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Rossi M, Belotti G, Mainardi L, Baroni G, Cerveri P. Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools. Comput Assist Surg (Abingdon) 2024; 29:2327981. [PMID: 38468391 DOI: 10.1080/24699322.2024.2327981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024] Open
Abstract
Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.
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Affiliation(s)
- Matteo Rossi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Laboratory of Innovation in Sleep Medicine, Istituto Auxologico Italiano, Milan, Italy
| | - Gabriele Belotti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Bioengineering Unit, Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Pietro Cerveri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Laboratory of Innovation in Sleep Medicine, Istituto Auxologico Italiano, Milan, Italy
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Sun P, Yang J, Tian X, Yuan G. Image fusion-based low-dose CBCT enhancement method for visualizing miniscrew insertion in the infrazygomatic crest. BMC Med Imaging 2024; 24:114. [PMID: 38760689 PMCID: PMC11100247 DOI: 10.1186/s12880-024-01289-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/03/2024] [Indexed: 05/19/2024] Open
Abstract
Digital dental technology covers oral cone-beam computed tomography (CBCT) image processing and low-dose CBCT dental applications. A low-dose CBCT image enhancement method based on image fusion is proposed to address the need for subzygomatic small screw insertion. Specifically, firstly, a sharpening correction module is proposed, where the CBCT image is sharpened to compensate for the loss of details in the underexposed/over-exposed region. Secondly, a visibility restoration module based on type II fuzzy sets is designed, and a contrast enhancement module using curve transformation is designed. In addition to this, we propose a perceptual fusion module that fuses visibility and contrast of oral CBCT images. As a result, the problems of overexposure/underexposure, low visibility, and low contrast that occur in oral CBCT images can be effectively addressed with consistent interpretability. The proposed algorithm was analyzed in comparison experiments with a variety of algorithms, as well as ablation experiments. After analysis, compared with advanced enhancement algorithms, this algorithm achieved excellent results in low-dose CBCT enhancement and effective observation of subzygomatic small screw implantation. Compared with the best performing method, the evaluation metric is 0.07-2 higher on both datasets. The project can be found at: https://github.com/sunpeipei2024/low-dose-CBCT .
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Affiliation(s)
- Peipei Sun
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Pediatric Dentistry, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jinghui Yang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Pediatric Dentistry, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Xue Tian
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Pediatric Dentistry, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Guohua Yuan
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China.
- Department of Pediatric Dentistry, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
- Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, China.
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Wen X, Zhao C, Zhao B, Yuan M, Chang J, Liu W, Meng J, Shi L, Yang S, Zeng J, Yang Y. Application of deep learning in radiation therapy for cancer. Cancer Radiother 2024; 28:208-217. [PMID: 38519291 DOI: 10.1016/j.canrad.2023.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 03/24/2024]
Abstract
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.
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Affiliation(s)
- X Wen
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - C Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, China
| | - B Zhao
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - M Yuan
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - J Chang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - W Liu
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Meng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - L Shi
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - S Yang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Zeng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - Y Yang
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
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Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
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Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
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Niu T, Xu L, Ren Q, Gao Y, Luo C, Teng Z, Du J, Ding M, Xie J, Han H, Jiang Y. UBES: Unified scatter correction using ultrafast Boltzmann equation solver for conebeam CT. Comput Biol Med 2024; 170:108045. [PMID: 38325213 DOI: 10.1016/j.compbiomed.2024.108045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/10/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
A semi-analytical solution to the unified Boltzmann equation is constructed to exactly describe the scatter distribution on a flat-panel detector for high-quality conebeam CT (CBCT) imaging. The solver consists of three parts, including the phase space distribution estimator, the effective source constructor and the detector signal extractor. Instead of the tedious Monte Carlo solution, the derived Boltzmann equation solver achieves ultrafast computational capability for scatter signal estimation by combining direct analytical derivation and time-efficient one-dimensional numerical integration over the trajectory along each momentum of the photon phase space distribution. The execution of scatter estimation using the proposed ultrafast Boltzmann equation solver (UBES) for a single projection is finalized in around 0.4 seconds. We compare the performance of the proposed method with the state-of-the-art schemes, including a time-expensive Monte Carlo (MC) method and a conventional kernel-based algorithm using the same dataset, which is acquired from the CBCT scans of a head phantom and an abdominal patient. The evaluation results demonstrate that the proposed UBES method achieves comparable correction accuracy compared with the MC method, while exhibits significant improvements in image quality over learning and kernel-based methods. With the advantages of MC equivalent quality and superfast computational efficiency, the UBES method has the potential to become a standard solution to scatter correction in high-quality CBCT reconstruction.
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Affiliation(s)
- Tianye Niu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China.
| | - Lei Xu
- Department of Radiation Oncology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Qing Ren
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Yajuan Gao
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Chen Luo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China; School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, Zhejiang, China
| | - Ze Teng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China
| | - Jiayi Xie
- Peking University Third Hospital, Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Beijing, China; Department of Automatic, Tsinghua University, Beijing, China
| | - Hongbin Han
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China
| | - Yin Jiang
- Physics Department, Beihang University, Beijing, China
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Hoffmans-Holtzer N, Magallon-Baro A, de Pree I, Slagter C, Xu J, Thill D, Olofsen-van Acht M, Hoogeman M, Petit S. Evaluating AI-generated CBCT-based synthetic CT images for target delineation in palliative treatments of pelvic bone metastasis at conventional C-arm linacs. Radiother Oncol 2024; 192:110110. [PMID: 38272314 DOI: 10.1016/j.radonc.2024.110110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/11/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
PURPOSE One-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients' comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy. MATERIALS AND METHODS CBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins. RESULTS Radiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT. CONCLUSION The evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs.
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Affiliation(s)
- Nienke Hoffmans-Holtzer
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
| | - Alba Magallon-Baro
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
| | - Ilse de Pree
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Cleo Slagter
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Jiaofeng Xu
- Elekta Inc, St. Charles office, 1450 Beale St, St. Charles, MO 63303, USA
| | - Daniel Thill
- Elekta Inc, St. Charles office, 1450 Beale St, St. Charles, MO 63303, USA
| | - Manouk Olofsen-van Acht
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Mischa Hoogeman
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
| | - Steven Petit
- Erasmus MC - Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands
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Luan S, Ding Y, Shao J, Zou B, Yu X, Qin N, Zhu B, Wei W, Xue X. Deep learning for head and neck semi-supervised semantic segmentation. Phys Med Biol 2024; 69:055008. [PMID: 38306968 DOI: 10.1088/1361-6560/ad25c2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/01/2024] [Indexed: 02/04/2024]
Abstract
Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias.Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer.Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms.Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.
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Affiliation(s)
- Shunyao Luan
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Jiakang Shao
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bing Zou
- Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, People's Republic of China
| | - Xiao Yu
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, People's Republic of China
| | - Nannan Qin
- The First Affiliated Hospital of Bengbu Medical College, Bengbu, People's Republic of China
| | - Benpeng Zhu
- School of Integrated Circuits, Laboratory for optoelectronics, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
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Hu Y, Xu S, Li B, Inscoe CR, Tyndall DA, Lee YZ, Lu J, Zhou O. Improving the accuracy of bone mineral density using a multisource CBCT. Sci Rep 2024; 14:3887. [PMID: 38366012 PMCID: PMC10873385 DOI: 10.1038/s41598-024-54529-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
Multisource cone beam computed tomography CBCT (ms-CBCT) has been shown to overcome some of the inherent limitations of a conventional CBCT. The purpose of this study was to evaluate the accuracy of ms-CBCT for measuring the bone mineral density (BMD) of mandible and maxilla compared to the conventional CBCT. The values measured from a multi-detector CT (MDCT) were used as substitutes for the ground truth. An anthropomorphic adult skull and tissue equivalent head phantom and a homemade calibration phantom containing inserts with varying densities of calcium hydroxyapatite were imaged using the ms-CBCT, the ms-CBCT operating in the conventional single source CBCT mode, and two clinical CBCT scanners at similar imaging doses; and a clinical MDCT. The images of the anthropomorphic head phantom were reconstructed and registered, and the cortical and cancellous bones of the mandible and the maxilla were segmented. The measured CT Hounsfield Unit (HU) and Greyscale Value (GV) at multiple region-of-interests were converted to the BMD using scanner-specific calibration functions. The results from the various CBCT scanners were compared to that from the MDCT. Statistical analysis showed a significant improvement in the agreement between the ms-CBCT and MDCT compared to that between the CBCT and MDCT.
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Affiliation(s)
- Yuanming Hu
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Shuang Xu
- Department of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Boyuan Li
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Christina R Inscoe
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Donald A Tyndall
- Department of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yueh Z Lee
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jianping Lu
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Otto Zhou
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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Tegtmeier RC, Kutyreff CJ, Smetanick JL, Hobbis D, Laughlin BS, Toesca DAS, Clouser EL, Rong Y. Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators. Pract Radiat Oncol 2024:S1879-8500(24)00035-3. [PMID: 38325548 DOI: 10.1016/j.prro.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/21/2023] [Accepted: 01/11/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions for intact prostate and prostate bed treatments. METHODS AND MATERIALS DLAS models were trained using 116 iCBCT data sets with manually delineated organs at risk (bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT data sets were used for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared with those obtained for an additional DLAS-based model trained on planning computed tomography (pCT) data sets and for a deformable image registration (DIR)-based automatic contour propagation method. RESULTS In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all organs at risk and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes of interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, and approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines. CONCLUSIONS The proposed method presents the potential for improved segmentation accuracy and efficiency compared with the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.
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Affiliation(s)
- Riley C Tegtmeier
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | | | | | - Dean Hobbis
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona; Department of Radiation Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Brady S Laughlin
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | | | - Edward L Clouser
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona
| | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona.
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10
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Piao Z, Deng W, Huang S, Lin G, Qin P, Li X, Wu W, Qi M, Zhou L, Li B, Ma J, Xu Y. Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning. Med Phys 2024; 51:1163-1177. [PMID: 37459053 DOI: 10.1002/mp.16618] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 06/11/2023] [Accepted: 06/26/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Scattering photons can seriously contaminate cone-beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality-related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. PURPOSE Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. METHODS Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q-network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U-net based scatter estimation approach for comparison. RESULTS The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal-to-noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware-based beam stop array algorithm to obtain the scatter-free projections as a comparison baseline, and our method can achieve superior performance with MAPE < 17.79% and PSNR > 16.34 dB. CONCLUSIONS In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.
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Affiliation(s)
- Zun Piao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wenxin Deng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shuang Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Guoqin Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Peishan Qin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xu Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wangjiang Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Mengke Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Bin Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianhui Ma
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuan Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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Cobanaj M, Corti C, Dee EC, McCullum L, Boldrini L, Schlam I, Tolaney SM, Celi LA, Curigliano G, Criscitiello C. Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow. Eur J Cancer 2024; 198:113504. [PMID: 38141549 DOI: 10.1016/j.ejca.2023.113504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/25/2023]
Abstract
Patient care workflows are highly multimodal and intertwined: the intersection of data outputs provided from different disciplines and in different formats remains one of the main challenges of modern oncology. Artificial Intelligence (AI) has the potential to revolutionize the current clinical practice of oncology owing to advancements in digitalization, database expansion, computational technologies, and algorithmic innovations that facilitate discernment of complex relationships in multimodal data. Within oncology, radiation therapy (RT) represents an increasingly complex working procedure, involving many labor-intensive and operator-dependent tasks. In this context, AI has gained momentum as a powerful tool to standardize treatment performance and reduce inter-observer variability in a time-efficient manner. This review explores the hurdles associated with the development, implementation, and maintenance of AI platforms and highlights current measures in place to address them. In examining AI's role in oncology workflows, we underscore that a thorough and critical consideration of these challenges is the only way to ensure equitable and unbiased care delivery, ultimately serving patients' survival and quality of life.
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Affiliation(s)
- Marisa Cobanaj
- National Center for Radiation Research in Oncology, OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Chiara Corti
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy.
| | - Edward C Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lucas McCullum
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laura Boldrini
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Ilana Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Leo A Celi
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology (DIPO), University of Milan, Milan, Italy
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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de Koster RJC, Thummerer A, Scandurra D, Langendijk JA, Both S. Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy. Med Phys 2023; 50:8023-8033. [PMID: 37831597 DOI: 10.1002/mp.16782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations. PURPOSE To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice. METHODS Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. RESULTS For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. CONCLUSIONS Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.
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Affiliation(s)
- Rutger J C de Koster
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Daniel Scandurra
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Yang B, Liu Y, Zhu J, Dai J, Men K. Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios. Med Phys 2023; 50:7641-7653. [PMID: 37345371 DOI: 10.1002/mp.16562] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. PURPOSE In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications. METHODS The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. RESULTS The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test). CONCLUSION We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.
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Affiliation(s)
- Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Liu X, Yang R, Xiong T, Yang X, Li W, Song L, Zhu J, Wang M, Cai J, Geng L. CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset. Cancers (Basel) 2023; 15:5479. [PMID: 38001738 PMCID: PMC10670900 DOI: 10.3390/cancers15225479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. MATERIALS AND METHODS A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder-decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. RESULTS The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. CONCLUSIONS Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.
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Affiliation(s)
- Xi Liu
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Ruijie Yang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Tianyu Xiong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Xueying Yang
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Wen Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Liming Song
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Jiarui Zhu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Mingqing Wang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing 100191, China; (R.Y.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China; (T.X.)
| | - Lisheng Geng
- School of Physics, Beihang University, Beijing 102206, China; (X.L.); (X.Y.)
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing 102206, China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing 100191, China
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de Hond YJM, van Haaren PMA, Verrijssen AE, Tijssen RHN, Hurkmans CW. Inter-observer variability in library plan selection on iterative CBCT and synthetic CT images of cervical cancer patients. J Appl Clin Med Phys 2023; 24:e14170. [PMID: 37788333 PMCID: PMC10647946 DOI: 10.1002/acm2.14170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/11/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023] Open
Abstract
INTRODUCTION In the Library-of-Plans (LoP) approach, correct plan selection is essential for delivering radiotherapy treatment accurately. However, poor image quality of the cone-beam computed tomography (CBCT) may introduce inter-observer variability and thereby hamper accurate plan selection. In this study, we investigated whether new techniques to improve the CBCT image quality and improve consistency in plan selection, affects the accuracy of LoP selection in cervical cancer patients. MATERIALS AND METHODS CBCT images of 12 patients were used to investigate the inter-observer variability of plan selection based on different CBCT image types. Six observers were asked to individually select a plan based on clinical X-ray Volumetric Imaging (XVI) CBCT, iterative reconstructed CBCT (iCBCT) and synthetic CTs (sCT). Selections were performed before and after a consensus meeting with the entire group, in which guidelines were created. A scoring by all observers on the image quality and plan selection procedure was also included. For plan selection, Fleiss' kappa (κ) statistical test was used to determine the inter-observer variability within one image type. RESULTS The agreement between observers was significantly higher on sCT compared to CBCT. The consensus meeting improved the duration and inter-observer variability. In this manuscript, the guidelines attributed the overall results in the plan selection. Before the meeting, the gold standard was selected in 76% of the cases on XVI CBCT, 74% on iCBCT, and 76% on sCT. After the meeting, the gold standard was selected in 83% of the cases on XVI CBCT, 81% on iCBCT, and 90% on sCT. CONCLUSION The use of sCTs can increase the agreement of plan selection among observers and the gold standard was indicated to be selected more often. It is important that clear guidelines for plan selection are implemented in order to benefit from the increased image quality, accurate selection, and decrease inter-observer variability.
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Affiliation(s)
- Yvonne J. M. de Hond
- Department of Radiation OncologyCatharina Hospital EindhovenEindhovenThe Netherlands
| | | | | | - Rob H. N. Tijssen
- Department of Radiation OncologyCatharina Hospital EindhovenEindhovenThe Netherlands
| | - Coen W. Hurkmans
- Department of Radiation OncologyCatharina Hospital EindhovenEindhovenThe Netherlands
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Pang B, Si H, Liu M, Fu W, Zeng Y, Liu H, Cao T, Chang Y, Quan H, Yang Z. Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy. Med Phys 2023; 50:6920-6930. [PMID: 37800874 DOI: 10.1002/mp.16777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/09/2023] [Accepted: 09/17/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning. PURPOSE This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer. METHODS CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network (cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics. RESULTS The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2 mm) has reached 99.02%, and the WET difference is only 1.28 mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model. CONCLUSIONS The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.
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Affiliation(s)
- Bo Pang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Hang Si
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Muyu Liu
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Wensheng Fu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiling Zeng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Hongyuan Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Cao
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Quan
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Wang T, Liu X, Dai J, Zhang C, He W, Liu L, Chan Y, He Y, Zhao H, Xie Y, Liang X. An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image. Comput Biol Med 2023; 165:107377. [PMID: 37651766 DOI: 10.1016/j.compbiomed.2023.107377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/08/2023] [Accepted: 08/14/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality. METHODS In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction. Initially, we transformed low-quality CBCT into high-quality synthetic pCT (spCT) and generated forward projections of CBCT and spCT. By computing the difference between these projections, we obtained a residual image containing image details and scatter artifacts. Image details primarily comprise high-frequency signals, while scatter artifacts consist mainly of low-frequency signals. We extracted the scatter projection signal by applying a low-pass filter to remove image details. The corrected CBCT (cCBCT) projection signal was obtained by subtracting the scatter artifacts projection signal from the original CBCT projection. Finally, we employed the FDK reconstruction algorithm to generate the cCBCT image. RESULTS To evaluate cCBCT image quality, we aligned the CBCT and pCT of six patients. In comparison to CBCT, cCBCT maintains anatomical consistency and significantly enhances CT number, spatial homogeneity, and artifact suppression. The mean absolute error (MAE) of the test data decreased from 88.0623 ± 26.6700 HU to 17.5086 ± 3.1785 HU. The MAE of fat regions of interest (ROIs) declined from 370.2980 ± 64.9730 HU to 8.5149 ± 1.8265 HU, and the error between their maximum and minimum CT numbers decreased from 572.7528 HU to 132.4648 HU. The MAE of muscle ROIs reduced from 354.7689 ± 25.0139 HU to 16.4475 ± 3.6812 HU. We also compared our proposed method with several conventional unsupervised synthetic image generation techniques, demonstrating superior performance. CONCLUSIONS Our approach effectively enhances CBCT image quality and shows promising potential for future clinical adoption.
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Affiliation(s)
- Tangsheng Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101408, China.
| | - Xuan Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Chulong Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Wenfeng He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Lin Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101408, China.
| | - Yinping Chan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Yutong He
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Hanqing Zhao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing 101408, China.
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
| | - Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
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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] [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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Wynne JF, Lei Y, Pan S, Wang T, Pasha M, Luca K, Roper J, Patel P, Patel SA, Godette K, Jani AB, Yang X. Rapid unpaired CBCT-based synthetic CT for CBCT-guided adaptive radiotherapy. J Appl Clin Med Phys 2023; 24:e14064. [PMID: 37345557 PMCID: PMC10562022 DOI: 10.1002/acm2.14064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/18/2023] [Accepted: 05/15/2023] [Indexed: 06/23/2023] Open
Abstract
In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN using mean absolute error, structural similarity index, root mean squared error, and Frèchet Inception Distance and show that CUT significantly outperforms cycleGAN while requiring less time and fewer resources. The investigated method improves the feasibility of online adaptive radiotherapy over the present state-of-the-art.
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Affiliation(s)
- Jacob F. Wynne
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Shaoyan Pan
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tonghe Wang
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
| | - Mosa Pasha
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Kirk Luca
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Sagar A. Patel
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Karen Godette
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Ashesh B. Jani
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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Zhang X, Jiang Y, Luo C, Li D, Niu T, Yu G. Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network. Med Phys 2023; 50:5002-5019. [PMID: 36734321 DOI: 10.1002/mp.16277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 12/23/2022] [Accepted: 01/23/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. PURPOSE To restore low-quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. METHODS In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U-shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter-window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. RESULT Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. CONCLUSIONS Accurately capturing the features at different levels is greatly beneficial for reconstructing high-quality scatter-free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image-guided radiation therapy.
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Affiliation(s)
- Xueren Zhang
- 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, China
| | - Yangkang Jiang
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chen Luo
- Shenzhen Bay Laboratory, Shenzhen, China
- School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, China
| | - Dengwang Li
- 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, China
| | - Tianye Niu
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Gang Yu
- 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, China
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22
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Wei R, Liu Y, Chen X, Zhu J, Yang B, Men K, Dai J. A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter. Med Phys 2023; 50:5045-5060. [PMID: 37006163 DOI: 10.1002/mp.16322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. PURPOSE To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter (NLMF) based on a reference digitally reconstructed radiograph (DRR). METHODS A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a NLMF based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, the image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. RESULTS The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image quality showed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. CONCLUSIONS The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients.
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Affiliation(s)
- Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Deng L, Zhang Y, Wang J, Huang S, Yang X. Improving performance of medical image alignment through super-resolution. Biomed Eng Lett 2023; 13:397-406. [PMID: 37519883 PMCID: PMC10382383 DOI: 10.1007/s13534-023-00268-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/29/2023] [Accepted: 02/01/2023] [Indexed: 02/21/2023] Open
Abstract
Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.
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Affiliation(s)
- Liwei Deng
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
| | - Yuanzhi Zhang
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
| | - Jing Wang
- Faculty of Rehabilitation Medicine, Biofeedback Laboratory, Guangzhou Xinhua University, Guangzhou, 510520 Guangdong China
| | - Sijuan Huang
- Department of Radiation Oncology State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060 Guangdong China
| | - Xin Yang
- Department of Radiation Oncology State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, 510060 Guangdong China
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24
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Uh J, Wang C, Jordan JA, Pirlepesov F, Becksfort JB, Ates O, Krasin MJ, Hua CH. A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration. Phys Med Biol 2023; 68:10.1088/1361-6560/ace754. [PMID: 37442128 PMCID: PMC10846632 DOI: 10.1088/1361-6560/ace754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/13/2023] [Indexed: 07/15/2023]
Abstract
Objective. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.Approach. CBCT, the same-day repeat CT, and the planning CT (pCT) of 81 pediatric patients were used for training (n= 60), validation (n= 6), and testing (n= 15) of the method. The proposed method hybridizes unsupervised deep learning (CycleGAN) and deformable image registration (DIR) of the pCT to CBCT. The CycleGAN and DIR are respectively applied to generate the geometry-weighted (high spatial-frequency) and intensity-weighted (low spatial-frequency) components of the sCT, thereby each process deals with only the component weighted toward its strength. The resultant sCT is further improved in bowel gas regions and other tissues by iteratively feeding back the sCT to adjust incorrect DIR and by increasing the contribution of the deformed pCT in regions of accurate DIR.Main results. The hybrid sCT was more accurate than deformed pCT and CycleGAN-only sCT as indicated by the smaller mean absolute error in CT numbers (28.7 ± 7.1 HU versus 38.8 ± 19.9 HU/53.2 ± 5.5 HU;P≤ 0.012) and higher Dice similarity of the internal gas regions (0.722 ± 0.088 versus 0.180 ± 0.098/0.659 ± 0.129;P≤ 0.002). Accordingly, the hybrid method resulted in more accurate proton range for the beams intersecting gas pockets (11 fields in 6 patients) than the individual methods (the 90th percentile error in 80% distal fall-off, 1.8 ± 0.6 mm versus 6.5 ± 7.8 mm/3.7 ± 1.5 mm;P≤ 0.013). The gamma passing rates also showed a significant dosimetric advantage by the hybrid method (99.7 ± 0.8% versus 98.4 ± 3.1%/98.3 ± 1.8%;P≤ 0.007).Significance. The hybrid method significantly improved the accuracy of sCT and showed promises in CBCT-based proton range verification and adaptive replanning of abdominal/pelvic proton therapy even when gas pockets are present in the beam path.
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Affiliation(s)
- Jinsoo Uh
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Chuang Wang
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Jacob A Jordan
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
- College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States of America
| | - Fakhriddin Pirlepesov
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Jared B Becksfort
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Ozgur Ates
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Matthew J Krasin
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America
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Park CS, Kang SR, Kim JE, Huh KH, Lee SS, Heo MS, Han JJ, Yi WJ. Validation of bone mineral density measurement using quantitative CBCT image based on deep learning. Sci Rep 2023; 13:11921. [PMID: 37488135 PMCID: PMC10366160 DOI: 10.1038/s41598-023-38943-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/17/2023] [Indexed: 07/26/2023] Open
Abstract
The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R2 (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm3, MAE of 67.94 mg/cm3, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm3, 460.52 mg/cm3, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R2 of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm3, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm3. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.
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Grants
- Project Number: 1711174552, KMDF_PR_20200901_0147 Korea Medical Device Development Fund Grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety)
- Project Number: 1711174543, KMDF_PR_20200901_0011 Korea Medical Device Development Fund Grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety)
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Affiliation(s)
- Chan-Soo Park
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Se-Ryong Kang
- Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Jeong-Joon Han
- Department of Oral and Maxillofacial Surgery, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Won-Jin Yi
- Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea.
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
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Taasti VT, Hattu D, Peeters S, van der Salm A, van Loon J, de Ruysscher D, Nilsson R, Andersson S, Engwall E, Unipan M, Canters R. Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients. Phys Imaging Radiat Oncol 2023; 27:100459. [PMID: 37397874 PMCID: PMC10314284 DOI: 10.1016/j.phro.2023.100459] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 07/04/2023] Open
Abstract
Background and purpose Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients. Materials and methods Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs. Results There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods. Conclusion Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.
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Affiliation(s)
- Vicki Trier Taasti
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Djoya Hattu
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Anke van der Salm
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Judith van Loon
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | | | - Mirko Unipan
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
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Szmul A, Taylor S, Lim P, Cantwell J, Moreira I, Zhang Y, D’Souza D, Moinuddin S, Gaze MN, Gains J, Veiga C. Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy. Phys Med Biol 2023; 68:105006. [PMID: 36996837 PMCID: PMC10160738 DOI: 10.1088/1361-6560/acc921] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/01/2023]
Abstract
Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning.Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics.Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline).Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.
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Affiliation(s)
- Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Sabrina Taylor
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Pei Lim
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Jessica Cantwell
- Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Isabel Moreira
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Ying Zhang
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Derek D’Souza
- Radiotherapy Physics Services, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Syed Moinuddin
- Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Mark N. Gaze
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Jennifer Gains
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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Zhang X, Sisniega A, Zbijewski WB, Lee J, Jones CK, Wu P, Han R, Uneri A, Vagdargi P, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Combining physics-based models with deep learning image synthesis and uncertainty in intraoperative cone-beam CT of the brain. Med Phys 2023; 50:2607-2624. [PMID: 36906915 PMCID: PMC10175241 DOI: 10.1002/mp.16351] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/03/2023] [Accepted: 02/27/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention. PURPOSE To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality. METHODS The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data. RESULTS CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images. CONCLUSIONS DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery.
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Affiliation(s)
- Xiaoxuan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Wojciech B. Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Junghoon Lee
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Craig K. Jones
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Pengwei Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Runze Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ali Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Prasad Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Mark Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
| | - William S. Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
| | - Jeffrey H. Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD 21218, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
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Yang P, Ge X, Tsui T, Liang X, Xie Y, Hu Z, Niu T. Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1495-1508. [PMID: 37015393 DOI: 10.1109/tmi.2022.3231461] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.
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30
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Jihong C, Kerun Q, Kaiqiang C, Xiuchun Z, Yimin Z, Penggang B. CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma. Sci Rep 2023; 13:6624. [PMID: 37095147 PMCID: PMC10125979 DOI: 10.1038/s41598-023-33472-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/13/2023] [Indexed: 04/26/2023] Open
Abstract
This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error (MAE) were used to quantify the image quality. For validations, the contours and treatment plans in CT images were transferred to original CBCT, CBCT_cor, SCT1 and SCT2 for dosimetric comparison. Dose distribution, dosimetric parameters and 3D gamma passing rate were analyzed. Compared with rigidly registered CT (RCT), the MAE of CBCT, CBCT_cor, SCT1 and SCT2 were 346.11 ± 13.58 HU, 145.95 ± 17.64 HU, 105.62 ± 16.08 HU and 83.51 ± 7.71 HU, respectively. Moreover, the average dosimetric parameter differences for the CBCT_cor, SCT1 and SCT2 were 2.7% ± 1.4%, 1.2% ± 1.0% and 0.6% ± 0.6%, respectively. Using the dose distribution of RCT images as reference, the 3D gamma passing rate of the hybrid method was significantly better than the other methods. The effectiveness of CBCT-based sCT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma was confirmed. The image quality and dose accuracy of SCT2 were outperform the simple CycleGAN method. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.
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Affiliation(s)
- Chen Jihong
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Quan Kerun
- Department of Radiation Oncology, Xiangtan City Central Hospital, Xiangtan, 411100, Hunan, China
| | - Chen Kaiqiang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Zhang Xiuchun
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Zhou Yimin
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Bai Penggang
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China.
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Suwanraksa C, Bridhikitti J, Liamsuwan T, Chaichulee S. CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer. Cancers (Basel) 2023; 15:cancers15072017. [PMID: 37046678 PMCID: PMC10093508 DOI: 10.3390/cancers15072017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dynamically estimate the correct labels, allowing supervised learning with noisy labels. The study developed and evaluated the approach using imaging data from 146 patients with head and neck cancer. The results showed that GAN trained with RegNet performed better than those trained without RegNet. Specifically, in the UNIT model trained with RegNet, the mean absolute error (MAE) was reduced from 40.46 to 37.21, the root mean-square error (RMSE) was reduced from 119.45 to 108.86, the peak signal-to-noise ratio (PSNR) was increased from 28.67 to 29.55, and the structural similarity index (SSIM) was increased from 0.8630 to 0.8791. The sCT generated from the model had fewer artifacts and retained the anatomical information as in CBCT.
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Gao L, Xie K, Sun J, Lin T, Sui J, Yang G, Ni X. Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy. Med Phys 2023; 50:879-893. [PMID: 36183234 DOI: 10.1002/mp.16017] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/02/2022] [Accepted: 09/25/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. PURPOSE To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high-quality sCT images from CBCT and achieve an accurate dose calculation. METHODS The proposed SARN was trained in a self-supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycleGAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. RESULTS The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. CONCLUSIONS The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high-quality sCT images with fewer artifacts, high-accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.
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Affiliation(s)
- Liugang Gao
- School of Computer Science and Engineering, Southeast University, Nanjing, China
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Kai Xie
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Jiawei Sun
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Tao Lin
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Jianfeng Sui
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Guanyu Yang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xinye Ni
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
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Wang H, Liu X, Kong L, Huang Y, Chen H, Ma X, Duan Y, Shao Y, Feng A, Shen Z, Gu H, Kong Q, Xu Z, Zhou Y. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol 2023; 199:485-497. [PMID: 36688953 PMCID: PMC10133081 DOI: 10.1007/s00066-022-02039-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/04/2022] [Indexed: 01/24/2023]
Abstract
OBJECTIVE This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. METHODS The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. RESULTS The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. CONCLUSION The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.
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Affiliation(s)
- Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China.,Institute of Modern Physics, Fudan University, Shanghai, China
| | - Xiao Liu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | | | - Ying Huang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Xiurui Ma
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yanhua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Aihui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Zhenjiong Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Hengle Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yongkang Zhou
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.
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Pai S, Hadzic I, Rao C, Zhovannik I, Dekker A, Traverso A, Asteriadis S, Hortal E. Frequency-Domain-Based Structure Losses for CycleGAN-Based Cone-Beam Computed Tomography Translation. SENSORS (BASEL, SWITZERLAND) 2023; 23:1089. [PMID: 36772129 PMCID: PMC9920313 DOI: 10.3390/s23031089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/28/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain. We apply this loss to the use-case of cone-beam computed tomography (CBCT) translation to computed tomography (CT)-like quality. Synthetic CT (sCT) images generated from our methods are compared against baseline CycleGAN along with other existing structure losses proposed in the literature. Our methods (MAE: 85.5, MSE: 20433, NMSE: 0.026, PSNR: 30.02, SSIM: 0.935) quantitatively and qualitatively improve over the baseline CycleGAN (MAE: 88.8, MSE: 24244, NMSE: 0.03, PSNR: 29.37, SSIM: 0.935) across all investigated metrics and are more robust than existing methods. Furthermore, no observable artifacts or loss in image quality were observed. Finally, we demonstrated that sCTs generated using our methods have superior performance compared to the original CBCT images on selected downstream tasks.
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Affiliation(s)
- Suraj Pai
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Ibrahim Hadzic
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Chinmay Rao
- Division of Image Processing, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Ivan Zhovannik
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Andre Dekker
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Alberto Traverso
- GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Stylianos Asteriadis
- Department of Advanced Computing Sciences, Maastricht University, 6229 EN Maastricht, The Netherlands
| | - Enrique Hortal
- Department of Advanced Computing Sciences, Maastricht University, 6229 EN Maastricht, The Netherlands
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Ryu K, Lee C, Han Y, Pang S, Kim YH, Choi C, Jang I, Han SS. Multi-planar 2.5D U-Net for image quality enhancement of dental cone-beam CT. PLoS One 2023; 18:e0285608. [PMID: 37167217 PMCID: PMC10174510 DOI: 10.1371/journal.pone.0285608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/26/2023] [Indexed: 05/13/2023] Open
Abstract
Cone-beam computed tomography (CBCT) can provide 3D images of a targeted area with the advantage of lower dosage than multidetector computed tomography (MDCT; also simply referred to as CT). However, in CBCT, due to the cone-shaped geometry of the X-ray source and the absence of post-patient collimation, the presence of more scattering rays deteriorates the image quality compared with MDCT. CBCT is commonly used in dental clinics, and image artifacts negatively affect the radiology workflow and diagnosis. Studies have attempted to eliminate image artifacts and improve image quality; however, a vast majority of that work sacrificed structural details of the image. The current study presents a novel approach to reduce image artifacts while preserving details and sharpness in the original CBCT image for precise diagnostic purposes. We used MDCT images as reference high-quality images. Pairs of CBCT and MDCT scans were collected retrospectively at a university hospital, followed by co-registration between the CBCT and MDCT images. A contextual loss-optimized multi-planar 2.5D U-Net was proposed. Images corrected using this model were evaluated quantitatively and qualitatively by dental clinicians. The quantitative metrics showed superior quality in output images compared to the original CBCT. In the qualitative evaluation, the generated images presented significantly higher scores for artifacts, noise, resolution, and overall image quality. This proposed novel approach for noise and artifact reduction with sharpness preservation in CBCT suggests the potential of this method for diagnostic imaging.
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Affiliation(s)
- Kanghyun Ryu
- Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology, Seoul, South Korea
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Yoseob Han
- College of Information Technology, Department of Electronic Engineering, IT Convergence Major, Soongsil University, Seoul, Korea
- Department of Radiology, Harvard Medical School, Boston, MA, United States of America
| | - Subeen Pang
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Young Hyun Kim
- Department of R&D Performance Evaluation, Korea Health Industry Development Institute (KHIDI), Cheongju, South Korea
| | - Chanyeol Choi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Ikbeom Jang
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
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Jiang Y, Shang F, Peng J, Liang J, Fan Y, Yang Z, Qi Y, Yang Y, Xu T, Jiang R. Automatic Masseter Muscle Accurate Segmentation from CBCT Using Deep Learning-Based Model. J Clin Med 2022; 12:jcm12010055. [PMID: 36614860 PMCID: PMC9820952 DOI: 10.3390/jcm12010055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 12/24/2022] Open
Abstract
Segmentation of the masseter muscle (MM) on cone-beam computed tomography (CBCT) is challenging due to the lack of sufficient soft-tissue contrast. Moreover, manual segmentation is laborious and time-consuming. The purpose of this study was to propose a deep learning-based automatic approach to accurately segment the MM from CBCT under the refinement of high-quality paired computed tomography (CT). Fifty independent CBCT and 42 clinically hard-to-obtain paired CBCT and CT were manually annotated by two observers. A 3D U-shape network was carefully designed to segment the MM effectively. Manual annotations on CT were set as the ground truth. Additionally, an extra five CT and five CBCT auto-segmentation results were revised by one oral and maxillofacial anatomy expert to evaluate their clinical suitability. CBCT auto-segmentation results were comparable to the CT counterparts and significantly improved the similarity with the ground truth compared with manual annotations on CBCT. The automatic approach was more than 332 times shorter than that of a human operation. Only 0.52% of the manual revision fraction was required. This automatic model could simultaneously and accurately segment the MM structures on CBCT and CT, which can improve clinical efficiency and efficacy, and provide critical information for personalized treatment and long-term follow-up.
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Affiliation(s)
- Yiran Jiang
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
| | - Fangxin Shang
- Intelligent Healthcare Unit, Baidu, Beijing 100081, China
| | - Jiale Peng
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
| | - Jie Liang
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing 100081, China
| | - Yi Fan
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
| | - Zhongpeng Yang
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
| | - Yuhan Qi
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
| | - Yehui Yang
- Intelligent Healthcare Unit, Baidu, Beijing 100081, China
| | - Tianmin Xu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
- Correspondence: (T.X.); (R.J.); Tel.: +86-10-8219-5330 (T.X.); +86-10-8129-5737 (R.J.)
| | - Ruoping Jiang
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100081, China
- National Clinical Research Center for Oral Diseases, Beijing 100081, China
- National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing 100081, China
- NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China
- Correspondence: (T.X.); (R.J.); Tel.: +86-10-8219-5330 (T.X.); +86-10-8129-5737 (R.J.)
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Hamming VC, Andersson S, Maduro JH, Langendijk JA, Both S, Sijtsema NM. Daily dose evaluation based on corrected CBCTs for breast cancer patients: accuracy of dose and complication risk assessment. Radiat Oncol 2022; 17:205. [PMID: 36510254 PMCID: PMC9746176 DOI: 10.1186/s13014-022-02174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/30/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES The goal of this study is to validate different CBCT correction methods to select the superior method that can be used for dose evaluation in breast cancer patients with large anatomical changes treated with photon irradiation. MATERIALS AND METHOD Seventy-six breast cancer patients treated with a partial VMAT photon technique (70% conformal, 30% VMAT) were included in this study. All patients showed at least a 5 mm variation (swelling or shrinkage) of the breast on the CBCT compared to the planning-CT (pCT) and had a repeat-CT (rCT) for dose evaluation acquired within 3 days of this CBCT. The original CBCT was corrected using four methods: (1) HU-override correction (CBCTHU), (2) analytical correction and conversion (CBCTCC), (3) deep learning (DL) correction (CTDL) and (4) virtual correction (CTV). Image quality evaluation consisted of calculating the mean absolute error (MAE) and mean error (ME) within the whole breast clinical target volume (CTV) and the field of view of the CBCT minus 2 cm (CBCT-ROI) with respect to the rCT. The dose was calculated on all image sets using the clinical treatment plan for dose and gamma passing rate analysis. RESULTS The MAE of the CBCT-ROI was below 66 HU for all corrected CBCTs, except for the CBCTHU with a MAE of 142 HU. No significant dose differences were observed in the CTV regions in the CBCTCC, CTDL and CTv. Only the CBCTHU deviated significantly (p < 0.01) resulting in 1.7% (± 1.1%) average dose deviation. Gamma passing rates were > 95% for 2%/2 mm for all corrected CBCTs. CONCLUSION The analytical correction and conversion, deep learning correction and virtual correction methods can be applied for an accurate CBCT correction that can be used for dose evaluation during the course of photon radiotherapy of breast cancer patients.
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Affiliation(s)
- Vincent C. Hamming
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | | | - John H. Maduro
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Johannes A. Langendijk
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Stefan Both
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Nanna M. Sijtsema
- grid.4830.f0000 0004 0407 1981Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Lim R, Penoncello GP, Hobbis D, Harrington DP, Rong Y. Technical note: Characterization of novel iterative reconstructed cone beam CT images for dose tracking and adaptive radiotherapy on L-shape linacs. Med Phys 2022; 49:7715-7732. [PMID: 36031929 DOI: 10.1002/mp.15943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 08/05/2022] [Accepted: 08/10/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Cone-beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT). PURPOSE This study aims to characterize iCBCT image quality, establish image value (HU)-to-relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites. MATERIAL AND METHODS Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On-Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp-Davis-Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site-specific HU-RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK-based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison. RESULTS Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU-RED calibration from CT showed 0.5%-2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site-specific HU-RED calibration curves. CONCLUSION iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU-RED calibration curve from CT.
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Affiliation(s)
- Rebecca Lim
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Physics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Gregory P Penoncello
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA.,Department of Radiation Oncology, University of Colorado, Aurora, Colorado, USA
| | - Dean Hobbis
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | | | - Yi Rong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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Cheon W, Jeong S, Jeong JH, Lim YK, Shin D, Lee SB, Lee DY, Lee SU, Suh YG, Moon SH, Kim TH, Kim H. Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer. Cancers (Basel) 2022; 14:cancers14235893. [PMID: 36497374 PMCID: PMC9741368 DOI: 10.3390/cancers14235893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022] Open
Abstract
This research addresses the problem of interobserver variability (IOV), in which different oncologists manually delineate varying primary gross tumor volume (pGTV) contours, adding risk to targeted radiation treatments. Thus, a method of IOV reduction is urgently needed. Hypothesizing that the radiation oncologist’s IOV may shrink with the aid of IOV maps, we propose IOV prediction network (IOV-Net), a deep-learning model that uses the fuzzy membership function to produce high-quality maps based on computed tomography (CT) images. To test the prediction accuracy, a ground-truth pGTV IOV map was created using the manual contour delineations of radiation therapy structures provided by five expert oncologists. Then, we tasked IOV-Net with producing a map of its own. The mean squared error (prediction vs. ground truth) and its standard deviation were 0.0038 and 0.0005, respectively. To test the clinical feasibility of our method, CT images were divided into two groups, and oncologists from our institution created manual contours with and without IOV map guidance. The Dice similarity coefficient and Jaccard index increased by ~6 and 7%, respectively, and the Hausdorff distance decreased by 2.5 mm, indicating a statistically significant IOV reduction (p < 0.05). Hence, IOV-net and its resultant IOV maps have the potential to improve radiation therapy efficacy worldwide.
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Niu T, Tsui T, Zhao W. AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery. Semin Radiat Oncol 2022; 32:365-376. [DOI: 10.1016/j.semradonc.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Generation and Evaluation of Synthetic Computed Tomography (CT) from Cone-Beam CT (CBCT) by Incorporating Feature-Driven Loss into Intensity-Based Loss Functions in Deep Convolutional Neural Network. Cancers (Basel) 2022; 14:cancers14184534. [PMID: 36139692 PMCID: PMC9497126 DOI: 10.3390/cancers14184534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Despite numerous benefits of cone-beam computed tomography (CBCT), its applications to radiotherapy were limited mainly due to degraded image quality. Recently, enhancing the CBCT image quality by generating synthetic CT image by deep convolutional neural network (CNN) has become frequent. Most of the previous works, however, generated synthetic CT with simple, classical intensity-driven loss in network training, while not specifying a full-package of verifications. This work trained the network by combining feature- and intensity-driven losses and attempted to demonstrate clinical relevance of the synthetic CT images by assessing both image similarity and dose calculating accuracy throughout a commercial Monte-Carlo. Abstract Deep convolutional neural network (CNN) helped enhance image quality of cone-beam computed tomography (CBCT) by generating synthetic CT. Most of the previous works, however, trained network by intensity-based loss functions, possibly undermining to promote image feature similarity. The verifications were not sufficient to demonstrate clinical applicability, either. This work investigated the effect of variable loss functions combining feature- and intensity-driven losses in synthetic CT generation, followed by strengthening the verification of generated images in both image similarity and dosimetry accuracy. The proposed strategy highlighted the feature-driven quantification in (1) training the network by perceptual loss, besides L1 and structural similarity (SSIM) losses regarding anatomical similarity, and (2) evaluating image similarity by feature mapping ratio (FMR), besides conventional metrics. In addition, the synthetic CT images were assessed in terms of dose calculating accuracy by a commercial Monte-Carlo algorithm. The network was trained with 50 paired CBCT-CT scans acquired at the same CT simulator and treatment unit to constrain environmental factors any other than loss functions. For 10 independent cases, incorporating perceptual loss into L1 and SSIM losses outperformed the other combinations, which enhanced FMR of image similarity by 10%, and the dose calculating accuracy by 1–2% of gamma passing rate in 1%/1mm criterion.
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Chen X, Liu Y, Yang B, Zhu J, Yuan S, Xie X, Liu Y, Dai J, Men K. A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy. Front Oncol 2022; 12:988800. [PMID: 36091131 PMCID: PMC9454309 DOI: 10.3389/fonc.2022.988800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.Materials and methodsIn this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.ResultsTransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.ConclusionsThe proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- School of Physics and Technology, Wuhan University, Wuhan, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuejie Xie
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yueping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Kuo Men,
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Zhang X, Zhang W, Sun W, Song A. A new soft tissue deformation model based on Runge-Kutta: Application in lung. Comput Biol Med 2022; 148:105811. [PMID: 35834968 DOI: 10.1016/j.compbiomed.2022.105811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/25/2022] [Accepted: 07/03/2022] [Indexed: 11/30/2022]
Abstract
Flexible body deformation model is the most critical research in the field of telemedicine, which decides whether the deformation process of tissues and organs can be simulated in real time and realistically. Basically, the improvement of model accuracy often means the loss of real-time performance. In order to effectively balance between accuracy and real-time performance, this paper proposes a new model, which uses the step-variable fourth-order Runge-Kutta method for the first time to solve the relationship problem between the external force and displacement of the nodes in the finite element deformation of the lung. To improve the real-time performance of the model, a one-stage solution optimization algorithm is proposed to optimize the step size selection problem. Finally, an accelerated two-level node update algorithm is proposed to further improve the real-time performance. A lung surgery simulation platform with 3DMax2020 and OpenGL4.5 is built to verify the accuracy, efficiency, realism and applicability of the model. Experimental results show that the proposed lung model can achieve real-world visual reproduction during remote surgery, and exceeds other 13 reference models in real-time performance, with natural deformation effect, high simulation accuracy, which is suitable for modeling normal lung and four types of lungs suffering from diseases.
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Affiliation(s)
- Xiaorui Zhang
- Wuxi Research Institute, Nanjing University of Information Science & Technology, Wuxi, 214100, China; Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Wenzheng Zhang
- Engineering Research Center of Digital Forensics, Ministry of Education, Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
| | - Wei Sun
- School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Aiguo Song
- State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China
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Rusanov B, Hassan GM, Reynolds M, Sabet M, Kendrick J, Farzad PR, Ebert M. Deep learning methods for enhancing cone-beam CT image quality towards adaptive radiation therapy: A systematic review. Med Phys 2022; 49:6019-6054. [PMID: 35789489 PMCID: PMC9543319 DOI: 10.1002/mp.15840] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 05/21/2022] [Accepted: 06/16/2022] [Indexed: 11/11/2022] Open
Abstract
The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings - with emphasis on study design and deep learning techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarised, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state of the art methods utilized in radiation oncology. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Branimir Rusanov
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mark Reynolds
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia
| | - Mahsheed Sabet
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Jake Kendrick
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Pejman Rowshan Farzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, 6009, Australia.,Department of Radiation Oncology, Sir Chairles Gairdner Hospital, Perth, Western Australia, 6009, Australia
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Jiang Y, Zhang Y, Luo C, Yang P, Wang J, Liang X, Zhao W, Li R, Niu T. A generalized image quality improvement strategy of cone-beam CT using multiple spectral CT labels in Pix2pix GAN. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6bda] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 04/29/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. The quantitative and routine imaging capabilities of cone-beam CT (CBCT) are hindered from clinical applications due to the severe shading artifacts of scatter contamination. The scatter correction methods proposed in the literature only consider the anatomy of the scanned objects while disregarding the impact of incident x-ray energy spectra. The multiple-spectral model is in urgent need for CBCT scatter estimation. Approach. In this work, we incorporate the multiple spectral diagnostic multidetector CT labels into the pixel-to-pixel (Pix2pix) GAN to estimate accurate scatter distributions from CBCT projections acquired at various imaging volume sizes and x-ray energy spectra. The Pix2pix GAN combines the residual network as the generator and the PatchGAN as the discriminator to construct the correspondence between the scatter-contaminated projection and scatter distribution. The network architectures and loss function of Pix2pix GAN are optimized to achieve the best performance on projection-to-scatter transition. Results. The CBCT data of a head phantom and abdominal patients are applied to test the performance of the proposed method. The error of the corrected CBCT image using the proposed method is reduced from over 200 HU to be around 20 HU in both phantom and patient studies. The mean structural similarity index of the CT image is improved from 0.2 to around 0.9 after scatter correction using the proposed method compared with the MC-simulation method, which indicates a high similarity of the anatomy in the images before and after the proposed correction. The proposed method achieves higher accuracy of scatter estimation than using the Pix2pix GAN with the U-net generator. Significance. The proposed scheme is an effective solution to the multiple spectral CBCT scatter correction. The scatter-correction software using the proposed model will be available at: https://github.com/YangkangJiang/Cone-beam-CT-scatter-correction-tool.
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Liu Y, Chen X, Zhu J, Yang B, Wei R, Xiong R, Quan H, Liu Y, Dai J, Men K. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies. Phys Med Biol 2022; 67. [PMID: 35354124 DOI: 10.1088/1361-6560/ac6289] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022]
Abstract
Objective.In this study, we aimed to develop deep learning framework to improve cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy (ART) applications.Approach.Paired CBCT and planning CT images of 2 pelvic phantoms and 91 patients (15 patients for testing) diagnosed with prostate cancer were included in this study. First, well-matched images of rigid phantoms were used to train a U-net, which is the supervised learning strategy to reduce serious artifacts. Second, the phantom-trained U-net generated intermediate CT images from the patient CBCT images. Finally, a cycle-consistent generative adversarial network (CycleGAN) was trained with intermediate CT images and deformed planning CT images, which is the unsupervised learning strategy to learn the style of the patient images for further improvement. When testing or applying the trained model on patient CBCT images, the intermediate CT images were generated from the original CBCT image by U-net, and then the synthetic CT images were generated by the generator of CycleGAN with intermediate CT images as input. The performance was compared with conventional methods (U-net/CycleGAN alone trained with patient images) on the test set.Results.The proposed two-step method effectively improved the CBCT image quality to the level of CT scans. It outperformed conventional methods for region-of-interest contouring and HU calibration, which are important to ART applications. Compared with the U-net alone, it maintained the structure of CBCT. Compared with CycleGAN alone, our method improved the accuracy of CT number and effectively reduced the artifacts, making it more helpful for identifying the clinical target volume.Significance.This novel two-step method improves CBCT image quality by combining phantom-based supervised and patient-based unsupervised learning strategies. It has immense potential to be integrated into the ART workflow to improve radiotherapy accuracy.
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Affiliation(s)
- Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China.,School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Rui Xiong
- School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Hong Quan
- School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Yueping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
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Zhang Y, Ding SG, Gong XC, Yuan XX, Lin JF, Chen Q, Li JG. Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients. Technol Cancer Res Treat 2022; 21:15330338221085358. [PMID: 35262422 PMCID: PMC8918752 DOI: 10.1177/15330338221085358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Purpose: To overcome the imaging artifacts and Hounsfield unit inaccuracy limitations of cone-beam computed tomography, a conditional generative adversarial network is proposed to synthesize high-quality computed tomography-like images from cone-beam computed tomography images. Methods: A total of 120 paired cone-beam computed tomography and computed tomography scans of patients with head and neck cancer who were treated during January 2019 and December 2020 retrospectively collected; the scans of 90 patients were assembled into training and validation datasets, and the scans of 30 patients were used in testing datasets. The proposed method integrates a U-Net backbone architecture with residual blocks into a conditional generative adversarial network framework to learn a mapping from cone-beam computed tomography images to pair planning computed tomography images. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess the performance of this method compared with U-Net and CycleGAN. Results: The synthesized computed tomography images produced by the conditional generative adversarial network were visually similar to planning computed tomography images. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio calculated from test images generated by conditional generative adversarial network were all significantly different than CycleGAN and U-Net. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio values between the synthesized computed tomography and the reference computed tomography were 16.75 ± 11.07 Hounsfield unit, 58.15 ± 28.64 Hounsfield unit, 0.92 ± 0.04, and 30.58 ± 3.86 dB in conditional generative adversarial network, 20.66 ± 12.15 Hounsfield unit, 66.53 ± 29.73 Hounsfield unit, 0.90 ± 0.05, and 29.29 ± 3.49 dB in CycleGAN, and 16.82 ± 10.99 Hounsfield unit, 58.68 ± 28.34 Hounsfield unit, 0.92 ± 0.04, and 30.48 ± 3.83 dB in U-Net, respectively. Conclusions: The synthesized computed tomography generated from the cone-beam computed tomography-based conditional generative adversarial network method has accurate computed tomography numbers while keeping the same anatomical structure as cone-beam computed tomography. It can be used effectively for quantitative applications in radiotherapy.
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Affiliation(s)
- Yun Zhang
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Sheng-gou Ding
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Xiao-chang Gong
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Xing-xing Yuan
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Jia-fan Lin
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Qi Chen
- MedMind Technology Co. Ltd, Beijing, People’s Republic of China
| | - Jin-gao Li
- Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
- Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma Nanchang, Jiangxi, People’s Republic of China
- Medical College of Nanchang University, Nanchang, Jiangxi, People’s Republic of China
- Jin-gao Li, Department of Radiation Oncology, Jiangxi Cancer Hospital of Nanchang University Nanchang, Jiangxi 330029, People’s Republic of China.
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Yang B, Chang Y, Liang Y, Wang Z, Pei X, Xu X, Qiu J. A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality. Front Oncol 2022; 12:896795. [PMID: 35707352 PMCID: PMC9189355 DOI: 10.3389/fonc.2022.896795] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/27/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose The aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN). Methods A total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation. Results The DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05). Conclusion The registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.
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Affiliation(s)
- Bo Yang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Yankui Chang
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Yongguang Liang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Zhiqun Wang
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Xi Pei
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
- Technology Development Department, Anhui Wisdom Technology Co., Ltd., Hefei, China
| | - Xie George Xu
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
- Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Jie Qiu
- Department of Radiation Oncology, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
- *Correspondence: Jie Qiu,
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A Comparative Study on the Voxel Values in Alveolar Bones Acquired by MDCT and Newly Developed Dental Dual-Energy CBCT. SENSORS 2021; 21:s21227552. [PMID: 34833629 PMCID: PMC8619558 DOI: 10.3390/s21227552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 01/23/2023]
Abstract
The purpose of this study was to analyze the effectiveness of newly developed dental dual-energy (DE) cone-beam computed tomography (CBCT) to compare both the voxel values in hard bone tissue of DE-CBCT and multidetector computed tomography (MDCT) images, collected in a clinical trial conducted at Seoul National University Dental Hospital. A software implemented as a scripted module of a three-dimensional (3D) slicer was developed to register the volume data from the MDCT space to DE-CBCT, locate the same 3D regions of interest (ROIs) in each image space, and extract the statistics of the ROIs. The mean values were paired and used as representative values of the ROIs. A scatter plot with the line of equality and Bland–Altman (BA) plot of difference for a pair of measured means were used for statistical analysis. Of the ROI pairs, 96% were within ±15% from the identity line, and more than 95% of the measured ROI pairs were within the limits of agreement of the 95% confidence intervals (CIs), with the CI of the limits in BA plots. The newly developed dental DE-CBCT showed a level of voxel value accuracy similar to that of MDCT.
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Chen X, Yang B, Li J, Zhu J, Ma X, Chen D, Hu Z, Men K, Dai J. A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma. Phys Med Biol 2021; 66. [PMID: 34700300 DOI: 10.1088/1361-6560/ac3345] [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: 08/11/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Objective:Megavoltage computed tomography (MV-CT) is used for setup verification and adaptive radiotherapy in tomotherapy. However, its low contrast and high noise lead to poor image quality. This study aimed to develop a deep-learning-based method to generate synthetic kilovoltage CT (skV-CT) and then evaluate its ability to improve image quality and tumor segmentation.Approach:The planning kV-CT and MV-CT images of 270 patients with nasopharyngeal carcinoma (NPC) treated on an Accuray TomoHD system were used. An improved cycle-consistent adversarial network which used residual blocks as its generator was adopted to learn the mapping between MV-CT and kV-CT and then generate skV-CT from MV-CT. A Catphan 700 phantom and 30 patients with NPC were used to evaluate image quality. The quantitative indices included contrast-to-noise ratio (CNR), uniformity and signal-to-noise ratio (SNR) for the phantom and the structural similarity index measure (SSIM), mean absolute error (MAE), and peak signal-to-noise ratio (PSNR) for patients. Next, we trained three models for segmentation of the clinical target volume (CTV): MV-CT, skV-CT, and MV-CT combined with skV-CT. The segmentation accuracy was compared with indices of the dice similarity coefficient (DSC) and mean distance agreement (MDA).Mainresults:Compared with MV-CT, skV-CT showed significant improvement in CNR (184.0%), image uniformity (34.7%), and SNR (199.0%) in the phantom study and improved SSIM (1.7%), MAE (24.7%), and PSNR (7.5%) in the patient study. For CTV segmentation with only MV-CT, only skV-CT, and MV-CT combined with skV-CT, the DSCs were 0.75 ± 0.04, 0.78 ± 0.04, and 0.79 ± 0.03, respectively, and the MDAs (in mm) were 3.69 ± 0.81, 3.14 ± 0.80, and 2.90 ± 0.62, respectively.Significance:The proposed method improved the image quality of MV-CT and thus tumor segmentation in helical tomotherapy. The method potentially can benefit adaptive radiotherapy.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jingwen Li
- Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology, People's Republic of China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Zhihui Hu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
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