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Park JC, Song B, Liang X, Lu B, Tan J, Parisi A, Denbeigh J, Yaddanpudi S, Choi B, Kim JS, Furutani KM, Beltran CJ. A high-resolution cone beam computed tomography (HRCBCT) reconstruction framework for CBCT-guided online adaptive therapy. Med Phys 2023; 50:6490-6501. [PMID: 37690458 DOI: 10.1002/mp.16734] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/18/2023] [Accepted: 08/19/2023] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND Kilo-voltage cone-beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely-used Feldkamp-Davis-Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary-based methods, and prior information-based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART. PURPOSE One of the primary challenges in IR-based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high-resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels. METHODS The HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp-Davis-Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process. RESULTS We evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms. CONCLUSION The proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high-definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.
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Affiliation(s)
- Justin C Park
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Bongyong Song
- Department of Radiation Oncology, University of California San Diego, San Diego, California, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Bo Lu
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Jun Tan
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Alessio Parisi
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | - Janet Denbeigh
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
| | | | - Byongsu Choi
- Department of Radiation Oncology, Mayo Clinic, Florida, USA
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
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Hatamikia S, Biguri A, Kronreif G, Russ T, Kettenbach J, Birkfellner W. Source-detector trajectory optimization for CBCT metal artifact reduction based on PICCS reconstruction. Z Med Phys 2023:S0939-3889(23)00009-0. [PMID: 36973106 DOI: 10.1016/j.zemedi.2023.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 03/29/2023]
Abstract
Precise instrument placement plays a critical role in all interventional procedures, especially percutaneous procedures such as needle biopsies, to achieve successful tumor targeting and increased diagnostic accuracy. C-arm cone beam computed tomography (CBCT) has the potential to precisely visualize the anatomy in direct vicinity of the needle and evaluate the adequacy of needle placement during the intervention, allowing for instantaneous adjustment in case of misplacement. However, even with the most advanced C-arm CBCT devices, it can be difficult to identify the exact needle position on CBCT images due to the strong metal artifacts around the needle. In this study, we proposed a framework for customized trajectory design in CBCT imaging based on Prior Image Constrained Compressed Sensing (PICCS) reconstruction with the goal of reducing metal artifacts in needle-based procedures. We proposed to optimize out-of-plane rotations in three-dimensional (3D) space and minimize projection views while reducing metal artifacts at specific volume of interests (VOIs). An anthropomorphic thorax phantom with a needle inserted inside and two tumor models as the imaging targets were used to validate the proposed approach. The performance of the proposed approach was also evaluated for CBCT imaging under kinematic constraints by simulating some collision areas on the geometry of the C-arm. We compared the result of optimized 3D trajectories using the PICCS algorithm and 20 projections with the result of a circular trajectory with sparse view using PICCS and Feldkamp, Davis, and Kress (FDK), both using 20 projections, and the circular FDK method with 313 projections. For imaging targets 1 and 2, the highest values of structural similarity index measure (SSIM) and universal quality index (UQI) between the reconstructed image from the optimized trajectories and the initial CBCT image at the VOI was calculated 0.7521, 0.7308 and 0.7308, 0.7248 respectively. These results significantly outperformed the FDK method (with 20 and 313 projections) and the PICCS method (20 projections) both using the circular trajectory. Our results showed that the proposed optimized trajectories not only significantly reduce metal artifacts but also suggest a dose reduction for needle-based CBCT interventions, considering the small number of projections used. Furthermore, our results showed that the optimized trajectories are compatible with spatially constrained situations and enable CBCT imaging under kinematic constraints when the standard circular trajectory is not feasible.
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Affiliation(s)
- Sepideh Hatamikia
- Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria; Research center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Ander Biguri
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Gernot Kronreif
- Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria
| | - Tom Russ
- Computer Assisted Clinical Medicine, Heidelberg University, Heidelberg, Germany
| | - Joachim Kettenbach
- Institute of Diagnostic, Interventional Radiology and Nuclear Medicine, Landesklinikum Wiener Neustadt, Wiener Neustadt, Austria
| | - Wolfgang Birkfellner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
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Xu S, Yang B, Xu C, Tian J, Liu Y, Yin L, Liu S, Zheng W, Liu C. Sparse Angle CBCT Reconstruction Based on Guided Image Filtering. Front Oncol 2022; 12:832037. [PMID: 35574417 PMCID: PMC9093219 DOI: 10.3389/fonc.2022.832037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.
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Affiliation(s)
- Siyuan Xu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Yang
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Congcong Xu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiawei Tian
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan Liu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lirong Yin
- Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, United States
| | - Shan Liu
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Wenfeng Zheng
- School of Automation, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Liu
- Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Unité Mixte de Recherche (UMR) 5506, French National Center for Scientific Research (CNRS) - University of Montpellier (UM), Montpellier, France
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Song Y, Zhang W, Zhang H, Wang Q, Xiao Q, Li Z, Wei X, Lai J, Wang X, Li W, Zhong Q, Gong P, Zhong R, Zhao J. Low-dose cone-beam CT (LD-CBCT) reconstruction for image-guided radiation therapy (IGRT) by three-dimensional dual-dictionary learning. Radiat Oncol 2020; 15:192. [PMID: 32787941 PMCID: PMC7425566 DOI: 10.1186/s13014-020-01630-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/29/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To develop a low-dose cone beam CT (LD-CBCT) reconstruction method named simultaneous algebraic reconstruction technique and dual-dictionary learning (SART-DDL) joint algorithm for image guided radiation therapy (IGRT) and evaluate its imaging quality and clinical application ability. METHODS In this retrospective study, 62 CBCT image sets from February 2018 to July 2018 at west china hospital were randomly collected from 42 head and neck patients (mean [standard deviation] age, 49.7 [11.4] years, 12 females and 30 males). All image sets were retrospectively reconstructed by SART-DDL (resultant D-CBCT image sets) with 18% less clinical raw projections. Reconstruction quality was evaluated by quantitative parameters compared with SART and Total Variation minimization (SART-TV) joint reconstruction algorithm with paired t test. Five-grade subjective grading evaluations were done by two oncologists in a blind manner compared with clinically used Feldkamp-Davis-Kress algorithm CBCT images (resultant F-CBCT image sets) and the grading results were compared by paired Wilcoxon rank test. Registration results between D-CBCT and F-CBCT were compared. D-CBCT image geometry fidelity was tested. RESULTS The mean peak signal to noise ratio of D-CBCT was 1.7 dB higher than SART-TV reconstructions (P < .001, SART-DDL vs SART-TV, 36.36 ± 0.55 dB vs 34.68 ± 0.28 dB). All D-CBCT images were recognized as clinically acceptable without significant difference with F-CBCT in subjective grading (P > .05). In clinical registration, the maximum translational and rotational difference was 1.8 mm and 1.7 degree respectively. The horizontal, vertical and sagittal geometry fidelity of D-CBCT were acceptable. CONCLUSIONS The image quality, geometry fidelity and clinical application ability of D-CBCT are comparable to that of the F-CBCT for head-and-neck patients with 18% less projections by SART-DDL.
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Affiliation(s)
- Ying Song
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Weikang Zhang
- The School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, 610065 P. R. China
| | - Hong Zhang
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Qiang Wang
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Qing Xiao
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Zhibing Li
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Xing Wei
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Jialu Lai
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Xuetao Wang
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Wan Li
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Quan Zhong
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Pan Gong
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Renming Zhong
- Department of Radiotherapy, Cancer Center, West China Hospital, Sichuan University, No.37 Guo Xue Alley, Chengdu, 610065 P. R. China
| | - Jun Zhao
- The School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, 610065 P. R. China
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