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Cui J, Hou Y, Jiang Z, Yu G, Ye L, Cao Q, Sun Q. Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation. J Cancer Res Ther 2024; 20:615-624. [PMID: 38687932 DOI: 10.4103/jcrt.jcrt_1761_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 12/01/2023] [Indexed: 05/02/2024]
Abstract
AIM The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. MATERIALS AND METHODS It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions. RESULTS This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves. CONCLUSION In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.
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Affiliation(s)
- Junlong Cui
- Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong Province, China
- Department of Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong Province, China
| | - Yong Hou
- Department of Radiation Oncology, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong Province, China
| | - Zekun Jiang
- Department of College of Computer Science, Sichuan University, Chengdu, Sichuan Province, China
- Department of West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Gang Yu
- Department of Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong Province, China
| | - Lan Ye
- Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong Province, China
| | - Qiang Cao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China
| | - Qian Sun
- Department of Cancer Center, The Second Hospital of Shandong University, Jinan, Shandong Province, China
- Department of Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Wang D, Ning R, Li G, Zhao J, Wang Y, Rong L. 3D image reconstruction of terahertz computed tomography at sparse angles by total variation minimization. APPLIED OPTICS 2022; 61:B1-B7. [PMID: 35201119 DOI: 10.1364/ao.440847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/09/2021] [Indexed: 06/14/2023]
Abstract
Continuous-wave terahertz computed tomography (THz-CT) is an important three-dimensional imaging method for probing the profile and inner properties of a sample's structure. We applied the total variation (TV) minimization iterative algorithm to squeeze 75% data acquisition time of THz-CT without the loss of reconstruction fidelity. The imaging system is built based on a 278.6 GHz avalanche diode source. A zero-order Bessel beam is generated by an axicon, for which the intensity profile remains essentially propagation invariant within the non-diffracting zone. The effectiveness of the proposed method is verified by using three optically opaque objects. The reconstruction results show that the TV-minimization algorithm can effectively suppress noise, artefacts, and shape distortion created in sparse angle reconstruction.
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Zhang L, Zhao H, Zhou Z, Jia M, Zhang L, Jiang J, Gao F. Improving spatial resolution with an edge-enhancement model for low-dose propagation-based X-ray phase-contrast computed tomography. OPTICS EXPRESS 2021; 29:37399-37417. [PMID: 34808812 DOI: 10.1364/oe.440664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/14/2021] [Indexed: 06/13/2023]
Abstract
Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) has been increasingly popular for distinguishing low contrast tissues. Phase retrieval is an important step to quantitatively obtain the phase information before the tomographic reconstructions, while typical phase retrieval methods in PB-PCCT, such as homogenous transport of intensity equation (TIE-Hom), are essentially low-pass filters and thus improve the signal to noise ratio at the expense of the reduced spatial resolution of the reconstructed image. To improve the reconstructed spatial resolution, measured phase contrast projections with high edge enhancement and the phase projections retrieved by TIE-Hom were weighted summed and fed into an iterative tomographic algorithm within the framework of the adaptive steepest descent projections onto convex sets (ASD-POCS), which was employed for suppressing the image noise in low dose reconstructions because of the sparse-view scanning strategy or low exposure time for single phase contrast projection. The merging strategy decreases the accuracy of the linear model of PB-PCCT and would finally lead to the reconstruction failure in iterative reconstructions. Therefore, the additive median root prior is also introduced in the algorithm to partly increase the model accuracy. The reconstructed spatial resolution and noise performance can be flexibly balanced by a pair of antagonistic hyper-parameters. Validations were performed by the established phase-contrast Feldkamp-Davis-Kress, phase-retrieved Feldkamp-Davis-Kress, conventional ASD-POCS and the proposed enhanced ASD-POCS with a numerical phantom dataset and experimental biomaterial dataset. Simulation results show that the proposed algorithm outperforms the conventional ASD-POCS in spatial evaluation assessments such as root mean square error (a ratio of 9.78%), contrast to noise ratio (CNR) (a ratio of 7.46%), and also frequency evaluation assessments such as modulation transfer function (a ratio of 66.48% of MTF50% (50% MTF value)), noise power spectrum (a ratio of 35.25% of f50% (50% value of the Nyquist frequency)) and noise equivalent quanta (1-2 orders of magnitude at high frequencies). Experimental results again confirm the superiority of proposed strategy relative to the conventional one in terms of edge sharpness and CNR (an average increase of 67.35%).
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Zhang Z, Chen B, Xia D, Sidky EY, Pan X. Directional-TV algorithm for image reconstruction from limited-angular-range data. Med Image Anal 2021; 70:102030. [PMID: 33752167 PMCID: PMC8044061 DOI: 10.1016/j.media.2021.102030] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 02/25/2021] [Accepted: 03/01/2021] [Indexed: 01/24/2023]
Abstract
Investigation of image reconstruction from data collected over a limited-angular range in X-ray CT remains a topic of active research because it may yield insight into the development of imaging workflow of practical significance. This reconstruction problem is well-known to be challenging, however, because it is highly ill-conditioned. In the work, we investigate optimization-based image reconstruction from data acquired over a limited-angular range that is considerably smaller than the angular range in short-scan CT. We first formulate the reconstruction problem as a convex optimization program with directional total-variation (TV) constraints applied to the image, and then develop an iterative algorithm, referred to as the directional-TV (DTV) algorithm for image reconstruction through solving the optimization program. We use the DTV algorithm to reconstruct images from data collected over a variety of limited-angular ranges for breast and bar phantoms of clinical- and industrial-application relevance. The study demonstrates that the DTV algorithm accurately recovers the phantoms from data generated over a significantly reduced angular range, and that it considerably diminishes artifacts observed otherwise in reconstructions of existing algorithms. We have also obtained empirical conditions on minimal-angular ranges sufficient for numerically accurate image reconstruction with the DTV algorithm.
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Affiliation(s)
- Zheng Zhang
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
| | - Buxin Chen
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
| | - Dan Xia
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
| | - Emil Y Sidky
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA
| | - Xiaochuan Pan
- Department of Radiology, The University of Chicago, Chicago, IL 60637, USA; Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, USA.
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Matenine D, Schmittbuhl M, Bedwani S, Després P, de Guise JA. Iterative reconstruction for image enhancement and dose reduction in diagnostic cone beam CT imaging. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:805-819. [PMID: 31450539 DOI: 10.3233/xst-190523] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND Iterative reconstruction is well-established in diagnostic multidetector computed tomography (MDCT) for dose reduction and image quality enhancement. Its application to diagnostic cone beam computed tomography (CBCT) is only emerging and warrants a quantitative evaluation. METHODS Several phantoms and a canine head specimen were imaged using a commercially available small-field CBCT scanner. Raw projection data were reconstructed using the Feldkamp-Davis-Kress (FDK) method with different filters, including denoising via total variation (TV) minimization (FDK-TV). Iterative reconstruction was carried out using the TV-regularized ordered subsets convex technique (OSC-TV). Signal-to-noise ratio (SNR), noise power spectrum (NPS) and spatial resolution of images were estimated. Dose levels were measured via the weighted computed tomography dose index, while low-dose image quality degradation was estimated via structural similarity (SSIM). RESULTS OSC-TV and FDK-TV were shown to significantly improve image signal-to-noise ratio (SNR) compared to FDK with a standard filter, 5.8 and 4.0 times, respectively. Spatial resolution attained with different algorithms varied moderately across different experiments. For low-dose acquisitions, image quality decreased dramatically for FDK but not for FDK-TV nor OSC-TV. For low-dose canine head images acquired using about 1/5 of the dose compared to a reference image, SSIM dropped to about 0.3 for FDK, while remaining at 0.92 for FDK-TV and 0.96 for OSC-TV. CONCLUSION OSC-TV was shown to improve image quality compared to FDK and FDK-TV. Moreover, this iterative approach allowed for significant dose reduction while maintaining image quality.
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Affiliation(s)
- Dmitri Matenine
- Laboratoire de recherche en imagerie et orthopédie, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC, Canada
- Département de génie des systèmes, École de technologie supérieure, Montréal, QC, Canada
| | - Matthieu Schmittbuhl
- Laboratoire de recherche en imagerie et orthopédie, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC, Canada
- Faculté de médecine dentaire, Université de Montréal, Montréal, QC, Canada
| | - Stéphane Bedwani
- Département de physique, Université de Montréal, Montréal, QC, Canada
- Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
| | - Philippe Després
- Département de physique, de génie physique et d'optique and Centre de recherche sur le cancer, Université Laval, Québec, QC, Canada
- Département de radio-oncologie and Centre de recherche du CHU de Québec, Québec (QC) G1R 2J6, Canada
| | - Jacques A de Guise
- Laboratoire de recherche en imagerie et orthopédie, Centre de recherche du Centre hospitalier de l'Université de Montréal, Montréal, QC, Canada
- Département de génie des systèmes, École de technologie supérieure, Montréal, QC, Canada
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Zhang H, Wang J, Zeng D, Tao X, Ma J. Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review. Med Phys 2018; 45:e886-e907. [PMID: 30098050 DOI: 10.1002/mp.13123] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 06/22/2018] [Accepted: 08/04/2018] [Indexed: 12/17/2022] Open
Abstract
Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.
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Affiliation(s)
- Hao Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94304, USA
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Xi Tao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
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MacFarlane M, Wong D, Hoover DA, Wong E, Johnson C, Battista JJ, Chen JZ. Patient-specific calibration of cone-beam computed tomography data sets for radiotherapy dose calculations and treatment plan assessment. J Appl Clin Med Phys 2018; 19:249-257. [PMID: 29479821 PMCID: PMC5849848 DOI: 10.1002/acm2.12293] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 12/07/2017] [Accepted: 01/21/2018] [Indexed: 11/24/2022] Open
Abstract
Purpose In this work, we propose a new method of calibrating cone beam computed tomography (CBCT) data sets for radiotherapy dose calculation and plan assessment. The motivation for this patient‐specific calibration (PSC) method is to develop an efficient, robust, and accurate CBCT calibration process that is less susceptible to deformable image registration (DIR) errors. Methods Instead of mapping the CT numbers voxel‐by‐voxel with traditional DIR calibration methods, the PSC methods generates correlation plots between deformably registered planning CT and CBCT voxel values, for each image slice. A linear calibration curve specific to each slice is then obtained by least‐squares fitting, and applied to the CBCT slice's voxel values. This allows each CBCT slice to be corrected using DIR without altering the patient geometry through regional DIR errors. A retrospective study was performed on 15 head‐and‐neck cancer patients, each having routine CBCTs and a middle‐of‐treatment re‐planning CT (reCT). The original treatment plan was re‐calculated on the patient's reCT image set (serving as the gold standard) as well as the image sets produced by voxel‐to‐voxel DIR, density‐overriding, and the new PSC calibration methods. Dose accuracy of each calibration method was compared to the reference reCT data set using common dose‐volume metrics and 3D gamma analysis. A phantom study was also performed to assess the accuracy of the DIR and PSC CBCT calibration methods compared with planning CT. Results Compared with the gold standard using reCT, the average dose metric differences were ≤ 1.1% for all three methods (PSC: −0.3%; DIR: −0.7%; density‐override: −1.1%). The average gamma pass rates with thresholds 3%, 3 mm were also similar among the three techniques (PSC: 95.0%; DIR: 96.1%; density‐override: 94.4%). Conclusions An automated patient‐specific calibration method was developed which yielded strong dosimetric agreement with the results obtained using a re‐planning CT for head‐and‐neck patients.
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Affiliation(s)
- Michael MacFarlane
- London Regional Cancer Program, London Health Science Center, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Daniel Wong
- London Regional Cancer Program, London Health Science Center, London, ON, Canada
| | - Douglas A Hoover
- London Regional Cancer Program, London Health Science Center, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Eugene Wong
- London Regional Cancer Program, London Health Science Center, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Carol Johnson
- London Regional Cancer Program, London Health Science Center, London, ON, Canada
| | - Jerry J Battista
- London Regional Cancer Program, London Health Science Center, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Jeff Z Chen
- London Regional Cancer Program, London Health Science Center, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
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Real-Time Whole-Brain Radiation Therapy: A Single-Institution Experience. Int J Radiat Oncol Biol Phys 2017; 100:1280-1288. [PMID: 29397212 DOI: 10.1016/j.ijrobp.2017.12.282] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/19/2017] [Accepted: 12/20/2017] [Indexed: 11/21/2022]
Abstract
PURPOSE To demonstrate the feasibility of a real-time whole-brain radiation therapy (WBRT) workflow, taking advantage of contemporary radiation therapy capabilities and seeking to optimize clinical workflow for WBRT. METHODS AND MATERIALS We developed a method incorporating the linear accelerator's on-board imaging system for patient simulation, used cone-beam computed tomography (CBCT) data for treatment planning, and delivered the first fraction of prescribed therapy, all during the patient's initial appointment. Simulation was performed in the linear accelerator vault. An acquired CBCT data set was used for scripted treatment planning protocol, providing inversely planned, automated treatment plan generation. The osseous boundaries of the brain were auto-contoured to create a target volume. Two parallel-opposed beams using field-in-field intensity modulate radiation therapy covered this target to the user-defined inferior level (C1 or C2). The method was commissioned using an anthropomorphic head phantom and verified using 100 clinically treated patients. RESULTS Whole-brain target heterogeneity was within 95%-107% of the prescription dose, and target coverage compared favorably to standard, manually created 3-dimensional plans. For the commissioning CBCT datasets, the secondary monitor unit verification and independent 3-dimensional dose distribution comparison for computed and delivered doses were within 2% agreement relative to the scripted auto-plans. On average, time needed to complete the entire process was 35.1 ± 10.3 minutes from CBCT start to last beam delivered. CONCLUSIONS The real-time WBRT workflow using integrated on-site imaging, planning, quality assurance, and delivery was tested and deemed clinically feasible. The design necessitates a synchronized team consisting of physician, physicist, dosimetrist, and therapists. This work serves as a proof of concept of real-time planning and delivery for other treatment sites.
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Jiang Y, Padgett E, Hovden R, Muller DA. Sampling limits for electron tomography with sparsity-exploiting reconstructions. Ultramicroscopy 2017; 186:94-103. [PMID: 29277084 DOI: 10.1016/j.ultramic.2017.12.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Revised: 12/01/2017] [Accepted: 12/06/2017] [Indexed: 10/18/2022]
Abstract
Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized by compressed sensing, sparsity-exploiting algorithms have been applied to experimental ET data and show promise for improving reconstruction quality or reducing the total beam dose applied to a specimen. Nevertheless, theoretical bounds for these methods have been less explored in the context of ET applications. Here, we perform numerical simulations to investigate performance of ℓ1-norm and total-variation (TV) minimization under various imaging conditions. From 36,100 different simulated structures, our results show specimens with more complex structures generally require more projections for exact reconstruction. However, once sufficient data is acquired, dividing the beam dose over more projections provides no improvements-analogous to the traditional dose-fraction theorem. Moreover, a limited tilt range of ±75° or less can result in distorting artifacts in sparsity-exploiting reconstructions. The influence of optimization parameters on reconstructions is also discussed.
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Affiliation(s)
- Yi Jiang
- Department of Physics, Cornell University, Ithaca, NY 14853, United States.
| | - Elliot Padgett
- School of Applied & Engineering Physics, Cornell University, Ithaca, NY 14853, United States
| | - Robert Hovden
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States
| | - David A Muller
- School of Applied & Engineering Physics, Cornell University, Ithaca, NY 14853, United States; Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY 14853, United States
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Liu L, Li X, Xiang K, Wang J, Tan S. Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:2588-2599. [PMID: 29192888 PMCID: PMC5744602 DOI: 10.1109/tmi.2017.2766185] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Cone-beam computed tomography (CBCT) has been widely used in radiation therapy. For accurate patient setup and treatment target localization, it is important to obtain high-quality reconstruction images. The total variation (TV) penalty has shown the state-of-the-art performance in suppressing noise and preserving edges for statistical iterative image reconstruction, but it sometimes leads to the so-called staircase effect. In this paper, we proposed to use a new family of penalties-the Hessian Schatten (HS) penalties-for the CBCT reconstruction. Consisting of the second-order derivatives, the HS penalties are able to reflect the smooth intensity transitions of the underlying image without introducing the staircase effect. We discussed and compared the behaviors of several convex HS penalties with orders 1, 2, and for CBCT reconstruction. We used the majorization-minimization approach with a primal-dual formulation for the corresponding optimization problem. Experiments on two digital phantoms and two physical phantoms demonstrated the proposed penalty family's outstanding performance over TV in suppressing the staircase effect, and the HS penalty with order 1 had the best performance among the HS penalties tested.
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Xia D, Langan DA, Solomon SB, Zhang Z, Chen B, Lai H, Sidky EY, Pan X. Optimization-based image reconstruction with artifact reduction in C-arm CBCT. Phys Med Biol 2016; 61:7300-7333. [PMID: 27694700 PMCID: PMC5109550 DOI: 10.1088/0031-9155/61/20/7300] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.
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Affiliation(s)
- Dan Xia
- Department of Radiology, The University of Chicago, Chicago, IL, USA
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Zhang Z, Ye J, Chen B, Perkins AE, Rose S, Sidky EY, Kao CM, Xia D, Tung CH, Pan X. Investigation of optimization-based reconstruction with an image-total-variation constraint in PET. Phys Med Biol 2016; 61:6055-84. [PMID: 27452653 DOI: 10.1088/0031-9155/61/16/6055] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications.
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Affiliation(s)
- Zheng Zhang
- Department of Radiology, The University of Chicago, Chicago, IL, USA
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Hu Z, Zhang Y, Liu J, Ma J, Zheng H, Liang D. A feature refinement approach for statistical interior CT reconstruction. Phys Med Biol 2016; 61:5311-34. [DOI: 10.1088/0031-9155/61/14/5311] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Zhang Z, Han X, Pearson E, Pelizzari C, Sidky EY, Pan X. Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Phys Med Biol 2016; 61:3387-406. [PMID: 27046218 DOI: 10.1088/0031-9155/61/9/3387] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp-Davis-Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration.
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Affiliation(s)
- Zheng Zhang
- Department of Radiology, The University of Chicago, Chicago, IL, USA
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Qiao Z, Redler G, Epel B, Qian Y, Halpern H. 3D pulse EPR imaging from sparse-view projections via constrained, total variation minimization. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2015; 258:49-57. [PMID: 26225440 PMCID: PMC4827344 DOI: 10.1016/j.jmr.2015.06.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Revised: 06/18/2015] [Accepted: 06/19/2015] [Indexed: 05/13/2023]
Abstract
Tumors and tumor portions with low oxygen concentrations (pO2) have been shown to be resistant to radiation therapy. As such, radiation therapy efficacy may be enhanced if delivered radiation dose is tailored based on the spatial distribution of pO2 within the tumor. A technique for accurate imaging of tumor oxygenation is critically important to guide radiation treatment that accounts for the effects of local pO2. Electron paramagnetic resonance imaging (EPRI) has been considered one of the leading methods for quantitatively imaging pO2 within tumors in vivo. However, current EPRI techniques require relatively long imaging times. Reducing the number of projection scan considerably reduce the imaging time. Conventional image reconstruction algorithms, such as filtered back projection (FBP), may produce severe artifacts in images reconstructed from sparse-view projections. This can lower the utility of these reconstructed images. In this work, an optimization based image reconstruction algorithm using constrained, total variation (TV) minimization, subject to data consistency, is developed and evaluated. The algorithm was evaluated using simulated phantom, physical phantom and pre-clinical EPRI data. The TV algorithm is compared with FBP using subjective and objective metrics. The results demonstrate the merits of the proposed reconstruction algorithm.
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Affiliation(s)
- Zhiwei Qiao
- School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China.
| | - Gage Redler
- Department of Radiation Oncology, Rush University Medical Center, Chicago, IL 60612, USA
| | - Boris Epel
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL 60637, USA
| | - Yuhua Qian
- School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Howard Halpern
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL 60637, USA.
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