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Fan M, Cao X, Lü F, Xie S, Yu Z, Chen Y, Lü Z, Li L. Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer. Phys Med Biol 2024; 69:095002. [PMID: 38537294 DOI: 10.1088/1361-6560/ad3889] [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: 11/27/2023] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer.Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n= 200), validation (n= 60), and testing sets (n= 62).Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively.Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.
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Affiliation(s)
- Ming Fan
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Xuan Cao
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Fuqing Lü
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Sangma Xie
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Zhou Yu
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Yuanlin Chen
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University,People's Republic of China
| | - Lihua Li
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University,Hangzhou 310018, Zhejiang, People's Republic of 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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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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Xie K, Gao L, Zhang H, Zhang S, Xi Q, Zhang F, Sun J, Lin T, Sui J, Ni X. GAN-based metal artifacts region inpainting in brain MRI imaging with reflective registration. Med Phys 2024; 51:2066-2080. [PMID: 37665773 DOI: 10.1002/mp.16724] [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: 12/19/2022] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Metallic magnetic resonance imaging (MRI) implants can introduce magnetic field distortions, resulting in image distortion, such as bulk shifts and signal-loss artifacts. Metal Artifacts Region Inpainting Network (MARINet), using the symmetry of brain MRI images, has been developed to generate normal MRI images in the image domain and improve image quality. METHODS T1-weighted MRI images containing or located near the teeth of 100 patients were collected. A total of 9000 slices were obtained after data augmentation. Then, MARINet based on U-Net with a dual-path encoder was employed to inpaint the artifacts in MRI images. The input of MARINet contains the original image and the flipped registered image, with partial convolution used concurrently. Subsequently, we compared PConv with partial convolution, and GConv with gated convolution, SDEdit using a diffusion model for inpainting the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. In addition, the artifact masks of clinical MRI images were inpainted by physicians. RESULTS MARINet could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the test results of PConv, GConv, SDEdit, and MARINet, the masked MAEs were 0.1938, 0.1904, 0.1876, and 0.1834, respectively, and the masked PSNRs were 17.39, 17.40, 17.49, and 17.60 dB, respectively. The visualization results also suggest that the network can recover the tissue texture, alveolar shape, and tooth contour. Additionally, for clinical artifact MRI images, MARINet completed the artifact region inpainting task more effectively when compared with other models. CONCLUSIONS By leveraging the quasi-symmetry of brain MRI images, MARINet can directly and effectively inpaint the metal artifacts in MRI images in the image domain, restoring the tooth contour and detail, thereby enhancing the image quality.
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Affiliation(s)
- Kai Xie
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Liugang Gao
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Heng Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou, China
- Changzhou Key Laboratory of Medical Physics, Changzhou, China
| | - Sai Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou, China
- Changzhou Key Laboratory of Medical Physics, Changzhou, China
| | - Qianyi Xi
- Center for Medical Physics, Nanjing Medical University, Changzhou, China
- Changzhou Key Laboratory of Medical Physics, Changzhou, China
| | - Fan Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou, China
- Changzhou Key Laboratory of Medical Physics, Changzhou, China
| | - Jiawei Sun
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Tao Lin
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Jianfeng Sui
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
| | - Xinye Ni
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, China
- Jiangsu Province Engineering Research Center of Medical Physics, Changzhou, China
- Center for Medical Physics, Nanjing Medical University, Changzhou, China
- Changzhou Key Laboratory of Medical Physics, Changzhou, China
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Yang X, Feng B, Yang H, Wang X, Luo H, Chen L, Jin F, Wang Y. CNN-based multi-modal radiomics analysis of pseudo-CT utilization in MRI-only brain stereotactic radiotherapy: a feasibility study. BMC Cancer 2024; 24:59. [PMID: 38200424 PMCID: PMC10782704 DOI: 10.1186/s12885-024-11844-3] [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: 07/21/2023] [Accepted: 01/04/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective. METHODS 34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation. RESULTS There was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5-99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation. CONCLUSIONS This is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis.
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Affiliation(s)
- Xin Yang
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China.
| | - Bin Feng
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China
| | - Han Yang
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China
| | - Xiaoqi Wang
- Apodibot Medical, Beijing, People's Republic of China
| | - Huanli Luo
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China
| | - Liyuan Chen
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China
| | - Fu Jin
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China.
| | - Ying Wang
- Departments of Radiation Oncology, Chongqing University Cancer Hospital, No. 181, Han Yu Road, Shapingba District, Chongqing, 400030, People's Republic of China.
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Achar S, Hwang D, Finkenstaedt T, Malis V, Bae WC. Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:8001. [PMID: 37766055 PMCID: PMC10538057 DOI: 10.3390/s23188001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/31/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Isthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine, found in as many as half of adolescent athletes with persistent low back pain. While computed tomography (CT) is the gold standard for the diagnosis of spondylolysis, the use of ionizing radiation near reproductive organs in young subjects is undesirable. While magnetic resonance imaging (MRI) is preferable, it has lowered sensitivity for detecting the condition. Recently, it has been shown that ultrashort echo time (UTE) MRI can provide markedly improved bone contrast compared to conventional MRI. To take UTE MRI further, we developed supervised deep learning tools to generate (1) CT-like images and (2) saliency maps of fracture probability from UTE MRI, using ex vivo preparation of cadaveric spines. We further compared quantitative metrics of the contrast-to-noise ratio (CNR), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) between UTE MRI (inverted to make the appearance similar to CT) and CT and between CT-like images and CT. Qualitative results demonstrated the feasibility of successfully generating CT-like images from UTE MRI to provide easier interpretability for bone fractures thanks to improved image contrast and CNR. Quantitatively, the mean CNR of bone against defect-filled tissue was 35, 97, and 146 for UTE MRI, CT-like, and CT images, respectively, being significantly higher for CT-like than UTE MRI images. For the image similarity metrics using the CT image as the reference, CT-like images provided a significantly lower mean MSE (0.038 vs. 0.0528), higher mean PSNR (28.6 vs. 16.5), and higher SSIM (0.73 vs. 0.68) compared to UTE MRI images. Additionally, the saliency maps enabled quick detection of the location with probable pars fracture by providing visual cues to the reader. This proof-of-concept study is limited to the data from ex vivo samples, and additional work in human subjects with spondylolysis would be necessary to refine the models for clinical use. Nonetheless, this study shows that the utilization of UTE MRI and deep learning tools could be highly useful for the evaluation of isthmic spondylolysis.
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Affiliation(s)
- Suraj Achar
- Department of Family Medicine, University of California-San Diego, La Jolla, CA 92093, USA
| | - Dosik Hwang
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Republic of Korea
| | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, 8091 Zurich, Switzerland
| | - Vadim Malis
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
| | - Won C. Bae
- Department of Radiology, University of California-San Diego, La Jolla, CA 92093, USA
- Department of Radiology, VA San Diego Healthcare System, San Diego, CA 92161, USA
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Gu X, Zhang Y, Zeng W, Zhong S, Wang H, Liang D, Li Z, Hu Z. Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 237:107571. [PMID: 37156020 DOI: 10.1016/j.cmpb.2023.107571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 04/19/2023] [Accepted: 04/25/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions. CT combined with MRI diagnosis has become a regular image-guided radiation treatment plan. METHODS In this paper, to reduce the dose of radiation exposure in CT examinations and ameliorate the limitations of traditional virtual imaging technologies, we propose a Generative MRI-to-CT transformation method with structural perceptual supervision. Even though structural reconstruction is structurally misaligned in the MRI-CT dataset registration, our proposed method can better align structural information of synthetic CT (sCT) images to input MRI images while simulating the modality of CT in the MRI-to-CT cross-modality transformation. RESULTS We retrieved a total of 3416 brain MRI-CT paired images as the train/test dataset, including 1366 train images of 10 patients and 2050 test images of 15 patients. Several methods (the baseline methods and the proposed method) were evaluated by the HU difference map, HU distribution, and various similarity metrics, including the mean absolute error (MAE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). In our quantitative experimental results, the proposed method achieves the lowest MAE mean of 0.147, highest PSNR mean of 19.27, and NCC mean of 0.431 in the overall CT test dataset. CONCLUSIONS In conclusion, both qualitative and quantitative results of synthetic CT validate that the proposed method can preserve higher similarity of structural information of the bone tissue of target CT than the baseline methods. Furthermore, the proposed method provides better HU intensity reconstruction for simulating the distribution of the CT modality. The experimental estimation indicates that the proposed method is worth further investigation.
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Affiliation(s)
- Xianfan Gu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yu Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Wen Zeng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Sihua Zhong
- Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China
| | - Haining Wang
- Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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Xie K, Gao L, Xi Q, Zhang H, Zhang S, Zhang F, Sun J, Lin T, Sui J, Ni X. New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107393. [PMID: 36739623 DOI: 10.1016/j.cmpb.2023.107393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/26/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVE A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer. METHODS The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN). RESULTS The three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet. CONCLUSIONS High-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.
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Affiliation(s)
- Kai Xie
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China
| | - Liugang Gao
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China
| | - Qianyi Xi
- Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China
| | - Heng Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China
| | - Sai Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China
| | - Fan Zhang
- Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China
| | - Jiawei Sun
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China
| | - Tao Lin
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China
| | - Jianfeng Sui
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China
| | - Xinye Ni
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou 213000, China; Jiangsu Province Engineering Research Center of Medical Physics, Changzhou 213000, China; Center for Medical Physics, Nanjing Medical University, Changzhou 213003, China; Changzhou Key Laboratory of Medical Physics, Changzhou 213000, China.
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Cao Z, Gao X, Chang Y, Liu G, Pei Y. A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN. Front Oncol 2022; 12:1024160. [PMID: 36439465 PMCID: PMC9686009 DOI: 10.3389/fonc.2022.1024160] [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: 08/21/2022] [Accepted: 10/27/2022] [Indexed: 08/15/2023] Open
Abstract
PURPOSE To develop a metal artifact reduction (MAR) algorithm and eliminate the adverse effects of metal artifacts on imaging diagnosis and radiotherapy dose calculations. METHODS Cycle-consistent adversarial network (CycleGAN) was used to generate synthetic CT (sCT) images from megavoltage cone beam CT (MVCBCT) images. In this study, there were 140 head cases with paired CT and MVCBCT images, from which 97 metal-free cases were used for training. Based on the trained model, metal-free sCT (sCT_MF) images and metal-containing sCT (sCT_M) images were generated from the MVCBCT images of 29 metal-free cases and 14 metal cases, respectively. Then, the sCT_MF and sCT_M images were quantitatively evaluated for imaging and dosimetry accuracy. RESULTS The structural similarity (SSIM) index of the sCT_MF and metal-free CT (CT_MF) images were 0.9484, and the peak signal-to-noise ratio (PSNR) was 31.4 dB. Compared with the CT images, the sCT_MF images had similar relative electron density (RED) and dose distribution, and their gamma pass rate (1 mm/1%) reached 97.99% ± 1.14%. The sCT_M images had high tissue resolution with no metal artifacts, and the RED distribution accuracy in the range of 1.003 to 1.056 was improved significantly. The RED and dose corrections were most significant for the planning target volume (PTV), mandible and oral cavity. The maximum correction of Dmean and D50 for the oral cavity reached 90 cGy. CONCLUSIONS Accurate sCT_M images were generated from MVCBCT images based on CycleGAN, which eliminated the metal artifacts in clinical images completely and corrected the RED and dose distributions accurately for clinical application.
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Affiliation(s)
- Zheng Cao
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
- Hematology and Oncology Department, Hefei First People’s Hospital, Hefei, China
| | - Xiang Gao
- Hematology and Oncology Department, Hefei First People’s Hospital, Hefei, China
| | - Yankui Chang
- School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, China
| | - Gongfa Liu
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
| | - Yuanji Pei
- National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei, China
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Shokraei Fard A, Reutens DC, Vegh V. From CNNs to GANs for cross-modality medical image estimation. Comput Biol Med 2022; 146:105556. [DOI: 10.1016/j.compbiomed.2022.105556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/03/2022] [Accepted: 04/22/2022] [Indexed: 11/03/2022]
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