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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 H, Yang Z, Zhu J, Li J, Gong J, Chen L, Wang Z, Yin Y, Ren G, Cai J, Zhao L. Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108007. [PMID: 38241802 DOI: 10.1016/j.cmpb.2024.108007] [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: 09/18/2023] [Revised: 11/03/2023] [Accepted: 01/03/2024] [Indexed: 01/21/2024]
Abstract
Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains. As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.
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Affiliation(s)
- Hongfei Sun
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhi Yang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jiarui Zhu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jie Li
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jie Gong
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Liting Chen
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zhongfei Wang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yutian Yin
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, 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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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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He X, Chen Z, Gao Y, Wang W, You M. Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study. Dentomaxillofac Radiol 2023; 52:20230180. [PMID: 37664997 DOI: 10.1259/dmfr.20230180] [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: 09/05/2023] Open
Abstract
OBJECTIVES This study aims to determine the reproducibility and location-stability of cone-beam computed tomography (CBCT) radiomic features. METHODS Centrifugal tubes with six concentrations of K2HPO4 solutions (50, 100, 200, 400, 600, and 800 mg ml-1) were imaged within a customized phantom. For each concentration, images were captured twice as test and retest sets. Totally, 69 radiomic features were extracted by LIFEx. The reproducibility was assessed between the test and retest sets. We used the concordance correlation coefficient (CCC) to screen qualified features and then compared the differences in the numbers of them under 24 series (four locations groups * six concentrations). The location-stability was assessed using the Kruskal-Wallis test under different concentration sets; likewise, the numbers of qualified features under six test sets were analyzed. RESULTS There were 20 and 23 qualified features in the reproducibility and location-stability experiments, respectively. In the reproducibility experiment, the performance of the peripheral groups and high-concentration sets was significantly better than the center groups and low-concentration sets. The effect of concentration on the location-stability of features was not monotonic, and the number of qualified features in the low-concentration sets was greater than that in the high-concentration sets. No features were qualified in both experiments. CONCLUSIONS The density and location of the target object can affect the number of reproducible radiomic features, and its density can also affect the number of location-stable radiomic features. The problem of feature reliability should be treated cautiously in radiomic research on CBCT.
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Affiliation(s)
- Xian He
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, China
| | - Zhi Chen
- School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Yutao Gao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Wanjing Wang
- Faculty of Mathematics, Sichuan University, Chengdu, China
| | - Meng You
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Li Z, Zhang Q, Li H, Kong L, Wang H, Liang B, Chen M, Qin X, Yin Y, Li Z. Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators. BMC Cancer 2023; 23:828. [PMID: 37670252 PMCID: PMC10478281 DOI: 10.1186/s12885-023-11274-7] [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: 06/03/2023] [Accepted: 08/08/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND The goal was to investigate the feasibility of the registration generative adversarial network (RegGAN) model in image conversion for performing adaptive radiation therapy on the head and neck and its stability under different cone beam computed tomography (CBCT) models. METHODS A total of 100 CBCT and CT images of patients diagnosed with head and neck tumors were utilized for the training phase, whereas the testing phase involved 40 distinct patients obtained from four different linear accelerators. The RegGAN model was trained and tested to evaluate its performance. The generated synthetic CT (sCT) image quality was compared to that of planning CT (pCT) images by employing metrics such as the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). Moreover, the radiation therapy plan was uniformly applied to both the sCT and pCT images to analyze the planning target volume (PTV) dose statistics and calculate the dose difference rate, reinforcing the model's accuracy. RESULTS The generated sCT images had good image quality, and no significant differences were observed among the different CBCT modes. The conversion effect achieved for Synergy was the best, and the MAE decreased from 231.3 ± 55.48 to 45.63 ± 10.78; the PSNR increased from 19.40 ± 1.46 to 26.75 ± 1.32; the SSIM increased from 0.82 ± 0.02 to 0.85 ± 0.04. The quality improvement effect achieved for sCT image synthesis based on RegGAN was obvious, and no significant sCT synthesis differences were observed among different accelerators. CONCLUSION The sCT images generated by the RegGAN model had high image quality, and the RegGAN model exhibited a strong generalization ability across different accelerators, enabling its outputs to be used as reference images for performing adaptive radiation therapy on the head and neck.
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Affiliation(s)
- Zhenkai Li
- Chengdu University of Technology, Chengdu, China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | | | - Haodong Li
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Lingke Kong
- Manteia Technologies Co., Ltd., Xiamen, China
| | - Huadong Wang
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Benzhe Liang
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Mingming Chen
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiaohang Qin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Zhenjiang Li
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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Karius A, Szkitsak J, Strnad V, Fietkau R, Bert C. Cone-beam CT imaging with laterally enlarged field of view based on independently movable source and detector. Med Phys 2023; 50:5135-5149. [PMID: 37194354 DOI: 10.1002/mp.16463] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/03/2023] [Accepted: 05/01/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND CBCT imaging with field of views (FOVs) exceeding the size of scans acquired in the conventional imaging geometry, i.e. with opposing source and detector, is of high clinical importance for many medical fields. A novel approach for enlarged FOV scanning with one full-scan (EnFOV360) or two short-scans (EnFOV180) using an O-arm system arises from non-isocentric imaging based on independent source and detector rotations. PURPOSE The presentation, description, and experimental validation of this novel approach and the novel scanning techniques EnFOV360 and EnFOV180 for an O-arm system forms the scope of this work. METHODS We describe the EnFOV360, EnFOV180, and non-isocentric imaging techniques for the acquisition of laterally extended FOVs. For their experimental validation, scans of dedicated quality assurance as well as anthropomorphic phantoms were acquired, with the phantoms being placed both within the tomographic plane and at the longitudinal FOV border with and without lateral shifts from the gantry center. Based on this, geometric accuracy, contrast-noise-ratio (CNR) of different materials, spatial resolution, noise characteristics, as well as CT number profiles were quantitatively assessed. Results were compared to scans performed with the conventional imaging geometry. RESULTS With EnFOV360 and EnFOV180, we increased the in-plane size of acquired FOVs from 250 × 250 mm2 obtained for the conventional imaging geometry to up to 400 × 400 mm2 for the performed measurements. Geometric accuracy was very high for all scanning techniques with mean values ≤0.21 ± 0.11 mm. CNR and spatial resolution were comparable between isocentric and non-isocentric full-scans as well as EnFOV360, whereas substantial image quality deteriorations in this respect were observed for EnFOV180. Image noise in the isocenter was lowest for conventional full-scans with 13.4 ± 0.2 HU. For laterally shifted phantom positions, noise increased for conventional scans and EnFOV360, whereas noise reductions were observed for EnFOV180. Considering the anthropomorphic phantom scans, both EnFOV360 and EnFOV180 were comparable to conventional full-scans. CONCLUSION Both enlarged FOV techniques have high potential for imaging laterally extended FOVs. EnFOV360 revealed an image quality comparable to conventional full-scans in general. EnFOV180 showed an inferior performance particularly regarding CNR and spatial resolution.
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Affiliation(s)
- Andre Karius
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Juliane Szkitsak
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Vratislav Strnad
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
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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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Hirashima H, Nakamura M, Imanishi K, Nakao M, Mizowaki T. Evaluation of generalization ability for deep learning-based auto-segmentation accuracy in limited field of view CBCT of male pelvic region. J Appl Clin Med Phys 2023; 24:e13912. [PMID: 36659871 PMCID: PMC10161011 DOI: 10.1002/acm2.13912] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/21/2023] Open
Abstract
PURPOSE The aim of this study was to evaluate generalization ability of segmentation accuracy for limited FOV CBCT in the male pelvic region using a full-image CNN. Auto-segmentation accuracy was evaluated using various datasets with different intensity distributions and FOV sizes. METHODS A total of 171 CBCT datasets from patients with prostate cancer were enrolled. There were 151, 10, and 10 CBCT datasets acquired from Vero4DRT, TrueBeam STx, and Clinac-iX, respectively. The FOV for Vero4DRT, TrueBeam STx, and Clinac-iX was 20, 26, and 25 cm, respectively. The ROIs, including the bladder, prostate, rectum, and seminal vesicles, were manually delineated. The U2 -Net CNN network architecture was used to train the segmentation model. A total of 131 limited FOV CBCT datasets from Vero4DRT were used for training (104 datasets) and validation (27 datasets); thereafter the rest were for testing. The training routine was set to save the best weight values when the DSC in the validation set was maximized. Segmentation accuracy was qualitatively and quantitatively evaluated between the ground truth and predicted ROIs in the different testing datasets. RESULTS The mean scores ± standard deviation of visual evaluation for bladder, prostate, rectum, and seminal vesicle in all treatment machines were 1.0 ± 0.7, 1.5 ± 0.6, 1.4 ± 0.6, and 2.1 ± 0.8 points, respectively. The median DSC values for all imaging devices were ≥0.94 for the bladder, 0.84-0.87 for the prostate and rectum, and 0.48-0.69 for the seminal vesicles. Although the DSC values for the bladder and seminal vesicles were significantly different among the three imaging devices, the DSC value of the bladder changed by less than 1% point. The median MSD values for all imaging devices were ≤1.2 mm for the bladder and 1.4-2.2 mm for the prostate, rectum, and seminal vesicles. The MSD values for the seminal vesicles were significantly different between the three imaging devices. CONCLUSION The proposed method is effective for testing datasets with different intensity distributions and FOV from training datasets.
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Affiliation(s)
- Hideaki Hirashima
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan.,Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan
| | | | - Megumi Nakao
- Department of Advanced Medical Engineering and Intelligence, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan
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