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Shi Y, Li J, Li K, Zhang X, Chang P, Huang Z, Liu Y, Wang Y, Zhan Y, Cao X, Zhu S. Detector-trigger-based cardiac multiphase micro-CT imaging for small animals. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1047-1066. [PMID: 37483057 DOI: 10.3233/xst-230034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
BACKGROUND Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. OBJECTIVE To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. METHODS The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. RESULTS Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. CONCLUSIONS DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.
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Affiliation(s)
- Yu Shi
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Juntao Li
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Ke Li
- Xi'an Key Laboratory for Prevention and Treatment of Common Aging Diseases, Translational and Research Centre for Prevention and Therapy of Chronic Disease, Institute of Basic and Translational Medicine, Xi'an Medical University, Xi'an, China
| | - Xuexue Zhang
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Peng Chang
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Zujian Huang
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Yanyun Liu
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Yihan Wang
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Yonghua Zhan
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Xu Cao
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
| | - Shouping Zhu
- School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
- Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China
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Papoutsellis E, Ametova E, Delplancke C, Fardell G, Jørgensen JS, Pasca E, Turner M, Warr R, Lionheart WRB, Withers PJ. Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200193. [PMID: 34218671 PMCID: PMC8255950 DOI: 10.1098/rsta.2020.0193] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 05/10/2023]
Abstract
The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Affiliation(s)
- Evangelos Papoutsellis
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
- Scientific Computing Department, Science Technology Facilities Council, UK Research and Innovation, Rutherford Appleton Laboratory, Didcot, UK
| | - Evelina Ametova
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
- Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Gemma Fardell
- Scientific Computing Department, Science Technology Facilities Council, UK Research and Innovation, Rutherford Appleton Laboratory, Didcot, UK
| | - Jakob S Jørgensen
- Department of Mathematics, The University of Manchester, Manchester, UK
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Edoardo Pasca
- Scientific Computing Department, Science Technology Facilities Council, UK Research and Innovation, Rutherford Appleton Laboratory, Didcot, UK
| | - Martin Turner
- Research IT Services, The University of Manchester, Manchester, UK
| | - Ryan Warr
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | | | - Philip J Withers
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
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Shoghi KI, Badea CT, Blocker SJ, Chenevert TL, Laforest R, Lewis MT, Luker GD, Manning HC, Marcus DS, Mowery YM, Pickup S, Richmond A, Ross BD, Vilgelm AE, Yankeelov TE, Zhou R. Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine. ACTA ACUST UNITED AC 2021; 6:273-287. [PMID: 32879897 PMCID: PMC7442091 DOI: 10.18383/j.tom.2020.00023] [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] [Indexed: 12/11/2022]
Abstract
The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.
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Affiliation(s)
- Kooresh I Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Cristian T Badea
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC
| | - Stephanie J Blocker
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC
| | | | - Richard Laforest
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Michael T Lewis
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX
| | - Gary D Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - H Charles Manning
- Vanderbilt Center for Molecular Probes-Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, Durham, NC
| | - Stephen Pickup
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania.,Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
| | - Ann Richmond
- Department of Pharmacology, Vanderbilt School of Medicine, Nashville, TN
| | - Brian D Ross
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Anna E Vilgelm
- Department of Pathology, The Ohio State University, Columbus, OH
| | - Thomas E Yankeelov
- Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Oden Institute for Computational Engineering and Sciences, Austin, TX; and.,Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Rong Zhou
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania.,Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
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Podgorsak AR, Shiraz Bhurwani MM, Ionita CN. Investigation of the efficacy of a data-driven CT artifact correction scheme for sparse and truncated projection data for intracranial hemorrhage diagnosis. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 11595:1159542. [PMID: 33814670 PMCID: PMC8018695 DOI: 10.1117/12.2580899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Data-driven CT-image reconstruction techniques for truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts have been proposed. These approaches have shown good performance and preservation of image quality metrics. To continue these efforts, we investigated whether these techniques affect the performance of a machine-learning algorithm to identify the presence of intracranial hemorrhage (ICH). Ten-thousand head CT scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and one-third sparse manner. GANs were tasked with correcting the incomplete projection data in two ways. Firstly, in the sinogram domain, where the incomplete sinogram was filled by the GAN and then reconstructed. Secondly, in the reconstruction domain, where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the GAN. Eighty-five hundred images were used for artifact correction network training, and 1500 were withheld for network assessment via an already trained machine-learning algorithm tasked with diagnosis of ICH presence. Fully-sampled reconstructions were compared with the sparse and truncated reconstructions for classification accuracy. Difference in classification accuracy between the fully sampled (83.4%), sparse (82.0%), and truncated (82.3%) reconstructions was minimal, demonstrating that the network diagnosis performance is unaffected by 2/3 reduction of projection data. This work indicates that data-driven reconstructions for a sparse or truncated projection dataset can provide high diagnostic performance for ICH detection at a fraction of the typical radiation dose.
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Affiliation(s)
- Alexander R Podgorsak
- Department of Biomedical Engineering, State University of New York at Buffalo
- Medical Physics Program, State University of New York at Buffalo
- Canon Stroke and Vascular Research Center
| | | | - Ciprian N Ionita
- Department of Biomedical Engineering, State University of New York at Buffalo
- Medical Physics Program, State University of New York at Buffalo
- Canon Stroke and Vascular Research Center
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5
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Badea CT. Principles of Micro X-ray Computed Tomography. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00006-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Panetta D, Gabelloni M, Faggioni L, Pelosi G, Aringhieri G, Caramella D, Salvadori PA. Cardiac Computed Tomography Perfusion: Contrast Agents, Challenges and Emerging Methodologies from Preclinical Research to the Clinics. Acad Radiol 2021; 28:e1-e13. [PMID: 32220550 DOI: 10.1016/j.acra.2019.12.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/20/2019] [Accepted: 12/24/2019] [Indexed: 12/19/2022]
Abstract
Computed Tomography (CT) has long been regarded as a purely anatomical imaging modality. Recent advances on CT technology and Contrast Agents (CA) in both clinical and preclinical cardiac imaging offer opportunities for the use of CT in functional imaging. Combined with modern ECG-gating techniques, functional CT has now become a reality allowing a comprehensive evaluation of myocardial global and regional function, perfusion and coronary angiography. This article aims at reviewing the current status of cardiac CT perfusion and micro-CT perfusion with established and experimental scanners and contrast agents, from clinical practice to the experimental domain of investigations based on animal models of heart diseases.
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Podgorsak AR, Shiraz Bhurwani MM, Ionita CN. CT artifact correction for sparse and truncated projection data using generative adversarial networks. Med Phys 2020; 48:615-626. [PMID: 32996149 DOI: 10.1002/mp.14504] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Computed tomography image reconstruction using truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts has been widely investigated. To continue these efforts, we investigated the use of machine learning-based reconstruction techniques using deep convolutional generative adversarial networks (DCGANs) and evaluated its effect using standard imaging metrics. METHODS Ten thousand head computed tomography (CT) scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and one-third sparse manner. DCGANs were tasked with correcting the incomplete projection data, either in the sinogram domain where the full sinogram was recovered by the DCGAN and then reconstructed, or the reconstruction domain where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the DCGAN. Seventy-five hundred images were used for network training and 2500 were withheld for network assessment using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR) between results of different correction techniques. Image data from a quality-assurance phantom were also resampled in the two manners and corrected and reconstructed for network performance assessment using line profiles across high-contrast features, the modulation transfer function (MTF), noise power spectrum (NPS), and Hounsfield Unit (HU) linearity analysis. RESULTS Better agreement with the fully sampled reconstructions were achieved from sparse acquisition corrected in the sinogram domain and the truncated acquisition corrected in the reconstruction domain. MAE, SSIM, and PSNR showed quantitative improvement from the DCGAN correction techniques. HU linearity of the reconstructions was maintained by the correction techniques for the sparse and truncated acquisitions. MTF curves reached the 10% modulation cutoff frequency at 5.86 lp/cm for the truncated corrected reconstruction compared with 2.98 lp/cm for the truncated uncorrected reconstruction, and 5.36 lp/cm for the sparse corrected reconstruction compared with around 2.91 lp/cm for the sparse uncorrected reconstruction. NPS analyses yielded better agreement across a range of frequencies between the resampled corrected phantom and truth reconstructions. CONCLUSIONS We demonstrated the use of DCGANs for CT-image correction from sparse and truncated simulated projection data, while preserving imaging quality of the fully sampled projection data.
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Affiliation(s)
- Alexander R Podgorsak
- Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14203, USA.,Medical Physics Program, State University of New York at Buffalo, 955 Main Street, Buffalo, NY, 14203, USA.,Department of Biomedical Engineering, State University of New York at Buffalo, 200 Lee Road, Buffalo, NY, 14228, USA
| | - Mohammad Mahdi Shiraz Bhurwani
- Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14203, USA.,Department of Biomedical Engineering, State University of New York at Buffalo, 200 Lee Road, Buffalo, NY, 14228, USA
| | - Ciprian N Ionita
- Canon Stroke and Vascular Research Center, 875 Ellicott Street, Buffalo, NY, 14203, USA.,Medical Physics Program, State University of New York at Buffalo, 955 Main Street, Buffalo, NY, 14203, USA.,Department of Biomedical Engineering, State University of New York at Buffalo, 200 Lee Road, Buffalo, NY, 14228, USA
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Sheng W, Zhao X, Li M. A sequential regularization based image reconstruction method for limited-angle spectral CT. Phys Med Biol 2020; 65:235038. [PMID: 32464621 DOI: 10.1088/1361-6560/ab9771] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
In spectral computed tomography (CT), the object is respectively scanned under different x-ray spectra. Multiple projection data can be collectively used for reconstructing basis images and virtual monochromatic images, which have been used in material decomposition, beam-hardening correction, bone removal, and so on. In practice, projection data may be obtained in a limited scanning angular range. Images reconstructed from limited-angle data by conventional spectral CT reconstruction methods will be deteriorated by limited-angle related artifacts and basis image decomposition errors. Motivated by observations of limited-angle spectral CT, we propose a sequential regularization-based limited-angle spectral CT reconstruction model and its numerical solver. Both simulated and real data experiments validate that our method is capable of suppressing artifacts, preserving edges and reducing decomposition errors.
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Affiliation(s)
- Wenjuan Sheng
- School of Mathematical Sciences, Capital Normal University, Beijing 100048, People's Republic of China. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, People's Republic of China
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Huang X, Zhang Y, Chen L, Wang J. U-net-based deformation vector field estimation for motion-compensated 4D-CBCT reconstruction. Med Phys 2020; 47:3000-3012. [PMID: 32198934 DOI: 10.1002/mp.14150] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 03/02/2020] [Accepted: 03/10/2020] [Indexed: 11/10/2022] Open
Abstract
PURPOSE For four-dimensional cone-beam computed tomography (4D-CBCT), its image quality is usually degraded by insufficient projections at each respiratory phase after phase-sorting. Recently, we developed a simultaneous motion estimation and image reconstruction (SMEIR) technique, which can improve lung 4D-CBCT reconstruction quality by incorporating an interphase motion model generated as deformation vector fields (DVFs). Simultaneous motion estimation and image reconstruction uses an intensity-driven two-dimensional (2D)-three-dimensional (3D) deformation technique to estimate these DVFs by intensity-matching 2D projections. However, 2D-3D deformation may fail to generate accurate intra-lung DVFs, since the motion of intricate, small lung structures only leads to subtle intensity variations on 2D projections that are insufficient to drive accurate DVF optimization. This study is to develop convolutional neural network (CNN)-based methods to fine-tune the 2D-3D deformation DVFs to improve the efficiency and accuracy of 4D-CBCT reconstruction. METHODS We built two U-net-based architectures for this study. The first architecture (U-net-3C) uses 2D-3D deformation-estimated DVFs (in three cardinal directions) as the input with three channels (3C), and outputs are fine-tuned DVFs. For the second architecture (U-net-4C), the reference phase CBCT image reconstructed by SMEIR was added as an additional input channel (4C) to represent patient-specific heterogeneous properties of the lung. The output fine-tuned high-quality DVFs of both models were input again into the SMEIR workflow, as an optimized motion model, to generate the final 4D-CBCT. Both methods were evaluated on 11 lung patient cases, using fivefold cross-validation. We also reconstructed 4D-CBCTs by the original SMEIR and the SMEIR-Bio (SMEIR with biomechanical modeling) algorithms for comparison. The 4D-CBCT accuracy was quantitatively assessed through metrics including root-mean-square-error (RMSE), universal quality index (UQI), and normalized cross-correlation (NCC). The DVF accuracy was evaluated by manually tracked lung landmarks. We also evaluated our proposed methods on the SPARE challenge dataset based on reconstructed 4D-CBCT quality using the above metrics. RESULTS The average (±standard deviation) residual DVF errors of SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 3.88 ± 3.12 mm, 3.71 ± 2.90 mm, 3.75 ± 3.40 mm, and 5.73 ± 4.61 mm, respectively. The SMEIR-U-net-3C and SMEIR-U-net-4C generated images of generally improved RMSE, UQI, and NCC as compared to the other methods. Compared with SMERI-U-net-3C, SMEIR-U-net-4C has slightly higher 4D-CBCT reconstruction and DVF estimation accuracy. For the SPARE dataset, the UQI for SMEIR-U-net-3C, SMEIR-U-net-4C, SMEIR-Bio, and SMEIR were 0.96, 0.97, 0.96, and 0.94. CONCLUSION The CNN-based models can achieve fast (~10 s) and accurate DVF fine-tuning to improve the efficiency and accuracy of 4D-CBCT reconstruction.
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Affiliation(s)
- Xiaokun Huang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA
| | - You Zhang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA
| | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75235, USA
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Wang Y, Zhong Z, Hua J. DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020; 26:960-970. [PMID: 31442979 DOI: 10.1109/tvcg.2019.2934369] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).
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Chen Y, Yin FF, Jiang Z, Ren L. Daily edge deformation prediction using an unsupervised convolutional neural network model for low dose prior contour based total variation CBCT reconstruction (PCTV-CNN). Biomed Phys Eng Express 2019; 5:065013. [PMID: 32587754 PMCID: PMC7316357 DOI: 10.1088/2057-1976/ab446b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE Previously we developed a PCTV method to enhance the edge sharpness for low-dose CBCT reconstruction. However, the iterative deformable registration method used for deforming edges from planning-CT to on-board CBCT is time-consuming and user-dependent. This study aims to automate and accelerate PCTV reconstruction by developing an unsupervised CNN model to bypass the conventional deformable registration. METHODS The new method uses unsupervised CNN model for deformation prediction and PCTV reconstruction. An unsupervised CNN model with a u-net structure was used to predict deformation vector fields (DVF) to generate on-board contours for PCTV reconstruction. Paired 3D image volumes of prior CT and on-board CBCT are inputs and DVF are predicted without the need of ground truths. The model was initially trained on brain MRI images, and then fine-tuned using our lung SBRT data. This method was evaluated using lung SBRT patient data. In the intra-patient study, the first n-1 day's CBCTs are used for CNN training to predict nth day edge information (n = 2, 3, 4, 5). 45 half-fan projections covering 360˚ from nth day CBCT is used for reconstruction. In the inter-patient study, the 10 patient images including CT and first day's CBCT are used for training. Results from Edge-preserving (EPTV), PCTV and PCTV-CNN are compared. RESULTS The cross-correlations of the predicted edge map and the ground truth were on average 0.88 for both intra-patient and inter-patient studies. PCTV-CNN achieved comparable image quality as PCTV while automating the registration process and reducing the registration time from 1-2 min to 1.4 s. CONCLUSION It is feasible to use an unsupervised CNN to predict daily deformation of on-board edge information for PCTV based low-dose CBCT reconstruction. PCTV-CNN has a great potential for enhancing the edge sharpness with high efficiency for low-dose CBCT to improve the precision of on-board target localization and adaptive radiotherapy.
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Affiliation(s)
- Yingxuan Chen
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, United States of America
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, People's Republic of China
| | - Zhuoran Jiang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, United States of America
| | - Lei Ren
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, North Carolina, 27710, United States of America
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Chen Y, Yin FF, Zhang Y, Zhang Y, Ren L. Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV). Quant Imaging Med Surg 2019; 9:1214-1228. [PMID: 31448208 DOI: 10.21037/qims.2019.06.02] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method. Methods Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. Results EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Conclusions Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.
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Affiliation(s)
- Yingxuan Chen
- Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke University, Durham, NC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan 215316, China
| | - Yawei Zhang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - You Zhang
- Medical Physics Graduate Program, Duke University, Durham, NC, USA
| | - Lei Ren
- Medical Physics Graduate Program, Duke University, Durham, NC, USA.,Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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13
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Li Z, Wang L, Zhang W, Cai A, Li L, Liang N, Yan B. Efficient solving algorithm for determining the exact sampling condition of limited-angle computed tomography reconstruction. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:371-388. [PMID: 30856151 DOI: 10.3233/xst-180455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Total variation (TV) regularization-based iterative reconstruction algorithms have an impressive potential to solve limited-angle computed tomography with insufficient sampling projections. The analysis of exact reconstruction sampling conditions for a TV-minimization reconstruction model can determine the minimum number of scanning angle and minimize the scanning range. However, the large-scale matrix operations caused by increased testing phantom size are the computation bottleneck in determining the exact reconstruction sampling conditions in practice. When the size of the testing phantom increases to a certain scale, it is very difficult to analyze quantitatively the exact reconstruction sampling condition using existing methods. In this paper, we propose a fast and efficient algorithm to determine the exact reconstruction sampling condition for large phantoms. Specifically, the sampling condition of a TV minimization model is modeled as a convex optimization problem, which is derived from the sufficient and necessary condition of solution uniqueness for the L1 minimization model. An effective alternating direction minimization algorithm is developed to optimize the objective function by alternatively solving two sub-problems split from the convex problem. The Cholesky decomposition method is used in solving the first sub-problem to reduce computational complexity. Experimental results show that the proposed method can efficiently solve the verification problem of the accurate reconstruction sampling condition. Furthermore, we obtain the lower bounds of scanning angle range for the exact reconstruction of a specific phantom with the larger size.
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Affiliation(s)
- Ziheng Li
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Linyuan Wang
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Wenkun Zhang
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Ailong Cai
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Lei Li
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Ningning Liang
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
| | - Bin Yan
- National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, Henan, China
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14
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Melli SA, Wahid KA, Babyn P, Cooper DML, Hasan AM. A wavelet gradient sparsity based algorithm for reconstruction of reduced-view tomography datasets obtained with a monochromatic synchrotron-based X-ray source. Comput Med Imaging Graph 2018; 69:69-81. [PMID: 30212736 DOI: 10.1016/j.compmedimag.2018.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 06/16/2018] [Accepted: 08/21/2018] [Indexed: 11/16/2022]
Abstract
High-resolution synchrotron computed tomography (CT) is very helpful in the diagnosis and monitor of chronic diseases including osteoporosis. Osteoporosis is characterized by low bone mass and cortical bone porosity best imaged with CT. Synchrotron CT requires a large number of angular projections to reconstruct images with high resolution for detailed and accurate diagnosis. However, this poses great risks and challenges for serial in-vivo human and animal imaging due to a large amount of X-ray radiation dose required that can damage living specimens. Also, longer scan times are associated with increased risk of specimen movement and motion artifact in the reconstructed images. We developed a wavelet-gradient sparsity based algorithm to be utilized as a synchrotron tomography reconstruction technique allowing accurate reconstruction of cortical bone porosity assessed for in-vivo preclinical study which significantly reduces the radiation dose and scan time required while maintaining satisfactory image resolution for diagnosis. The results of our study on a rat forelimb sample imaged in the Biomedical Imaging and Therapy Bending Magnet (BMIT-BM) beamline at the Canadian Light Source show that the proposed algorithm can produce satisfactory image quality with more than 50 percent X-ray dose reduction as indicated by both visual and quantitative-based performance.
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Affiliation(s)
- S Ali Melli
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
| | - Khan A Wahid
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
| | - Paul Babyn
- Department of Medical Imaging, Royal University Hospital, University of Saskatchewan, Saskatoon, SK, Canada
| | - David M L Cooper
- Department of Anatomy and Cell Biology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ahmed M Hasan
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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15
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D'Eurydice MN, Arns CH, Arns JY, Armstrong RT. Dynamic imaging of multiphase flow through porous media using 4D cumulative reconstruction. J Microsc 2018; 272:12-24. [PMID: 29971773 DOI: 10.1111/jmi.12728] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 06/06/2018] [Indexed: 11/30/2022]
Abstract
This paper introduces an original application on reconstruction strategies for X-ray computed microtomography, enabling the observation of time-dependent changes that occur during multiphase flow. In general, by sparsely collecting radiographs, the reconstruction of the object is compromised. Optimizations can be achieved by combining specific characteristics of the dynamics with the acquisition. Herein, the proposed method relies on short random intervals in which no drastic changes occur in the sample to acquire as many radiographs as possible that constitute a reconstructible data set. As these intervals are unpredictable, the method tries to guarantee that the collected radiograph data during these specific intervals are enough to recover useful information about the dynamics. Simulations of a percolating fluid in a digital rock are used to replicate an X-ray computed microtomography experiment to test the proposed method. The results demonstrate the potential of the proposed strategy for imaging multiphase flow in porous media and how data collected during distinct events can be combined to enhance the reconstruction of frames of the percolation process.
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Affiliation(s)
- M N D'Eurydice
- School of Minerals and Energy Resources Engineering, University of New South Wales, Kensington, NSW, Australia.,Source Crafting Limited, Wellington, New Zealand
| | - C H Arns
- School of Minerals and Energy Resources Engineering, University of New South Wales, Kensington, NSW, Australia
| | - J-Y Arns
- School of Minerals and Energy Resources Engineering, University of New South Wales, Kensington, NSW, Australia
| | - R T Armstrong
- School of Minerals and Energy Resources Engineering, University of New South Wales, Kensington, NSW, Australia
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16
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Chen Y, Yin FF, Zhang Y, Zhang Y, Ren L. Low dose CBCT reconstruction via prior contour based total variation (PCTV) regularization: a feasibility study. Phys Med Biol 2018. [PMID: 29537385 DOI: 10.1088/1361-6560/aab68d] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE compressed sensing reconstruction using total variation (TV) tends to over-smooth the edge information by uniformly penalizing the image gradient. The goal of this study is to develop a novel prior contour based TV (PCTV) method to enhance the edge information in compressed sensing reconstruction for CBCT. METHODS the edge information is extracted from prior planning-CT via edge detection. Prior CT is first registered with on-board CBCT reconstructed with TV method through rigid or deformable registration. The edge contours in prior-CT is then mapped to CBCT and used as the weight map for TV regularization to enhance edge information in CBCT reconstruction. The PCTV method was evaluated using extended-cardiac-torso (XCAT) phantom, physical CatPhan phantom and brain patient data. Results were compared with both TV and edge preserving TV (EPTV) methods which are commonly used for limited projection CBCT reconstruction. Relative error was used to calculate pixel value difference and edge cross correlation was defined as the similarity of edge information between reconstructed images and ground truth in the quantitative evaluation. RESULTS compared to TV and EPTV, PCTV enhanced the edge information of bone, lung vessels and tumor in XCAT reconstruction and complex bony structures in brain patient CBCT. In XCAT study using 45 half-fan CBCT projections, compared with ground truth, relative errors were 1.5%, 0.7% and 0.3% and edge cross correlations were 0.66, 0.72 and 0.78 for TV, EPTV and PCTV, respectively. PCTV is more robust to the projection number reduction. Edge enhancement was reduced slightly with noisy projections but PCTV was still superior to other methods. PCTV can maintain resolution while reducing the noise in the low mAs CatPhan reconstruction. Low contrast edges were preserved better with PCTV compared with TV and EPTV. CONCLUSION PCTV preserved edge information as well as reduced streak artifacts and noise in low dose CBCT reconstruction. PCTV is superior to TV and EPTV methods in edge enhancement, which can potentially improve the localization accuracy in radiation therapy.
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Affiliation(s)
- Yingxuan Chen
- Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
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17
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Huang X, Zhang Y, Wang J. A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction. Phys Med Biol 2018; 63:045002. [PMID: 29328048 DOI: 10.1088/1361-6560/aaa730] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ's fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model's accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.
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Affiliation(s)
- Xiaokun Huang
- Xiaokun Huang and You Zhang contributed equally to the work
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18
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Holbrook M, Clark DP, Badea CT. Low-dose 4D cardiac imaging in small animals using dual source micro-CT. Phys Med Biol 2018; 63:025009. [PMID: 29148430 DOI: 10.1088/1361-6560/aa9b45] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Micro-CT is widely used in preclinical studies, generating substantial interest in extending its capabilities in functional imaging applications such as blood perfusion and cardiac function. However, imaging cardiac structure and function in mice is challenging due to their small size and rapid heart rate. To overcome these challenges, we propose and compare improvements on two strategies for cardiac gating in dual-source, preclinical micro-CT: fast prospective gating (PG) and uncorrelated retrospective gating (RG). These sampling strategies combined with a sophisticated iterative image reconstruction algorithm provide faster acquisitions and high image quality in low-dose 4D (i.e. 3D + Time) cardiac micro-CT. Fast PG is performed under continuous subject rotation which results in interleaved projection angles between cardiac phases. Thus, fast PG provides a well-sampled temporal average image for use as a prior in iterative reconstruction. Uncorrelated RG incorporates random delays during sampling to prevent correlations between heart rate and sampling rate. We have performed both simulations and animal studies to validate these new sampling protocols. Sampling times for 1000 projections using fast PG and RG were 2 and 3 min, respectively, and the total dose was 170 mGy each. Reconstructions were performed using a 4D iterative reconstruction technique based on the split Bregman method. To examine undersampling robustness, subsets of 500 and 250 projections were also used for reconstruction. Both sampling strategies in conjunction with our iterative reconstruction method are capable of resolving cardiac phases and provide high image quality. In general, for equal numbers of projections, fast PG shows fewer errors than RG and is more robust to undersampling. Our results indicate that only 1000-projection based reconstruction with fast PG satisfies a 5% error criterion in left ventricular volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.
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Affiliation(s)
- M Holbrook
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC 27710, United States of America
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19
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Ren L, Zheng B, Liu H. Tutorial on X-ray photon counting detector characterization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2018; 26:1-28. [PMID: 29154310 PMCID: PMC5909414 DOI: 10.3233/xst-16210] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
BACKGROUND Recent advances in photon counting detection technology have led to significant research interest in X-ray imaging. OBJECTIVE As a tutorial level review, this paper covers a wide range of aspects related to X-ray photon counting detector characterization. METHODS The tutorial begins with a detailed description of the working principle and operating modes of a pixelated X-ray photon counting detector with basic architecture and detection mechanism. Currently available methods and techniques for charactering major aspects including energy response, noise floor, energy resolution, count rate performance (detector efficiency), and charge sharing effect of photon counting detectors are comprehensively reviewed. Other characterization aspects such as point spread function (PSF), line spread function (LSF), contrast transfer function (CTF), modulation transfer function (MTF), noise power spectrum (NPS), detective quantum efficiency (DQE), bias voltage, radiation damage, and polarization effect are also remarked. RESULTS A cadmium telluride (CdTe) pixelated photon counting detector is employed for part of the characterization demonstration and the results are presented. CONCLUSIONS This review can serve as a tutorial for X-ray imaging researchers and investigators to understand, operate, characterize, and optimize photon counting detectors for a variety of applications.
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Affiliation(s)
- Liqiang Ren
- Center for Biomedical Engineering and School of Electrical and Computer
Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Bin Zheng
- Center for Biomedical Engineering and School of Electrical and Computer
Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Hong Liu
- Center for Biomedical Engineering and School of Electrical and Computer
Engineering, University of Oklahoma, Norman, OK 73019, USA
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20
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Abstract
In Fourier-based medical imaging, sampling below the Nyquist rate results in an underdetermined system, in which a linear reconstruction will exhibit artifacts. Another consequence is lower signal-to-noise ratio (SNR) because of fewer acquired measurements. Even if one could obtain information to perfectly disambiguate the underdetermined system, the reconstructed image could still have lower image quality than a corresponding fully sampled acquisition because of reduced measurement time. The coupled effects of low SNR and underdetermined system during reconstruction makes it difficult to isolate the impact of low SNR on image quality. To this end, we present an image quality prediction process that reconstructs fully sampled, fully determined data with noise added to simulate the SNR loss induced by a given undersampling pattern. The resulting prediction image empirically shows the effects of noise in undersampled image reconstruction without any effect from an underdetermined system. We discuss how our image quality prediction process simulates the distribution of noise for a given undersampling pattern, including variable density sampling that produces colored noise in the measurement data. An interesting consequence of our prediction model is that recovery from an underdetermined nonuniform sampling is equivalent to a weighted least squares optimization that accounts for heterogeneous noise levels across measurements. Through experiments with synthetic and in vivo datasets, we demonstrate the efficacy of the image quality prediction process and show that it provides a better estimation of reconstruction image quality than the corresponding fully sampled reference image.
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Affiliation(s)
- Patrick Virtue
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA
| | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA
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Besson A, Zhang M, Varray F, Liebgott H, Friboulet D, Wiaux Y, Thiran JP, Carrillo RE, Bernard O. A Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2016; 63:2092-2106. [PMID: 27913327 DOI: 10.1109/tuffc.2016.2614996] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct highquality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality.
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Melli SA, Wahid KA, Babyn P, Cooper DML, Gopi VP. A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2016; 87:123701. [PMID: 28040926 DOI: 10.1063/1.4968198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.
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Affiliation(s)
- S Ali Melli
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Saskatchewan S7N5A9, Canada
| | - Khan A Wahid
- Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Saskatchewan S7N5A9, Canada
| | - Paul Babyn
- Department of Medical Imaging, Royal University Hospital, University of Saskatchewan, Saskatoon, Saskatchewan S7N 0W8, Canada
| | - David M L Cooper
- Department of Anatomy and Cell Biology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E5, Canada
| | - Varun P Gopi
- Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Mananthavady, India
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23
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Yan B, Zhang W, Li L, Zhang H, Wang L. Quantitative study on exact reconstruction sampling condition by verifying solution uniqueness in limited-view CT. Phys Med 2016; 32:1321-1330. [DOI: 10.1016/j.ejmp.2016.07.094] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 07/17/2016] [Accepted: 07/19/2016] [Indexed: 10/21/2022] Open
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24
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Saha SK, Tahtali M, Lambert A, Pickering M. Sparsity Prior Computed Tomography Reconstruction Using a Nonstandard Simultaneous X-ray Acquisition Model. J Med Imaging Radiat Sci 2016; 47:251-266.e1. [PMID: 31047290 DOI: 10.1016/j.jmir.2016.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Revised: 05/26/2016] [Accepted: 05/30/2016] [Indexed: 11/18/2022]
Abstract
In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which sparse approximation is performed. The simultaneous x-ray acquisition model ensures rapid capture of x-rays; however, it captures a significantly fewer number of attenuation measurements, and the projections are nonuniform. We propose a weighted average filter in the reconstruction framework to ensure better quality reconstruction by minimizing the effect of nonuniform projections. The performance of the state-of-the-art algorithms is analyzed with and without weighted averaging before sparse approximation, in simulated and real environments. Experiments in the simulated environment are conducted with and without the presence of noise. From the results, it is evident that sparsity prior algorithms are capable of producing cross-sectional reconstruction using the simultaneous x-ray acquisition model, and better reconstruction quality is achievable with the incorporation of weighted averaging in the reconstruction framework.
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Affiliation(s)
- Sajib Kumar Saha
- Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
| | - Murat Tahtali
- Engineering and Information Technology, The University of New South Wales, Canberra, Australia
| | - Andrew Lambert
- Engineering and Information Technology, The University of New South Wales, Canberra, Australia
| | - Mark Pickering
- Engineering and Information Technology, The University of New South Wales, Canberra, Australia
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Yu Z, Leng S, Li Z, McCollough CH. Spectral prior image constrained compressed sensing (spectral PICCS) for photon-counting computed tomography. Phys Med Biol 2016; 61:6707-6732. [PMID: 27551878 DOI: 10.1088/0031-9155/61/18/6707] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43-73%) without sacrificing CT number accuracy or spatial resolution.
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Affiliation(s)
- Zhicong Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Wang T, Zhu L. Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR). Phys Med Biol 2016; 61:6684-6706. [PMID: 27552793 DOI: 10.1088/0031-9155/61/18/6684] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and prior-image-constrained-compressed-sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an average error of less than 1%.
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Affiliation(s)
- Tonghe Wang
- Nuclear & Radiological Engineering and Medical Physics Programs, The George W Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Zhong Z, Gu X, Mao W, Wang J. 4D cone-beam CT reconstruction using multi-organ meshes for sliding motion modeling. Phys Med Biol 2016; 61:996-1020. [PMID: 26758496 PMCID: PMC5026392 DOI: 10.1088/0031-9155/61/3/996] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
A simultaneous motion estimation and image reconstruction (SMEIR) strategy was proposed for 4D cone-beam CT (4D-CBCT) reconstruction and showed excellent results in both phantom and lung cancer patient studies. In the original SMEIR algorithm, the deformation vector field (DVF) was defined on voxel grid and estimated by enforcing a global smoothness regularization term on the motion fields. The objective of this work is to improve the computation efficiency and motion estimation accuracy of SMEIR for 4D-CBCT through developing a multi-organ meshing model. Feature-based adaptive meshes were generated to reduce the number of unknowns in the DVF estimation and accurately capture the organ shapes and motion. Additionally, the discontinuity in the motion fields between different organs during respiration was explicitly considered in the multi-organ mesh model. This will help with the accurate visualization and motion estimation of the tumor on the organ boundaries in 4D-CBCT. To further improve the computational efficiency, a GPU-based parallel implementation was designed. The performance of the proposed algorithm was evaluated on a synthetic sliding motion phantom, a 4D NCAT phantom, and four lung cancer patients. The proposed multi-organ mesh based strategy outperformed the conventional Feldkamp-Davis-Kress, iterative total variation minimization, original SMEIR and single meshing method based on both qualitative and quantitative evaluations.
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Affiliation(s)
- Zichun Zhong
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Xuejun Gu
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Weihua Mao
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
| | - Jing Wang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
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Sisniega A, Zbijewski W, Stayman JW, Xu J, Taguchi K, Fredenberg E, Lundqvist M, Siewerdsen JH. Volumetric CT with sparse detector arrays (and application to Si-strip photon counters). Phys Med Biol 2016; 61:90-113. [PMID: 26611740 PMCID: PMC5070652 DOI: 10.1088/0031-9155/61/1/90] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm × 25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8 × higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.
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Affiliation(s)
- A Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
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Brehm M, Sawall S, Maier J, Sauppe S, Kachelrieß M. Cardiorespiratory motion-compensated micro-CT image reconstruction using an artifact model-based motion estimation. Med Phys 2015; 42:1948-58. [PMID: 25832085 DOI: 10.1118/1.4916083] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Cardiac in vivo micro-CT imaging of small animals typically requires double gating due to long scan times and high respiratory rates. The simultaneous respiratory and cardiac gating can either be done prospectively or retrospectively. In any case, for true 5D imaging, i.e., three spatial dimensions plus one respiratory-temporal dimension plus one cardiac temporal dimension, the amount of information corresponding to a given respiratory and cardiac phase is orders of magnitude lower than the total amount of information acquired. Achieving similar image quality for 5D than for usual 3D investigations would require increasing the amount of data and thus the applied dose to the animal. Therefore, the goal is phase-correlated imaging with high image quality but without increasing the dose level. METHODS To achieve this, the authors propose a new image reconstruction algorithm that makes use of all available projection data, also of that corresponding to other motion windows. In particular, the authors apply a motion-compensated image reconstruction approach that sequentially compensates for respiratory and cardiac motion to decrease the impact of sparsification. In that process, all projection data are used no matter which motion phase they were acquired in. Respiratory and cardiac motion are compensated for by using motion vector fields. These motion vector fields are estimated from initial phase-correlated reconstructions based on a deformable registration approach. To decrease the sensitivity of the registration to sparse-view artifacts, an artifact model-based approach is used including a cyclic consistent nonrigid registration algorithm. RESULTS The preliminary results indicate that the authors' approach removes the sparse-view artifacts of conventional phase-correlated reconstructions while maintaining temporal resolution. In addition, it achieves noise levels and spatial resolution comparable to that of nongated reconstructions due to the improved dose usage. By using the proposed motion estimation, no sensitivity to streaking artifacts has been observed. CONCLUSIONS Using sequential double gating combined with artifact model-based motion estimation allows to accurately estimate respiratory and cardiac motion from highly undersampled data. No sensitivity to streaking artifacts introduced by sparse angular sampling has been observed for the investigated dose levels. The motion-compensated image reconstruction was able to correct for both, respiratory and cardiac motion, by applying the estimated motion vector fields. The administered dose per animal can thus be reduced for 5D imaging allowing for longitudinal studies at the highest image quality.
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Affiliation(s)
- Marcus Brehm
- Varian Medical System Imaging Laboratory, Täfernstrasse 7, Baden-Dättwil 5405, Switzerland and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Stefan Sawall
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Joscha Maier
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Sebastian Sauppe
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
| | - Marc Kachelrieß
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany
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Ashton JR, West JL, Badea CT. In vivo small animal micro-CT using nanoparticle contrast agents. Front Pharmacol 2015; 6:256. [PMID: 26581654 PMCID: PMC4631946 DOI: 10.3389/fphar.2015.00256] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 10/19/2015] [Indexed: 12/12/2022] Open
Abstract
Computed tomography (CT) is one of the most valuable modalities for in vivo imaging because it is fast, high-resolution, cost-effective, and non-invasive. Moreover, CT is heavily used not only in the clinic (for both diagnostics and treatment planning) but also in preclinical research as micro-CT. Although CT is inherently effective for lung and bone imaging, soft tissue imaging requires the use of contrast agents. For small animal micro-CT, nanoparticle contrast agents are used in order to avoid rapid renal clearance. A variety of nanoparticles have been used for micro-CT imaging, but the majority of research has focused on the use of iodine-containing nanoparticles and gold nanoparticles. Both nanoparticle types can act as highly effective blood pool contrast agents or can be targeted using a wide variety of targeting mechanisms. CT imaging can be further enhanced by adding spectral capabilities to separate multiple co-injected nanoparticles in vivo. Spectral CT, using both energy-integrating and energy-resolving detectors, has been used with multiple contrast agents to enable functional and molecular imaging. This review focuses on new developments for in vivo small animal micro-CT using novel nanoparticle probes applied in preclinical research.
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Affiliation(s)
- Jeffrey R Ashton
- Department of Biomedical Engineering, Duke University, Durham NC, USA ; Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham NC, USA
| | - Jennifer L West
- Department of Biomedical Engineering, Duke University, Durham NC, USA
| | - Cristian T Badea
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham NC, USA
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Park JC, Zhang H, Chen Y, Fan Q, Kahler DL, Liu C, Lu B. Priorimask guided image reconstruction (p-MGIR) for ultra-low dose cone-beam computed tomography. Phys Med Biol 2015; 60:8505-24. [DOI: 10.1088/0031-9155/60/21/8505] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Sánchez AA. Estimation of noise properties for TV-regularized image reconstruction in computed tomography. Phys Med Biol 2015; 60:7007-33. [PMID: 26308968 DOI: 10.1088/0031-9155/60/18/7007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
A method for predicting the image covariance resulting from total-variation-penalized iterative image reconstruction (TV-penalized IIR) is presented and demonstrated in a variety of contexts. The method is validated against the sample covariance from statistical noise realizations for a small image using a variety of comparison metrics. Potential applications for the covariance approximation include investigation of image properties such as object- and signal-dependence of noise, and noise stationarity. These applications are demonstrated, along with the construction of image pixel variance maps for two-dimensional 128 × 128 pixel images. Methods for extending the proposed covariance approximation to larger images and improving computational efficiency are discussed. Future work will apply the developed methodology to the construction of task-based image quality metrics such as the Hotelling observer detectability for TV-based IIR.
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Affiliation(s)
- Adrian A Sánchez
- Department of Radiology, University of Chicago, Chicago, IL 60637, USA
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Yan H, Wang X, Shi F, Bai T, Folkerts M, Cervino L, Jiang SB, Jia X. Towards the clinical implementation of iterative low-dose cone-beam CT reconstruction in image-guided radiation therapy: cone/ring artifact correction and multiple GPU implementation. Med Phys 2015; 41:111912. [PMID: 25370645 DOI: 10.1118/1.4898324] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Compressed sensing (CS)-based iterative reconstruction (IR) techniques are able to reconstruct cone-beam CT (CBCT) images from undersampled noisy data, allowing for imaging dose reduction. However, there are a few practical concerns preventing the clinical implementation of these techniques. On the image quality side, data truncation along the superior-inferior direction under the cone-beam geometry produces severe cone artifacts in the reconstructed images. Ring artifacts are also seen in the half-fan scan mode. On the reconstruction efficiency side, the long computation time hinders clinical use in image-guided radiation therapy (IGRT). METHODS Image quality improvement methods are proposed to mitigate the cone and ring image artifacts in IR. The basic idea is to use weighting factors in the IR data fidelity term to improve projection data consistency with the reconstructed volume. In order to improve the computational efficiency, a multiple graphics processing units (GPUs)-based CS-IR system was developed. The parallelization scheme, detailed analyses of computation time at each step, their relationship with image resolution, and the acceleration factors were studied. The whole system was evaluated in various phantom and patient cases. RESULTS Ring artifacts can be mitigated by properly designing a weighting factor as a function of the spatial location on the detector. As for the cone artifact, without applying a correction method, it contaminated 13 out of 80 slices in a head-neck case (full-fan). Contamination was even more severe in a pelvis case under half-fan mode, where 36 out of 80 slices were affected, leading to poorer soft tissue delineation and reduced superior-inferior coverage. The proposed method effectively corrects those contaminated slices with mean intensity differences compared to FDK results decreasing from ∼497 and ∼293 HU to ∼39 and ∼27 HU for the full-fan and half-fan cases, respectively. In terms of efficiency boost, an overall 3.1 × speedup factor has been achieved with four GPU cards compared to a single GPU-based reconstruction. The total computation time is ∼30 s for typical clinical cases. CONCLUSIONS The authors have developed a low-dose CBCT IR system for IGRT. By incorporating data consistency-based weighting factors in the IR model, cone/ring artifacts can be mitigated. A boost in computational efficiency is achieved by multi-GPU implementation.
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Affiliation(s)
- Hao Yan
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Xiaoyu Wang
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037
| | - Feng Shi
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Ti Bai
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 and Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Michael Folkerts
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390 and Department of Physics, University of California San Diego, La Jolla, California 92037
| | - Laura Cervino
- Center for Advanced Radiotherapy Technologies and Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California 92037
| | - Steve B Jiang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
| | - Xun Jia
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75390
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Jørgensen JS, Sidky EY. How little data is enough? Phase-diagram analysis of sparsity-regularized X-ray computed tomography. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:rsta.2014.0387. [PMID: 25939620 PMCID: PMC4424483 DOI: 10.1098/rsta.2014.0387] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/16/2015] [Indexed: 05/31/2023]
Abstract
We introduce phase-diagram analysis, a standard tool in compressed sensing (CS), to the X-ray computed tomography (CT) community as a systematic method for determining how few projections suffice for accurate sparsity-regularized reconstruction. In CS, a phase diagram is a convenient way to study and express certain theoretical relations between sparsity and sufficient sampling. We adapt phase-diagram analysis for empirical use in X-ray CT for which the same theoretical results do not hold. We demonstrate in three case studies the potential of phase-diagram analysis for providing quantitative answers to questions of undersampling. First, we demonstrate that there are cases where X-ray CT empirically performs comparably with a near-optimal CS strategy, namely taking measurements with Gaussian sensing matrices. Second, we show that, in contrast to what might have been anticipated, taking randomized CT measurements does not lead to improved performance compared with standard structured sampling patterns. Finally, we show preliminary results of how well phase-diagram analysis can predict the sufficient number of projections for accurately reconstructing a large-scale image of a given sparsity by means of total-variation regularization.
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Affiliation(s)
- J S Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Kongens Lyngby 2800, Denmark
| | - E Y Sidky
- Department of Radiology MC-2026, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA
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Dang J, Gu X, Pan T, Wang J. A pilot evaluation of a 4-dimensional cone-beam computed tomographic scheme based on simultaneous motion estimation and image reconstruction. Int J Radiat Oncol Biol Phys 2015; 91:410-8. [PMID: 25636763 DOI: 10.1016/j.ijrobp.2014.10.029] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2014] [Revised: 10/10/2014] [Accepted: 10/14/2014] [Indexed: 11/26/2022]
Abstract
PURPOSE To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies. METHODS AND MATERIALS The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm. RESULTS The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients. CONCLUSION The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.
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Affiliation(s)
- Jun Dang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Xuejun Gu
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Tinsu Pan
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jing Wang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas.
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Abascal JFPJ, Abella M, Sisniega A, Vaquero JJ, Desco M. Investigation of different sparsity transforms for the PICCS algorithm in small-animal respiratory gated CT. PLoS One 2015; 10:e0120140. [PMID: 25836670 PMCID: PMC4383608 DOI: 10.1371/journal.pone.0120140] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 02/04/2015] [Indexed: 12/04/2022] Open
Abstract
Respiratory gating helps to overcome the problem of breathing motion in cardiothoracic small-animal imaging by acquiring multiple images for each projection angle and then assigning projections to different phases. When this approach is used with a dose similar to that of a static acquisition, a low number of noisy projections are available for the reconstruction of each respiratory phase, thus leading to streak artifacts in the reconstructed images. This problem can be alleviated using a prior image constrained compressed sensing (PICCS) algorithm, which enables accurate reconstruction of highly undersampled data when a prior image is available. We compared variants of the PICCS algorithm with different transforms in the prior penalty function: gradient, unitary, and wavelet transform. In all cases the problem was solved using the Split Bregman approach, which is efficient for convex constrained optimization. The algorithms were evaluated using simulations generated from data previously acquired on a micro-CT scanner following a high-dose protocol (four times the dose of a standard static protocol). The resulting data were used to simulate scenarios with different dose levels and numbers of projections. All compressed sensing methods performed very similarly in terms of noise, spatiotemporal resolution, and streak reduction, and filtered back-projection was greatly improved. Nevertheless, the wavelet domain was found to be less prone to patchy cartoon-like artifacts than the commonly used gradient domain.
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Affiliation(s)
- Juan F. P. J. Abascal
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Monica Abella
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- * E-mail:
| | - Alejandro Sisniega
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Juan Jose Vaquero
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
| | - Manuel Desco
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain
- Centro de Investigación en Red de Salud Mental (CIBERSAM), Madrid, Spain
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Median prior constrained TV algorithm for sparse view low-dose CT reconstruction. Comput Biol Med 2015; 60:117-31. [PMID: 25817533 DOI: 10.1016/j.compbiomed.2015.03.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Revised: 03/03/2015] [Accepted: 03/06/2015] [Indexed: 11/20/2022]
Abstract
It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method.
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Wieczorek M, Frikel J, Vogel J, Eggl E, Kopp F, Noël PB, Pfeiffer F, Demaret L, Lasser T. X-ray computed tomography using curvelet sparse regularization. Med Phys 2015; 42:1555-65. [DOI: 10.1118/1.4914368] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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Abstract
The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed.
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Affiliation(s)
- Christian G. Graff
- Division of Imaging, Diagnostics and Software Reliability, U.S. Food and Drug Administration, 10903 New Hampshire Ave., Silver Spring MD 20993, USA
- Corresponding author:
| | - Emil Y. Sidky
- Department of Radiology MC-2026, The University of Chicago, 5841 S. Maryland Ave., Chicago IL 60637, USA
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Yang X, Hofmann R, Dapp R, van de Kamp T, dos Santos Rolo T, Xiao X, Moosmann J, Kashef J, Stotzka R. TV-based conjugate gradient method and discrete L-curve for few-view CT reconstruction of X-ray in vivo data. OPTICS EXPRESS 2015; 23:5368-5387. [PMID: 25836772 DOI: 10.1364/oe.23.005368] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
High-resolution, three-dimensional (3D) imaging of soft tissues requires the solution of two inverse problems: phase retrieval and the reconstruction of the 3D image from a tomographic stack of two-dimensional (2D) projections. The number of projections per stack should be small to accommodate fast tomography of rapid processes and to constrain X-ray radiation dose to optimal levels to either increase the duration of in vivo time-lapse series at a given goal for spatial resolution and/or the conservation of structure under X-ray irradiation. In pursuing the 3D reconstruction problem in the sense of compressive sampling theory, we propose to reduce the number of projections by applying an advanced algebraic technique subject to the minimisation of the total variation (TV) in the reconstructed slice. This problem is formulated in a Lagrangian multiplier fashion with the parameter value determined by appealing to a discrete L-curve in conjunction with a conjugate gradient method. The usefulness of this reconstruction modality is demonstrated for simulated and in vivo data, the latter acquired in parallel-beam imaging experiments using synchrotron radiation.
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Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms. Comput Biol Med 2015; 56:97-106. [DOI: 10.1016/j.compbiomed.2014.11.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2014] [Revised: 10/28/2014] [Accepted: 11/02/2014] [Indexed: 11/19/2022]
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Lu W, Yan H, Gu X, Tian Z, Luo O, Yang L, Zhou L, Cervino L, Wang J, Jiang S, Jia X. Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study. Phys Med Biol 2014; 59:6251-66. [PMID: 25255957 PMCID: PMC4197814 DOI: 10.1088/0031-9155/59/20/6251] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSEs) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3-6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1-3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.
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Affiliation(s)
- Wenting Lu
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Hao Yan
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xuejun Gu
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Zhen Tian
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ouyang Luo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Liu Yang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Linghong Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Laura Cervino
- Center for Advanced Radiotherapy Technologies, Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA 92037, USA
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Steve Jiang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xun Jia
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA
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Zbijewski W, Gang GJ, Xu J, Wang AS, Stayman JW, Taguchi K, Carrino JA, Siewerdsen JH. Dual-energy cone-beam CT with a flat-panel detector: effect of reconstruction algorithm on material classification. Med Phys 2014; 41:021908. [PMID: 24506629 DOI: 10.1118/1.4863598] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size. METHODS Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy. RESULTS Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose. CONCLUSIONS For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).
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Affiliation(s)
- W Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - G J Gang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - J Xu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - A S Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - J W Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205
| | - K Taguchi
- Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, Maryland 21205
| | - J A Carrino
- Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, Maryland 21205
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205 and Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, Maryland 21205
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Clark DP, Badea CT. Micro-CT of rodents: state-of-the-art and future perspectives. Phys Med 2014; 30:619-34. [PMID: 24974176 PMCID: PMC4138257 DOI: 10.1016/j.ejmp.2014.05.011] [Citation(s) in RCA: 134] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Revised: 05/15/2014] [Accepted: 05/28/2014] [Indexed: 02/06/2023] Open
Abstract
Micron-scale computed tomography (micro-CT) is an essential tool for phenotyping and for elucidating diseases and their therapies. This work is focused on preclinical micro-CT imaging, reviewing relevant principles, technologies, and applications. Commonly, micro-CT provides high-resolution anatomic information, either on its own or in conjunction with lower-resolution functional imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). More recently, however, advanced applications of micro-CT produce functional information by translating clinical applications to model systems (e.g., measuring cardiac functional metrics) and by pioneering new ones (e.g. measuring tumor vascular permeability with nanoparticle contrast agents). The primary limitations of micro-CT imaging are the associated radiation dose and relatively poor soft tissue contrast. We review several image reconstruction strategies based on iterative, statistical, and gradient sparsity regularization, demonstrating that high image quality is achievable with low radiation dose given ever more powerful computational resources. We also review two contrast mechanisms under intense development. The first is spectral contrast for quantitative material discrimination in combination with passive or actively targeted nanoparticle contrast agents. The second is phase contrast which measures refraction in biological tissues for improved contrast and potentially reduced radiation dose relative to standard absorption imaging. These technological advancements promise to develop micro-CT into a commonplace, functional and even molecular imaging modality.
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Affiliation(s)
- D P Clark
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Box 3302, Durham, NC 27710, USA
| | - C T Badea
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Box 3302, Durham, NC 27710, USA.
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Lee S, Choi YN, Kim HJ. Quantitative material decomposition using spectral computed tomography with an energy-resolved photon-counting detector. Phys Med Biol 2014; 59:5457-82. [PMID: 25164993 DOI: 10.1088/0031-9155/59/18/5457] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Dual-energy computed tomography (CT) techniques have been used to decompose materials and characterize tissues according to their physical and chemical compositions. However, these techniques are hampered by the limitations of conventional x-ray detectors operated in charge integrating mode. Energy-resolved photon-counting detectors provide spectral information from polychromatic x-rays using multiple energy thresholds. These detectors allow simultaneous acquisition of data in different energy ranges without spectral overlap, resulting in more efficient material decomposition and quantification for dual-energy CT. In this study, a pre-reconstruction dual-energy CT technique based on volume conservation was proposed for three-material decomposition. The technique was combined with iterative reconstruction algorithms by using a ray-driven projector in order to improve the quality of decomposition images and reduce radiation dose. A spectral CT system equipped with a CZT-based photon-counting detector was used to implement the proposed dual-energy CT technique. We obtained dual-energy images of calibration and three-material phantoms consisting of low atomic number materials from the optimal energy bins determined by Monte Carlo simulations. The material decomposition process was accomplished by both the proposed and post-reconstruction dual-energy CT techniques. Linear regression and normalized root-mean-square error (NRMSE) analyses were performed to evaluate the quantitative accuracy of decomposition images. The calibration accuracy of the proposed dual-energy CT technique was higher than that of the post-reconstruction dual-energy CT technique, with fitted slopes of 0.97-1.01 and NRMSEs of 0.20-4.50% for all basis materials. In the three-material phantom study, the proposed dual-energy CT technique decreased the NRMSEs of measured volume fractions by factors of 0.17-0.28 compared to the post-reconstruction dual-energy CT technique. It was concluded that the proposed dual-energy CT technique can potentially be used to decompose mixtures into basis materials and characterize tissues according to their composition.
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Affiliation(s)
- Seungwan Lee
- Department of Radiological Science, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon-do 220-710, Republic of Korea
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Li H, Chen X, Wang Y, Zhou Z, Zhu Q, Yu D. Sparse CT reconstruction based on multi-direction anisotropic total variation (MDATV). Biomed Eng Online 2014; 13:92. [PMID: 24993336 PMCID: PMC4127084 DOI: 10.1186/1475-925x-13-92] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 06/23/2014] [Indexed: 11/23/2022] Open
Abstract
Background The sparse CT (Computed Tomography), inspired by compressed sensing, means to introduce a prior information of image sparsity into CT reconstruction to reduce the input projections so as to reduce the potential threat of incremental X-ray dose to patients’ health. Recently, many remarkable works were concentrated on the sparse CT reconstruction from sparse (limited-angle or few-view style) projections. In this paper we would like to incorporate more prior information into the sparse CT reconstruction for improvement of performance. It is known decades ago that the given projection directions can provide information about the directions of edges in the restored CT image. ATV (Anisotropic Total Variation), a TV (Total Variation) norm based regularization, could use the prior information of image sparsity and edge direction simultaneously. But ATV can only represent the edge information in few directions and lose much prior information of image edges in other directions. Methods To sufficiently use the prior information of edge directions, a novel MDATV (Multi-Direction Anisotropic Total Variation) is proposed. In this paper we introduce the 2D-IGS (Two Dimensional Image Gradient Space), and combined the coordinate rotation transform with 2D-IGS to represent edge information in multiple directions. Then by incorporating this multi-direction representation into ATV norm we get the MDATV regularization. To solve the optimization problem based on the MDATV regularization, a novel ART (algebraic reconstruction technique) + MDATV scheme is outlined. And NESTA (NESTerov’s Algorithm) is proposed to replace GD (Gradient Descent) for minimizing the TV-based regularization. Results The numerical and real data experiments demonstrate that MDATV based iterative reconstruction improved the quality of restored image. NESTA is more suitable than GD for minimization of TV-based regularization. Conclusions MDATV regularization can sufficiently use the prior information of image sparsity and edge information simultaneously. By incorporating more prior information, MDATV based approach could reconstruct the image more exactly.
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Affiliation(s)
| | - Xiaodong Chen
- College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
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Kaganovsky Y, Li D, Holmgren A, Jeon H, MacCabe KP, Politte DG, O'Sullivan JA, Carin L, Brady DJ. Compressed sampling strategies for tomography. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2014; 31:1369-94. [PMID: 25121423 DOI: 10.1364/josaa.31.001369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels. We propose a new approach based on quasi-random detector subsampling, whereas previous approaches only addressed subsampling with respect to source location (view angle). The performance of different sampling strategies is considered using object-independent figures of merit, and also based on reconstructions for specific objects, with synthetic and real data. The proposed approach can be implemented using a structured illumination of the interrogated object or the detector array by placing a coded aperture/mask at the source or detector side, respectively. Advantages of the proposed approach include (i) for structured illumination of the detector array, it leads to fewer detector pixels and allows one to integrate detectors for scattered radiation in the unused space; (ii) for structured illumination of the object, it leads to a reduced radiation dose for patients in medical scans; (iii) in the latter case, the blocking of rays reduces scattered radiation while keeping the same energy in the transmitted rays, resulting in a higher signal-to-noise ratio than that achieved by lowering exposure times or the energy of the source; (iv) compared to view-angle subsampling, it allows one to use fewer measurements for the same image quality, or leads to better image quality for the same number of measurements. The proposed approach can also be combined with view-angle subsampling.
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Lee S, Kim HJ. Noise properties of reconstructed images in a kilo-voltage on-board imaging system with iterative reconstruction techniques: A phantom study. Phys Med 2014; 30:365-73. [DOI: 10.1016/j.ejmp.2013.11.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/18/2013] [Accepted: 11/21/2013] [Indexed: 12/01/2022] Open
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Wang J, Gu X. Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT. Med Phys 2014; 40:101912. [PMID: 24089914 DOI: 10.1118/1.4821099] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). METHODS The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT. RESULTS Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively. CONCLUSIONS The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.
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Affiliation(s)
- Jing Wang
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas 75235-8808
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Wang AS, Stayman JW, Otake Y, Kleinszig G, Vogt S, Gallia GL, Khanna AJ, Siewerdsen JH. Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction. Phys Med Biol 2014; 59:1005-26. [PMID: 24504126 PMCID: PMC4046706 DOI: 10.1088/0031-9155/59/4/1005] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.
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Affiliation(s)
- Adam S Wang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
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