries have been widely explored. Generalized-Equiangular Geometry CT (GEGCT) architecture, in which an x-ray source might be positioned radially far away from the focus of arced detector array that is equiangularly spaced, is of importance in many novel CT systems and designs. PURPOSE GEGCT, unfortunately, has no theoretically exact and shift-invariant analytical image reconstruction algorithm in general. In this study, to obtain fast and accurate reconstruction from GEGCT and to promote its system design and optimization, an in-depth investigation on a group of approximate Filtered Back-Projection (FBP) algorithms with a variety of weighting strategies has been conducted. METHODS The architecture of GEGCT is first presented and characterized by using a normalized-radial-offset distance (NROD). Next, shift-invariant weighted FBP-type algorithms are derived in a unified framework, with pre-filtering, filtering, and post-filtering weights, for both fixed and dynamic NROD configurations. Three viable weighting strategies are then presented including a classic one developed by Besson in the literature and two new ones generated from a curvature fitting and from an empirical formula, where all of the three weights can be expressed as certain functions of NROD. After that, an analysis of reconstruction accuracy is conducted with a wide range of NROD. Finally, the weighted FBP algorithm for GEGCT is extended to a three-dimensional form in the case of cone-beam scan with a cylindrical detector array. RESULTS Theoretical analysis and numerical study show that weights in the shift-invariant FBP algorithms can guarantee highly accurate reconstruction for GEGCT. A simulation of Shepp-Logan phantom and a GEGCT scan of lung mimicked by using a clinical lung CT dataset both demonstrate that FBP reconstructions with Besson and polynomial weights can achieve excellent image quality, with Peak Signal to Noise Ratio and Structural Similarity being at the same level as that from the standard equiangular fan-beam CT scan. Reconstruction of a cylinder object with multiple contrasts from simulated GEGCT scan with dynamic NROD is also highly consistent with fixed ones when using the Besson and polynomial weights, with root mean square error less than 7 hounsfield units, demonstrating the robustness and flexibility of the presented FBP algorithms. In terms of resolution, the direct FBP methods for GEGCT could achieve 1.35 lp/mm of spatial resolution at 10% modulation transfer functions point, higher than that of the rebinning method which can only reach 1.14 lp/mm. Moreover, 3D reconstructions of a disc phantom reveal that a greater value of NROD for GEGCT will bring less cone beam artifacts as expected. CONCLUSIONS We propose the concept of GEGCT and investigate the feasibility of using shift-invariant weighted FBP-type algorithms for reconstruction from GEGCT data without rebinning. A comprehensive analysis and phantom studies have been conducted to validate the effectiveness of proposed weighting strategies in a wide range of NROD for GEGCT with fixed and dynamic NROD.
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Affiliation(s)
- Yingxian Xia
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing, China
| | - Li Zhang
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing, China
| | - Yuxiang Xing
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing, China
| | - Zhiqiang Chen
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing, China
| | - Hewei Gao
- Department of Engineering Physics, Tsinghua University, Beijing, China
- Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing, China
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Wang W, Xia XG, He C, Ren Z, Lu J. A new weighting scheme for arc based circle cone-beam CT reconstruction. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:145-163. [PMID: 34897109 DOI: 10.3233/xst-211000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Wei Wang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiang-Gen Xia
- Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, USA
| | - Chuanjiang He
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Zemin Ren
- College of Mathematics and Physics, Chongqing University of Science and Technology, Chongqing, China
| | - Jian Lu
- Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, Guangdong, China
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La Riviere PJ, Crawford CR. From EMI to AI: a brief history of commercial CT reconstruction algorithms. J Med Imaging (Bellingham) 2021; 8:052111. [PMID: 34660842 PMCID: PMC8492478 DOI: 10.1117/1.jmi.8.5.052111] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/20/2021] [Indexed: 12/04/2022] Open
Abstract
Computed tomography was one of the first imaging modalities to require a computerized solution of an inverse problem to produce a useful image from the data acquired by the sensor hardware. The computerized solutions, which are known as image reconstruction algorithms, have thus been a critical component of every CT scanner ever sold. We review the history of commercially deployed CT reconstruction algorithms and consider the forces that led, at various points, both to innovation and to convergence around certain broadly useful algorithms. The forces include the emergence of new hardware capabilities, competitive pressures, the availability of computational power, and regulatory considerations. We consider four major historical periods and turning points. The original EMI scanner was developed with an iterative reconstruction algorithm, but an explosion of innovation coupled with rediscovery of an older literature led to the development of alternative algorithms throughout the early 1970s. Most CT vendors quickly converged on the use of the filtered back-projection (FBP) algorithm, albeit layered with a variety of proprietary corrections in both projection data and image domains to improve image quality. Innovations such as helical scanning and multi-row detectors were both enabled by and drove the development of additional applications of FBP in the 1990s and 2000s. Finally, the last two decades have seen a return of iterative reconstruction and the introduction of artificial intelligence approaches that benefit from increased computational power to reduce radiation dose and improve image quality.
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Affiliation(s)
- Patrick J La Riviere
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Xiaoqin X, Jing Z, Qi Z, Xiaodong H. Global imaging with high resolution region of interest using fusion data based on dual-field of view detection system. OPTICS EXPRESS 2021; 29:15813-15829. [PMID: 33985275 DOI: 10.1364/oe.425214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/27/2021] [Indexed: 06/12/2023]
Abstract
X-ray micro-computed tomography (CT) is an important tool for high-resolution three-dimensional imaging. But one limitation of micro-CT is the compromise between resolution and fields of view (FOV). In this paper, we develop an x-rays dual-FOV optical coupling detection (DFOCD) system for global imaging with high-resolution region of interest (ROI). In DFOCD system, the beam splitter separates lights to form two sub-optical paths, two objectives with different FOV and magnification are used in the two sub-optical paths for dual-FOV imaging. Then a data fusion method is proposed to register and fuse dual-FOV data. Reconstructed images are obtained based on back projection filtering algorithm using fusion data. Dual-FOV data are collected simultaneously in DFOCD system, which precludes artifacts in fusion images from phantom movement or changes in two acquisitions on common micro-CT, and also saves scanning time. Simulation and experimental results show that details in ROI and global morphology of phantoms are correctly reconstructed. Bright ring artifacts of ROI caused by truncated data are corrected in reconstruction images. Therefore, global imaging with high-resolution ROI of samples can be obtained by single scan experiment using DFOCD system and data fusion method, which is expected to expand the application of micro-CT.
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Deng S, Zhu Y, Zhang H, Wang Q, Zhu P, Zhang K, Zhang P. A method for material decomposition and quantification with grating based phase CT. PLoS One 2021; 16:e0245449. [PMID: 33481858 PMCID: PMC7822388 DOI: 10.1371/journal.pone.0245449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/31/2020] [Indexed: 12/20/2022] Open
Abstract
Material decomposition (MD) is an important application of computer tomography (CT). For phase contrast imaging, conventional MD methods are categorized into two types with respect to different operation sequences, i.e., “before” or “after” image reconstruction. Both categories come down to two-step methods, which have the problem of noise amplification. In this study, we incorporate both phase and absorption (PA) information into MD process, and correspondingly develop a simultaneous algebraic reconstruction technique (SART). The proposed method is referred to as phase & absorption material decomposition-SART (PAMD-SART). By iteratively solving an optimization problem, material composition and substance quantification are reconstructed directly from absorption and differential phase projections. Comparing with two-step MD, the proposed one-step method is superior in noise suppression and accurate decomposition. Numerical simulations and synchrotron radiation based experiments show that PAMD-SART outperforms the classical MD method (image-based and dual-energy CT iterative method), especially for the quantitative accuracy of material equivalent atomic number.
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Affiliation(s)
- Shiwo Deng
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
| | - Yining Zhu
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
| | - Huitao Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
- * E-mail:
| | - Qian Wang
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, United States of America
| | - Peiping Zhu
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Kai Zhang
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Peng Zhang
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing, China
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Zheng A, Gao H, Zhang L, Xing Y. A dual-domain deep learning-based reconstruction method for fully 3D sparse data helical CT. Phys Med Biol 2020; 65:245030. [PMID: 32365345 DOI: 10.1088/1361-6560/ab8fc1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Helical CT has been widely used in clinical diagnosis. In this work, we focus on a new prototype of helical CT, equipped with sparsely spaced multidetector and multi-slit collimator (MSC) in the axis direction. This type of system can not only lower radiation dose, and suppress scattering by MSC, but also cuts down the manufacturing cost of the detector. The major problem to overcome with such a system, however, is that of insufficient data for reconstruction. Hence, we propose a deep learning-based function optimization method for this ill-posed inverse problem. By incorporating a Radon inverse operator, and disentangling each slice, we significantly simplify the complexity of our network for 3D reconstruction. The network is composed of three subnetworks. Firstly, a convolutional neural network (CNN) in the projection domain is constructed to estimate missing projection data, and to convert helical projection data to 2D fan-beam projection data. This is follwed by the deployment of an analytical linear operator to transfer the data from the projection domain to the image domain. Finally, an additional CNN in the image domain is added for further image refinement. These three steps work collectively, and can be trained end to end. The overall network is trained on a simulated CT dataset based on eight patients from the American Association of Physicists in Medicine (AAPM) Low Dose CT Grand Challenge. We evaluate the trained network on both simulated datasets and clinical datasets. Extensive experimental studies have yielded very encouraging results, based on both visual examination and quantitative evaluation. These results demonstrate the effectiveness of our method and its potential for clinical usage. The proposed method provides us with a new solution for a fully 3D ill-posed problem.
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Affiliation(s)
- Ao Zheng
- Department of Engineering Physics, Tsinghua University, Beijing 100084, People's Republic of China. Key Laboratory of Particle & Radiation Imaging, Tsinghua University, Ministry of Education, Beijing 100084, People's Republic of China
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Han Y, Kim J, Ye JC. Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3571-3582. [PMID: 32746105 DOI: 10.1109/tmi.2020.3000341] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approach for accurate conebeam artifact removal. In particular, our deep network, designed on the differentiated backprojection domain, performs a data-driven inversion of an ill-posed deconvolution problem associated with the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined using a spectral blending technique to minimize the spectral leakage. Experimental results under various conditions confirmed that our method generalizes well and outperforms the existing iterative methods despite significantly reduced runtime complexity.
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Wu P, Sheth N, Sisniega A, Uneri A, Han R, Vijayan R, Vagdargi P, Kreher B, Kunze H, Kleinszig G, Vogt S, Lo SF, Theodore N, Siewerdsen JH. C-arm orbits for metal artifact avoidance (MAA) in cone-beam CT. Phys Med Biol 2020; 65:165012. [PMID: 32428891 PMCID: PMC8650760 DOI: 10.1088/1361-6560/ab9454] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy-often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol-viz., the C-arm source-detector orbit-that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2-6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%-70% and 'blooming' artifacts (apparent width of the screw shaft) by 20-45%. Non-circular orbits defined by MAA achieved a further ∼46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits-either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm-exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts.
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Affiliation(s)
- P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
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Han Y, Ye JC. One network to solve all ROIs: Deep learning CT for any ROI using differentiated backprojection. Med Phys 2020; 46:e855-e872. [PMID: 31811795 DOI: 10.1002/mp.13631] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 05/16/2019] [Accepted: 05/21/2019] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Computed tomography for the reconstruction of region of interest (ROI) has advantages in reducing the x-ray dose and the use of a small detector. However, standard analytic reconstruction methods such as filtered back projection (FBP) suffer from severe cupping artifacts, and existing model-based iterative reconstruction methods require extensive computations. Recently, we proposed a deep neural network to learn the cupping artifacts, but the network was not generalized well for different ROIs due to the singularities in the corrupted images. Therefore, there is an increasing demand for a neural network that works well for any ROI size. METHOD Two types of neural networks are designed. The first type learns ROI size-specific cupping artifacts from FBP images, whereas the second type network is for the inversion of the truncated Hilbert transform from the truncated differentiated backprojection (DBP) data. Their generalizabilities for different ROI sizes, pixel sizes, detector pitch and starting angles for a short scan are then investigated. RESULTS Experimental results show that the new type of neural networks significantly outperform existing iterative methods for all ROI sizes despite significantly lower runtime complexity. In addition, performance improvement is consistent across different acquisition scenarios. CONCLUSIONS Since the proposed method consistently surpasses existing methods, it can be used as a general CT reconstruction engine for many practical applications without compromising possible detector truncation.
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Affiliation(s)
- Yoseob Han
- BISPL - Bio Imaging, Signal Processing, and Learning Laboratory, Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
| | - Jong Chul Ye
- BISPL - Bio Imaging, Signal Processing, and Learning Laboratory, Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea
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15
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Shi L, Liu B, Yu H, Wei C, Wei L, Zeng L, Wang G. Review of CT image reconstruction open source toolkits. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:619-639. [PMID: 32390648 DOI: 10.3233/xst-200666] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Computed tomography (CT) has been widely applied in medical diagnosis, nondestructive evaluation, homeland security, and other science and engineering applications. Image reconstruction is one of the core CT imaging technologies. In this review paper, we systematically reviewed the currently publicly available CT image reconstruction open source toolkits in the aspects of their environments, object models, imaging geometries, and algorithms. In addition to analytic and iterative algorithms, deep learning reconstruction networks and open codes are also reviewed as the third category of reconstruction algorithms. This systematic summary of the publicly available software platforms will help facilitate CT research and development.
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Affiliation(s)
- Liu Shi
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Baodong Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Cunfeng Wei
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Long Wei
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
- School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Zeng
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China
| | - Ge Wang
- Biomedical Imaging Center, AI-based X-ray Imaging System (AXIS) Lab, Rensselaer Polytechnic Institute, Troy, NY, USA
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16
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Clackdoyle R, Noo F. Quantification of Tomographic Incompleteness in Cone-Beam Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 4:63-80. [PMID: 33506155 DOI: 10.1109/trpms.2019.2918222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
For situations of cone-beam scanning where the measurements are incomplete, we propose a method to quantify the severity of the missing information at each voxel. This incompleteness metric is geometric; it uses only the relative locations of all cone-beam vertices with respect to the voxel in question, and does not apply global information such as the object extent or the pattern of incompleteness of other voxels. The values are non-negative, with zero indicating "least incompleteness," i.e. minimal danger of incompleteness artifacts. The incompleteness value can be related to the severity of the potential reconstruction artifact at the voxel location, independent of reconstruction algorithm. We performed a computer simulation of x-ray sources along a circular trajectory, and used small multi-disk test-objects to examine the local effects of data incompleteness. The observed behavior of the reconstructed test-objects quantitatively matched the precalculated incompleteness values. A second simulation of a hypothetical SPECT breast imaging system used only 12 pinholes. Reconstructions were performed using analytic and iterative methods, and five reconstructed test-objects matched the behavior predicted by the incompleteness model. The model is based on known sufficiency conditions for data incompleteness, and provides strong predictive guidance for what can go wrong with incomplete cone-beam data.
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Affiliation(s)
| | - Frédéric Noo
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, USA
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17
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Tang S, Huang K, Cheng Y, Niu T, Tang X. Three-Dimensional Weighting in Cone Beam FBP Reconstruction and Its Transformation Over Geometries. IEEE Trans Biomed Eng 2019; 65:1235-1244. [PMID: 29787996 DOI: 10.1109/tbme.2017.2711478] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
GOALS With substantially increased number of detector rows in multidetector CT (MDCT), axial scan with projection data acquired along a circular source trajectory has become the method-of-choice in increasing clinical applications. Recognizing the practical relevance of image reconstruction directly from the projection data acquired in the native cone beam (CB) geometry, especially in scenarios wherein the most achievable in-plane resolution is desirable, we present a three-dimensional (3-D) weighted CB-FBP algorithm in such geometry in this paper. METHODS We start the algorithm's derivation in the cone-parallel geometry. Via changing of variables, taking the Jacobian into account and making heuristic and empirical assumptions, we arrive at the formulas for 3-D weighted image reconstruction in the native CB geometry. RESULTS Using the projection data simulated by computer and acquired by an MDCT scanner, we evaluate and verify performance of the proposed algorithm for image reconstruction directly from projection data acquired in the native CB geometry. CONCLUSION The preliminary data show that the proposed algorithm performs as well as the 3-D weighted CB-FBP algorithm in the cone-parallel geometry. SIGNIFICANCE The proposed algorithm is anticipated to find its utility in extensive clinical and preclinical applications wherein the reconstruction of images in the native CB geometry, i.e., the geometry for data acquisition, is of relevance.
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18
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Min J, Pua R, Kim C, Park M, Lee J, Ye SJ, Cho S. A weighted rebinned backprojection-filtration algorithm from partially beam-blocked data for a single-scan cone-beam CT with hybrid type scatter correction. Med Phys 2019; 46:1182-1197. [PMID: 30592313 DOI: 10.1002/mp.13365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 12/17/2018] [Accepted: 12/19/2018] [Indexed: 01/11/2023] Open
Abstract
PURPOSE Scatter contamination constitutes a dominant source of degradation of image quality in cone-beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone-beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection-filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. METHODS The proposed method uses a beam-blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. This design allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement-based and a convolution-based method. We first estimated the scatter signal using a measurement-based method in the partially beam-blocked regions, and then optimized the fitting parameters of a convolution-kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. RESULTS The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam-blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution-based scatter correction. CONCLUSIONS The proposed method that enjoys the advantages of both measurement-based and convolution-based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT.
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Affiliation(s)
- Jonghwan Min
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Rizza Pua
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Changhwan Kim
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Miran Park
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Jongha Lee
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.,Medical Imaging R&D Group, Health&Medical Equipment Business, Samsung Electronics, Suwon, 16677, Republic of Korea
| | - Sung-Joon Ye
- Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, 16229, Republic of Korea
| | - Seungryong Cho
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology(KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.,KAIST Institutes for Health Science and Technology & for IT Convergence, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
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19
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Nam H, Guo M, Yu H, Lee K, Li R, Han B, Xing L, Lee R, Gao H. Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT. PLoS One 2019; 14:e0210410. [PMID: 30633760 PMCID: PMC6329516 DOI: 10.1371/journal.pone.0210410] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 12/21/2018] [Indexed: 11/18/2022] Open
Abstract
In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.
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Affiliation(s)
- Haewon Nam
- Department of Liberal Arts, Hongik University, Sejong, Republic of Korea
| | - Minghao Guo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, Massachusetts 01854, United States of America
| | - Keumsil Lee
- Department of Radiology, Stanford University, Stanford, California 94305, United States of America
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California 94305, United States of America
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Stanford, California 94305, United States of America
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, California 94305, United States of America
| | - Rena Lee
- Department of Radiation Oncology, Ewha Womans University, Seoul, Korea
| | - Hao Gao
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, United States of America
- * E-mail:
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20
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Uneri A, Zhang X, Yi T, Stayman JW, Helm PA, Theodore N, Siewerdsen JH. Image quality and dose characteristics for an O-arm intraoperative imaging system with model-based image reconstruction. Med Phys 2018; 45:4857-4868. [PMID: 30180274 DOI: 10.1002/mp.13167] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To assess the imaging performance and radiation dose characteristics of the O-arm CBCT imaging system (Medtronic Inc., Littleton MA) and demonstrate the potential for improved image quality and reduced dose via model-based image reconstruction (MBIR). METHODS Two main studies were performed to investigate previously unreported characteristics of the O-arm system. First is an investigation of dose and 3D image quality achieved with filtered back-projection (FBP) - including enhancements in geometric calibration, handling of lateral truncation and detector saturation, and incorporation of an isotropic apodization filter. Second is implementation of an MBIR algorithm based on Huber-penalized likelihood estimation (PLH) and investigation of image quality improvement at reduced dose. Each study involved measurements in quantitative phantoms as a basis for analysis of contrast-to-noise ratio and spatial resolution as well as imaging of a human cadaver to test the findings under realistic imaging conditions. RESULTS View-dependent calibration of system geometry improved the accuracy of reconstruction as quantified by the full-width at half maximum of the point-spread function - from 0.80 to 0.65 mm - and yielded subtle but perceptible improvement in high-contrast detail of bone (e.g., temporal bone). Standard technique protocols for the head and body imparted absorbed dose of 16 and 18 mGy, respectively. For low-to-medium contrast (<100 HU) imaging at fixed spatial resolution (1.3 mm edge-spread function) and fixed dose (6.7 mGy), PLH improved CNR over FBP by +48% in the head and +35% in the body. Evaluation at different dose levels demonstrated 30% increase in CNR at 62% of the dose in the head and 90% increase in CNR at 50% dose in the body. CONCLUSIONS A variety of improvements in FBP implementation (geometric calibration, truncation and saturation effects, and isotropic apodization) offer the potential for improved image quality and reduced radiation dose on the O-arm system. Further gains are possible with MBIR, including improved soft-tissue visualization, low-dose imaging protocols, and extension to methods that naturally incorporate prior information of patient anatomy and/or surgical instrumentation.
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Affiliation(s)
- A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - X Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - T Yi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - J W Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - P A Helm
- Medtronic Inc., Littleton, MA, 01460, USA
| | - N Theodore
- Department of Neurosurgery, Johns Hopkins Medical Institute, Baltimore, MD, 21287, USA
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.,Department of Neurosurgery, Johns Hopkins Medical Institute, Baltimore, MD, 21287, USA
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21
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Tang X, Krupinski EA, Xie H, Stillman AE. On the data acquisition, image reconstruction, cone beam artifacts, and their suppression in axial MDCT and CBCT - A review. Med Phys 2018; 45. [PMID: 30019342 DOI: 10.1002/mp.13095] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 06/12/2018] [Accepted: 07/05/2018] [Indexed: 12/12/2022] Open
Abstract
PURPOSE In the clinic, computed tomography (CT) has evolved into an essential modality for diagnostic imaging by multidetector row CT (MDCT) and image guided intervention by cone beam CT (CBCT). Recognizing the increasing importance of axial MDCT/CBCT in clinical and preclinical applications, and the existence of CB artifacts in MDCT/CBCT images, we provide a review of CB artifacts' root causes, rendering mechanisms and morphology, and possible solutions for elimination and/or reduction of the artifacts. METHODS By examining the null space in Radon and Fourier domain, the root cause of CB artifacts (i.e., data insufficiency) in axial MDCT/CBCT is analytically investigated, followed by a review of the data sufficiency conditions and the "circle +" source trajectories. The rendering mechanisms and morphology of CB artifacts in axial MDCT/CBCT and their special cases (e.g., half/short scan and full scan with latitudinally displaced detector) are then analyzed, followed by a survey of the potential solutions to suppress the artifacts. The phenomenon of imaged zone indention and its variation over FBP, BPF/DBPF, two-pass and iterative CB reconstruction algorithms and/or schemes are discussed in detail. RESULTS An interdomain examination of the null space provides an insightful understanding of the root cause of CB artifacts in axial MDCT/CBCT. The decomposition of CB artifacts rendering mechanisms facilitates understanding of the artifacts' behavior under different conditions and the potential solutions to suppress them. An inspection of the imaged zone intention phenomenon provides guidance on the design and implementation of CB image reconstruction algorithms and schemes for CB artifacts suppression in axial MDCT/CBCT. CONCLUSIONS With increasing importance of axial MDCT/CBCT in clinical and preclinical applications, this review article can update the community with in-depth information and clarification on the latest progress in dealing with CB artifacts and thus increase clinical/preclinical confidence.
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Affiliation(s)
- Xiangyang Tang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Dr., C-5018, Atlanta, GA, 30322, USA
| | - Elizabeth A Krupinski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Dr., C-5018, Atlanta, GA, 30322, USA
| | - Huiqiao Xie
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Dr., C-5018, Atlanta, GA, 30322, USA
| | - Arthur E Stillman
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Dr., C-5018, Atlanta, GA, 30322, USA
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Luo S, Shen T, Sun Y, Li J, Li G, Tang X. Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution. Phys Med Biol 2018; 63:075006. [PMID: 29509149 DOI: 10.1088/1361-6560/aab46f] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.
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Affiliation(s)
- Shouhua Luo
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, People's Republic of China
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23
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Wu W, Yu H, Cong W, Liu F. Theoretically exact backprojection filtration algorithm for multi-segment linear trajectory. Phys Med Biol 2018; 63:015037. [PMID: 29053104 DOI: 10.1088/1361-6560/aa9501] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A theoretically exact backprojection filtration algorithm is proved and implemented for image reconstruction from a multi-segment linear trajectory assuming fan-beam geometry. The reconstruction formula is based on a concept of linear PI-line (L-PI) proposed in our previous work. The proof is completed in two consecutive steps. In the first step, it is proved that theoretically exact image reconstruction can be obtained on an arbitrary L-PI line from an infinite straight-line trajectory. In the second step, it is shown that accurate image reconstruction can be achieved from a multi-segment line trajectory by introducing a weight function to deal with the data redundancy. Numerical implementation and simulation results validate the correctness of our theoretical results.
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Affiliation(s)
- Weiwen Wu
- Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, People's Republic of China
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24
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Clackdoyle R, Noo F, Momey F, Desbat L, Rit S. Accurate Transaxial Region-of-Interest Reconstruction in Helical CT? IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2017. [DOI: 10.1109/trpms.2017.2706196] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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25
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Wu W, Yu H, Wang S, Liu F. BPF-type region-of-interest reconstruction for parallel translational computed tomography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2017; 25:487-504. [PMID: 28157118 DOI: 10.3233/xst-16208] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances.
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Affiliation(s)
- Weiwen Wu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
- Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China
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Dang H, Stayman JW, Sisniega A, Zbijewski W, Xu J, Wang X, Foos DH, Aygun N, Koliatsos VE, Siewerdsen JH. Multi-resolution statistical image reconstruction for mitigation of truncation effects: application to cone-beam CT of the head. Phys Med Biol 2016; 62:539-559. [PMID: 28033118 DOI: 10.1088/1361-6560/aa52b8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
A prototype cone-beam CT (CBCT) head scanner featuring model-based iterative reconstruction (MBIR) has been recently developed and demonstrated the potential for reliable detection of acute intracranial hemorrhage (ICH), which is vital to diagnosis of traumatic brain injury and hemorrhagic stroke. However, data truncation (e.g. due to the head holder) can result in artifacts that reduce image uniformity and challenge ICH detection. We propose a multi-resolution MBIR method with an extended reconstruction field of view (RFOV) to mitigate truncation effects in CBCT of the head. The image volume includes a fine voxel size in the (inner) nontruncated region and a coarse voxel size in the (outer) truncated region. This multi-resolution scheme allows extension of the RFOV to mitigate truncation effects while introducing minimal increase in computational complexity. The multi-resolution method was incorporated in a penalized weighted least-squares (PWLS) reconstruction framework previously developed for CBCT of the head. Experiments involving an anthropomorphic head phantom with truncation due to a carbon-fiber holder were shown to result in severe artifacts in conventional single-resolution PWLS, whereas extending the RFOV within the multi-resolution framework strongly reduced truncation artifacts. For the same extended RFOV, the multi-resolution approach reduced computation time compared to the single-resolution approach (viz. time reduced by 40.7%, 83.0%, and over 95% for an image volume of 6003, 8003, 10003 voxels). Algorithm parameters (e.g. regularization strength, the ratio of the fine and coarse voxel size, and RFOV size) were investigated to guide reliable parameter selection. The findings provide a promising method for truncation artifact reduction in CBCT and may be useful for other MBIR methods and applications for which truncation is a challenge.
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Affiliation(s)
- Hao Dang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD 21205, USA
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27
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Zhang H, Li L, Yan B, Wang L, Cai A, Hu G. A two-step filtering-based iterative image reconstruction method for interior tomography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2016; 24:733-747. [PMID: 27392828 DOI: 10.3233/xst-160584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The optimization-based method that utilizes the additional sparse prior of region-of-interest (ROI) image, such as total variation, has been the subject of considerable research in problems of interior tomography reconstruction. One challenge for optimization-based iterative ROI image reconstruction is to build the relationship between ROI image and truncated projection data. When the reconstruction support region is smaller than the original object, an unsuitable representation of data fidelity may lead to bright truncation artifacts in the boundary region of field of view. In this work, we aim to develop an iterative reconstruction method to suppress the truncation artifacts and improve the image quality for direct ROI image reconstruction. A novel reconstruction approach is proposed based on an optimization problem involving a two-step filtering-based data fidelity. Data filtering is achieved in two steps: the first takes the derivative of projection data; in the second step, Hilbert filtering is applied in the differentiated data. Numerical simulations and real data reconstructions have been conducted to validate the new reconstruction method. Both qualitative and quantitative results indicate that, as theoretically expected, the proposed method brings reasonable performance in suppressing truncation artifacts and preserving detailed features. The presented local reconstruction method based on the two-step filtering strategy provides a simple and efficient approach for the iterative reconstruction from truncated projections.
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Gao H. Fused analytical and iterative reconstruction (AIR) via modified proximal forward–backward splitting: a FDK-based iterative image reconstruction example for CBCT. Phys Med Biol 2016; 61:7187-7204. [DOI: 10.1088/0031-9155/61/19/7187] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Pan X, Zou Y, Xia D, Sidky EY. Reconstruction of 3D Regions-of-Interest from Data in Reduced Helical Cone-beam Scans. Technol Cancer Res Treat 2016; 4:143-50. [PMID: 15773783 DOI: 10.1177/153303460500400203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The suffciency conditions are derived for exact image reconstruction of a 3D ROI from projections acquired with a reduced helical scan over an angular range considerably smaller than that required by image reconstruction in, e.g., the conventional long object problem, for which the scanned angular range is often more than 2π. ROI reconstruction is investigated by a recently developed filtered-backprojection algorithm that can make use of data acquired with a reduced helical scan. Preliminary numerical studies demonstrate and validate the ROI reconstruction. This work may have significant practical implications because a reduced scan in CT often translates to reduced motion artifacts and reduced radiation dose delivered to the subject.
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Affiliation(s)
- Xiaochuan Pan
- Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, Chicago, IL 60637, USA.
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Hu Z, Liu Q, Zhang N, Zhang Y, Peng X, Wu PZ, Zheng H, Liang D. Image reconstruction from few-view CT data by gradient-domain dictionary learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2016; 24:627-638. [PMID: 27232200 DOI: 10.3233/xst-160579] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
BACKGROUND Decreasing the number of projections is an effective way to reduce the radiation dose exposed to patients in medical computed tomography (CT) imaging. However, incomplete projection data for CT reconstruction will result in artifacts and distortions. OBJECTIVE In this paper, a novel dictionary learning algorithm operating in the gradient-domain (Grad-DL) is proposed for few-view CT reconstruction. Specifically, the dictionaries are trained from the horizontal and vertical gradient images, respectively and the desired image is reconstructed subsequently from the sparse representations of both gradients by solving the least-square method. METHODS Since the gradient images are sparser than the image itself, the proposed approach could lead to sparser representations than conventional DL methods in the image-domain, and thus a better reconstruction quality is achieved. RESULTS To evaluate the proposed Grad-DL algorithm, both qualitative and quantitative studies were employed through computer simulations as well as real data experiments on fan-beam and cone-beam geometry. CONCLUSIONS The results show that the proposed algorithm can yield better images than the existing algorithms.
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Affiliation(s)
- Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Department of Biomedical Engineering, University of California, Davis, CA, USA
- Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yunwan Zhang
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xi Peng
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China
| | - Peter Z Wu
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China
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Chen GH, Li Y. Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT. Med Phys 2016; 42:4698-707. [PMID: 26233197 DOI: 10.1118/1.4926430] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In x-ray computed tomography (CT), a violation of the Tuy data sufficiency condition leads to limited-view artifacts. In some applications, it is desirable to use data corresponding to a narrow temporal window to reconstruct images with reduced temporal-average artifacts. However, the need to reduce temporal-average artifacts in practice may result in a violation of the Tuy condition and thus undesirable limited-view artifacts. In this paper, the authors present a new iterative reconstruction method, synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON), to eliminate limited-view artifacts using data acquired within an ultranarrow temporal window that severely violates the Tuy condition. METHODS In time-resolved contrast enhanced CT acquisitions, image contrast dynamically changes during data acquisition. Each image reconstructed from data acquired in a given temporal window represents one time frame and can be denoted as an image vector. Conventionally, each individual time frame is reconstructed independently. In this paper, all image frames are grouped into a spatial-temporal image matrix and are reconstructed together. Rather than the spatial and/or temporal smoothing regularizers commonly used in iterative image reconstruction, the nuclear norm of the spatial-temporal image matrix is used in SMART-RECON to regularize the reconstruction of all image time frames. This regularizer exploits the low-dimensional structure of the spatial-temporal image matrix to mitigate limited-view artifacts when an ultranarrow temporal window is desired in some applications to reduce temporal-average artifacts. Both numerical simulations in two dimensional image slices with known ground truth and in vivo human subject data acquired in a contrast enhanced cone beam CT exam have been used to validate the proposed SMART-RECON algorithm and to demonstrate the initial performance of the algorithm. Reconstruction errors and temporal fidelity of the reconstructed images were quantified using the relative root mean square error (rRMSE) and the universal quality index (UQI) in numerical simulations. The performance of the SMART-RECON algorithm was compared with that of the prior image constrained compressed sensing (PICCS) reconstruction quantitatively in simulations and qualitatively in human subject exam. RESULTS In numerical simulations, the 240(∘) short scan angular span was divided into four consecutive 60(∘) angular subsectors. SMART-RECON enables four high temporal fidelity images without limited-view artifacts. The average rRMSE is 16% and UQIs are 0.96 and 0.95 for the two local regions of interest, respectively. In contrast, the corresponding average rRMSE and UQIs are 25%, 0.78, and 0.81, respectively, for the PICCS reconstruction. Note that only one filtered backprojection image can be reconstructed from the same data set with an average rRMSE and UQIs are 45%, 0.71, and 0.79, respectively, to benchmark reconstruction accuracies. For in vivo contrast enhanced cone beam CT data acquired from a short scan angular span of 200(∘), three 66(∘) angular subsectors were used in SMART-RECON. The results demonstrated clear contrast difference in three SMART-RECON reconstructed image volumes without limited-view artifacts. In contrast, for the same angular sectors, PICCS cannot reconstruct images without limited-view artifacts and with clear contrast difference in three reconstructed image volumes. CONCLUSIONS In time-resolved CT, the proposed SMART-RECON method provides a new method to eliminate limited-view artifacts using data acquired in an ultranarrow temporal window, which corresponds to approximately 60(∘) angular subsectors.
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Affiliation(s)
- Guang-Hong Chen
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705 and Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin 53792
| | - Yinsheng Li
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705
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Bendory T, Feuer A. Sparse sampling in helical cone-beam CT perfect reconstruction algorithms. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2016; 24:389-405. [PMID: 27257877 DOI: 10.3233/xst-160553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In the current paper we consider the Helical Cone Beam CT. This scanning method exposes the patient to large quantities of radiation and results in very large amounts of data being collected and stored. Both these facts are prime motivators for the development of an efficient, reduced rate, sampling pattern. We calculate bounds on the support in the frequency domain of the collected data and use these to suggest an efficient sampling pattern. A reduction of up to a factor of 2 in sampling rate is suggested. Indeed, we show that reconstruction quality is not affected by this reduction of sampling rates.
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Wang AS, Stayman JW, Otake Y, Vogt S, Kleinszig G, Siewerdsen JH. Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method. Med Phys 2016; 42:2699-708. [PMID: 25979068 DOI: 10.1118/1.4914378] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
PURPOSE To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours. METHODS The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed. RESULTS Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method. CONCLUSIONS The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.
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Affiliation(s)
- Adam S Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - J Webster Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Yoshito Otake
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - Sebastian Vogt
- Siemens Healthcare XP Division, Erlangen, 91052, Germany
| | | | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
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Chen P, Han Y, Gui Z. CT Scanning Imaging Method Based on a Spherical Trajectory. PLoS One 2016; 11:e0149779. [PMID: 26934744 PMCID: PMC4775072 DOI: 10.1371/journal.pone.0149779] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 02/04/2016] [Indexed: 11/18/2022] Open
Abstract
In industrial computed tomography (CT), the mismatch between the X-ray energy and the effective thickness makes it difficult to ensure the integrity of projection data using the traditional scanning model, because of the limitations of the object's complex structure. So, we have developed a CT imaging method that is based on a spherical trajectory. Considering an unrestrained trajectory for iterative reconstruction, an iterative algorithm can be used to realise the CT reconstruction of a spherical trajectory for complete projection data only. Also, an inclined circle trajectory is used as an example of a spherical trajectory to illustrate the accuracy and feasibility of this new scanning method. The simulation results indicate that the new method produces superior results for a larger cone-beam angle, a limited angle and tabular objects compared with traditional circle trajectory scanning.
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Affiliation(s)
- Ping Chen
- National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China
- Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China
- * E-mail:
| | - Yan Han
- National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China
- Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China
| | - Zhiguo Gui
- National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China
- Key Laboratory of Instrumentation Science & Dynamic Measurement, North University of China, Taiyuan 030051, China
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Yu Z, Lauritsch G, Dennerlein F, Mao Y, Hornegger J, Noo F. Extended ellipse-line-ellipse trajectory for long-object cone-beam imaging with a mounted C-arm system. Phys Med Biol 2016; 61:1829-51. [PMID: 26854687 DOI: 10.1088/0031-9155/61/4/1829] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Recent reports show that three-dimensional cone-beam (CB) imaging with a floor-mounted (or ceiling-mounted) C-arm system has become a valuable tool in interventional radiology. Currently, a circular short scan is used for data acquisition, which inevitably yields CB artifacts and a short coverage in the direction of the patient table. To overcome these two limitations, a more sophisticated data acquisition geometry is needed. This geometry should be complete in terms of Tuy's condition and should allow continuous scanning, while being compatible with the mechanical constraints of mounted C-arm systems. Additionally, the geometry should allow accurate image reconstruction from truncated data. One way to ensure such a feature is to adopt a trajectory that provides full R-line coverage within the field-of-view (FOV). An R-line is any segment of line that connects two points on a source trajectory, and the R-line coverage is the set of points that belong to an R-line. In this work, we propose a novel geometry called the extended ellipse-line-ellipse (ELE) for long-object imaging with a mounted C-arm system. This trajectory is built from modules consisting of two elliptical arcs connected by a line. We demonstrate that the extended ELE can be configured in many ways so that full R-line coverage is guaranteed. Both tight and relaxed parametric settings are presented. All results are supported by extensive mathematical proofs provided in appendices. Our findings make the extended ELE trajectory attractive for axially-extended FOV imaging in interventional radiology.
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Affiliation(s)
- Zhicong Yu
- Department of Radiology, University of Utah, Salt Lake City, USA. Department of Radiology, Mayo Clinic, Rochester, USA
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Tang S, Tang X. Axial Cone-Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering. IEEE Trans Biomed Eng 2015; 63:1895-1903. [PMID: 26660512 DOI: 10.1109/tbme.2015.2504484] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
GOAL The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone-beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane, determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. METHODS The solution is an integration of three-dimensional (3-D) weighted axial CB-BPF/DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting the reconstruction accuracy, and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate the performance of the proposed algorithm. RESULTS Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3-D weighted axial CB-BPF/DBPF algorithm located at off-central planes. CONCLUSION Integrated with orthogonal butterfly filtering, the 3-D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3-D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. SIGNIFICANCE The proposed 3-D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications.
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Tang S, Tang X. Radial differential interior tomography and its image reconstruction with differentiated backprojection and projection onto convex sets. Med Phys 2014; 40:091914. [PMID: 24007165 DOI: 10.1118/1.4812676] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
PURPOSE Interior tomography has been recognized as one of the most effective approaches in computed tomography (CT) to reduce radiation dose rendered to patients. In this work, the authors propose and evaluate an imaging method of radial differential interior tomography. METHODS In interior tomography, an x-ray beam is collimated to only irradiate the region of interest (ROI) with suspected lesions while the surrounding area∕volume of normal tissues∕organs is spared. In the proposed imaging method of radial differential interior tomography, the outcome is a ROI image that has gone through a radial differential filtering. The image reconstruction algorithm for the radial differential interior tomography is kept in the fashion of differentiated backprojection and projection onto convex sets, but the required a priori knowledge in a small round area becomes zero and may be more readily available in practice. RESULTS Using the projection data simulated by computer and acquired by CT scanner, the authors evaluate and verify the performance of the proposed radial differential interior tomography method and its associated image reconstruction algorithm. The preliminary results show that the proposed imaging method can generate an image that is the radial differentiation of a conventional tomographic image and is robust over noise that inevitably exist in practice. CONCLUSIONS It is believed that the proposed imaging method may find its utility in advanced clinical applications wherein a ROI-based image processing and analysis is required for lesion visualization, characterization, and diagnosis.
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Affiliation(s)
- Shaojie Tang
- Imaging and Medical Physics, Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Drive, C-5018, Atlanta, Georgia 30322, USA
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P. Gopi V, Palanisamy P, A. Wahid K, Babyn P, Cooper D. Micro-CT image reconstruction based on alternating direction augmented Lagrangian method and total variation. Comput Med Imaging Graph 2013; 37:419-29. [DOI: 10.1016/j.compmedimag.2013.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2012] [Revised: 07/15/2013] [Accepted: 08/27/2013] [Indexed: 11/16/2022]
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Fahimian BP, Zhao Y, Huang Z, Fung R, Mao Y, Zhu C, Khatonabadi M, DeMarco JJ, Osher SJ, McNitt-Gray MF, Miao J. Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction. Med Phys 2013; 40:031914. [PMID: 23464329 DOI: 10.1118/1.4791644] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
PURPOSE A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. METHODS EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. RESULTS Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. CONCLUSIONS A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.
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Affiliation(s)
- Benjamin P Fahimian
- Department of Radiation Oncology, Stanford University, Stanford, California 94305, USA
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Ye Y, Yu H, Wang G. Gel'fand-Graev's reconstruction formula in the 3D real space. Med Phys 2013; 38 Suppl 1:S69. [PMID: 21978119 DOI: 10.1118/1.3577765] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Gel'fand and Graev performed classical work on the inversion of integral transforms in different spaces [Gel'fand and Graev, Funct. Anal. Appl. 25(1) 1-5 (1991)]. This paper discusses their key results for further research and development. METHODS The Gel'fand-Graev inversion formula reveals a fundamental relationship between projection data and the Hilbert transform of an image to be reconstructed. This differential backprojection (DBP)∕backprojection filtration (BPF) approach was rediscovered in the CT field, and applied in important applications such as reconstruction from truncated projections, interior tomography, and limited-angle tomography. Here the authors present the Gel'fand-Graev inversion formula in a 3D setting assuming the 1D x-ray transform. RESULTS The pseudodifferential operator is a powerful theoretical tool. There is a fundamental mathematical link between the Gel'fand-Graev formula and the DBP (or BPF) approach in the case of the 1D x-ray transform in a 3D real space. CONCLUSIONS This paper shows the power of mathematics for tomographic imaging and the value of a pure theoretical finding, which may appear quite irrelevant to daily healthcare at the first glance.
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Affiliation(s)
- Yangbo Ye
- Department of Mathematics, University of Iowa, Iowa City, Iowa 52242, USA.
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Jang KE, Lee J, Sung Y, Lee S. Information-theoretic discrepancy based iterative reconstructions (IDIR) for polychromatic x-ray tomography. Med Phys 2013; 40:091908. [PMID: 24007159 DOI: 10.1118/1.4816945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE X-ray photons generated from a typical x-ray source for clinical applications exhibit a broad range of wavelengths, and the interactions between individual particles and biological substances depend on particles' energy levels. Most existing reconstruction methods for transmission tomography, however, neglect this polychromatic nature of measurements and rely on the monochromatic approximation. In this study, we developed a new family of iterative methods that incorporates the exact polychromatic model into tomographic image recovery, which improves the accuracy and quality of reconstruction. METHODS The generalized information-theoretic discrepancy (GID) was employed as a new metric for quantifying the distance between the measured and synthetic data. By using special features of the GID, the objective function for polychromatic reconstruction which contains a double integral over the wavelength and the trajectory of incident x-rays was simplified to a paraboloidal form without using the monochromatic approximation. More specifically, the original GID was replaced with a surrogate function with two auxiliary, energy-dependent variables. Subsequently, the alternating minimization technique was applied to solve the double minimization problem. Based on the optimization transfer principle, the objective function was further simplified to the paraboloidal equation, which leads to a closed-form update formula. Numerical experiments on the beam-hardening correction and material-selective reconstruction were conducted to compare and assess the performance of conventional methods and the proposed algorithms. RESULTS The authors found that the GID determines the distance between its two arguments in a flexible manner. In this study, three groups of GIDs with distinct data representations were considered. The authors demonstrated that one type of GIDs that comprises "raw" data can be viewed as an extension of existing statistical reconstructions; under a particular condition, the GID is equivalent to the Poisson log-likelihood function. The newly proposed GIDs of the other two categories consist of log-transformed measurements, which have the advantage of imposing linearized penalties over multiple discrepancies. For all proposed variants of the GID, the aforementioned strategy was used to obtain a closed-form update equation. Even though it is based on the exact polychromatic model, the derived algorithm bears a structural resemblance to conventional methods based on the monochromatic approximation. The authors named the proposed approach as information-theoretic discrepancy based iterative reconstructions (IDIR). In numerical experiments, IDIR with raw data converged faster than previously known statistical reconstruction methods. IDIR with log-transformed data exhibited superior reconstruction quality and faster convergence speed compared with conventional methods and their variants. CONCLUSIONS The authors' new framework for tomographic reconstruction allows iterative inversion of the polychromatic data model. The primary departure from the traditional iterative reconstruction was the employment of the GID as a new metric for quantifying the inconsistency between the measured and synthetic data. The proposed methods outperformed not only conventional methods based on the monochromatic approximation but also those based on the polychromatic model. The authors have observed that the GID is a very flexible means to design an objective function for iterative reconstructions. Hence, the authors expect that the proposed IDIR framework will also be applicable to other challenging tasks.
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Affiliation(s)
- Kwang Eun Jang
- Advanced Media Laboratory, Samsung Advanced Institute of Technology (SAIT), San 14, Nongseo Dong, Giheung Gu, Yongin, Gyeonggi 446-712, Republic of Korea.
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Li J, Sun Y, Zhu P. A theoretically exact reconstruction algorithm for helical cone-beam differential phase-contrast computed tomography. Phys Med Biol 2013; 58:5421-32. [PMID: 23877274 DOI: 10.1088/0031-9155/58/16/5421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Differential phase-contrast computed tomography (DPC-CT) reconstruction problems are usually solved by using parallel-, fan- or cone-beam algorithms. For rod-shaped objects, the x-ray beams cannot recover all the slices of the sample at the same time. Thus, if a rod-shaped sample is required to be reconstructed by the above algorithms, one should alternately perform translation and rotation on this sample, which leads to lower efficiency. The helical cone-beam CT may significantly improve scanning efficiency for rod-shaped objects over other algorithms. In this paper, we propose a theoretically exact filter-backprojection algorithm for helical cone-beam DPC-CT, which can be applied to reconstruct the refractive index decrement distribution of the samples directly from two-dimensional differential phase-contrast images. Numerical simulations are conducted to verify the proposed algorithm. Our work provides a potential solution for inspecting the rod-shaped samples using DPC-CT, which may be applicable with the evolution of DPC-CT equipments.
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Affiliation(s)
- Jing Li
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning Province 116023, People's Republic of China.
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Abstract
The classic imaging geometry for computed tomography is for the collection of un-truncated projections and the reconstruction of a global image, with the Fourier transform as the theoretical foundation that is intrinsically non-local. Recently, interior tomography research has led to theoretically exact relationships between localities in the projection and image spaces and practically promising reconstruction algorithms. Initially, interior tomography was developed for x-ray computed tomography. Then, it was elevated to have the status of a general imaging principle. Finally, a novel framework known as 'omni-tomography' is being developed for a grand fusion of multiple imaging modalities, allowing tomographic synchrony of diversified features.
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Affiliation(s)
- Ge Wang
- Biomedical Imaging Cluster, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
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Kudo H, Suzuki T, Rashed EA. Image reconstruction for sparse-view CT and interior CT-introduction to compressed sensing and differentiated backprojection. Quant Imaging Med Surg 2013; 3:147-61. [PMID: 23833728 DOI: 10.3978/j.issn.2223-4292.2013.06.01] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Accepted: 06/05/2013] [Indexed: 11/14/2022]
Abstract
New designs of future computed tomography (CT) scanners called sparse-view CT and interior CT have been considered in the CT community. Since these CTs measure only incomplete projection data, a key to put these CT scanners to practical use is a development of advanced image reconstruction methods. After 2000, there was a large progress in this research area briefly summarized as follows. In the sparse-view CT, various image reconstruction methods using the compressed sensing (CS) framework have been developed towards reconstructing clinically feasible images from a reduced number of projection data. In the interior CT, several novel theoretical results on solution uniqueness and solution stability have been obtained thanks to the discovery of a new class of reconstruction methods called differentiated backprojection (DBP). In this paper, we mainly review this progress including mathematical principles of the CS image reconstruction and the DBP image reconstruction for readers unfamiliar with this area. We also show some experimental results from our past research to demonstrate that this progress is not only theoretically elegant but also works in practical imaging situations.
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Affiliation(s)
- Hiroyuki Kudo
- Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, Tennoudai 1-1-1, Tsukuba 305-8573, Japan
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Bian J, Wang J, Han X, Sidky EY, Shao L, Pan X. Optimization-based image reconstruction from sparse-view data in offset-detector CBCT. Phys Med Biol 2012; 58:205-30. [PMID: 23257068 DOI: 10.1088/0031-9155/58/2/205] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The field of view (FOV) of a cone-beam computed tomography (CBCT) unit in a single-photon emission computed tomography (SPECT)/CBCT system can be increased by offsetting the CBCT detector. Analytic-based algorithms have been developed for image reconstruction from data collected at a large number of densely sampled views in offset-detector CBCT. However, the radiation dose involved in a large number of projections can be of a health concern to the imaged subject. CBCT-imaging dose can be reduced by lowering the number of projections. As analytic-based algorithms are unlikely to reconstruct accurate images from sparse-view data, we investigate and characterize in the work optimization-based algorithms, including an adaptive steepest descent-weighted projection onto convex sets (ASD-WPOCS) algorithms, for image reconstruction from sparse-view data collected in offset-detector CBCT. Using simulated data and real data collected from a physical pelvis phantom and patient, we verify and characterize properties of the algorithms under study. Results of our study suggest that optimization-based algorithms such as ASD-WPOCS may be developed for yielding images of potential utility from a number of projections substantially smaller than those used currently in clinical SPECT/CBCT imaging, thus leading to a dose reduction in CBCT imaging.
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Affiliation(s)
- Junguo Bian
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
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Dong X, Niu T, Jia X, Zhu L. Relationship between x-ray illumination field size and flat field intensity and its impacts on x-ray imaging. Med Phys 2012; 39:5901-9. [PMID: 23039629 DOI: 10.1118/1.4750054] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
PURPOSE X-ray cone-beam CT (CBCT) is being increasingly used for various clinical applications, while its performance is still hindered by image artifacts. This work investigates a new source of reconstruction error, which is often overlooked in the current CBCT imaging. The authors find that the x-ray flat field intensity (I(0)) varies significantly as the illumination volume size changes at different collimator settings. A wrong I(0) value leads to inaccurate CT numbers of reconstructed images as well as wrong scatter measurements in the CBCT research. METHODS The authors argue that the finite size of x-ray focal spot together with the detector glare effect cause the I(0) variation at different illumination sizes. Although the focal spot of commercial x-ray tubes typically has a nominal size of less than 1 mm, the off-focal-spot radiation covers an area of several millimeters on the tungsten target. Due to the large magnification factor from the field collimator to the detector, the penumbra effects of the collimator blades result in different I(0) values for different illumination field sizes. Detector glare further increases the variation, since one pencil beam of incident x-ray is scattered into an area of several centimeters on the detector. In this paper, the authors study these two effects by measuring the focal spot distribution with a pinhole assembly and the detector point spread function (PSF) with an edge-spread function method. The authors then derive a formula to estimate the I(0) value for different illumination field sizes, using the measured focal spot distribution and the detector PSF. Phantom studies are carried out to investigate the accuracy of scatter measurements and CT images with and without considering the I(0) variation effects. RESULTS On our tabletop system with a Varian Paxscan 4030CB flat-panel detector and a Varian RAD-94 x-ray tube as used on a clinical CBCT system, the focal spot distribution has a measured full-width-at-half-maximum (FWHM) of around 0.4 mm, while non-negligible off-focal-spot radiation is observed at a distance of over 2 mm from the center. The measured detector PSF has an FWHM of 0.510 mm, with a shape close to Gaussian. From these two distributions, the author calculate the estimated I(0) values at different collimator settings. The I(0) variation mainly comes from the focal spot effect. The estimation matches well with the measurements at different collimator widths in both horizontal and vertical directions, with an average error of less than 3%. Our method improves the accuracy of conventional scatter measurements, where the scatter is measured as the difference between fan-beam and cone-beam projections. On a uniform water cylinder phantom, more accurate I(0) suppresses the unfaithful high-frequency signals at the object boundaries of the measured scatter, and the SPR estimation error is reduced from 0.158 to 0.014. The proposed I(0) estimation also reduces the reconstruction error from about 20 HU on the Catphan©600 phantom in the selected regions of interest to less than 4 HU. CONCLUSIONS The I(0) variation is identified as one additional error source in x-ray imaging. By measuring the focal-spot distribution and detector PSF, the authors propose an accurate method of estimating the I(0) value for different illumination field sizes. The method obtains more accurate scatter measurements and therefore facilitates scatter correction algorithm designs. As correction methods for other CBCT artifacts become more successful, our research is significant in further improving the CBCT imaging accuracy.
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Affiliation(s)
- Xue Dong
- The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Tan J, Li HH, Klein E, Li H, Parikh P, Yang D. Physical phantom studies of helical cone-beam CT with exact reconstruction. Med Phys 2012; 39:4695-704. [DOI: 10.1118/1.4736535] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
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Lu Y, Yang J, Emerson JW, Mao H, Zhou T, Si Y, Jiang M. Cone-beam reconstruction for the two-circles-plus-one-line trajectory. Phys Med Biol 2012; 57:2689-707. [PMID: 22508942 DOI: 10.1088/0031-9155/57/9/2689] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The Kodak Image Station In-Vivo FX has an x-ray module with cone-beam configuration for radiographic imaging but lacks the functionality of tomography. To introduce x-ray tomography into the system, we choose the two-circles-plus-one-line trajectory by mounting one translation motor and one rotation motor. We establish a reconstruction algorithm by applying the M-line reconstruction method. Numerical studies and preliminary physical phantom experiment demonstrate the feasibility of the proposed design and reconstruction algorithm.
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Affiliation(s)
- Yanbin Lu
- LMAM, School of Mathematical Sciences, Peking University, Beijing, People's Republic of China.
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