Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy

Song, Liming1,2,3; Li, Yafen3; Dong, Guoya1,2; Lambo, Ricardo3; Qin, Wenjian3; Wang, Yuenan4; Zhang, Guangwei5,6; Liu, Jing7; Xie, Yaoqin3

2021-05-01

10.21037/qims-20-1239

Quantitative Imaging in Medicine and Surgery   影响因子和分区

2223-4292

2223-4306

["Background: In the radiotherapy of nasopharyngeal carcinoma (NPC), magnetic resonance imaging (MRI) is widely used to delineate tumor area more accurately. While MRI offers the higher soft tissue contrast, patient positioning and couch correction based on bony image fusion of computed tomography (CT) is also necessary. There is thus an urgent need to obtain a high image contrast between bone and soft tissue to facilitate target delineation and patient positioning for NPC radiotherapy. In this paper, our aim is to develop a novel image conversion between the CT and MRI modalities to obtain clear bone and soft tissue images simultaneously, here called bone-enhanced MRI (BeMRI).","Methods: Thirty-five patients were retrospectively selected for this study. All patients underwent clinical CT simulation and 1.5T MRI within the same week in Shenzhen Second People's Hospital. To synthesize BeMRI, two deep learning networks, U-Net and CycleGAN, were constructed to transform MRI to synthetic CT (sCT) images. Each network used 28 patients' images as the training set, while the remaining 7 patients were used as the test set (similar to 1/5 of all datasets). The bone structure from the sCT was then extracted by the threshold-based method and embedded in the corresponding part of the MRI image to generate the BeMRI image. To evaluate the performance of these networks, the following metrics were applied: mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).","Results: In our experiments, both deep learning models achieved good performance and were able to effectively extract bone structure from MRI. Specifically, the supervised U-Net model achieved the best results with the lowest overall average MAE of 125.55 (P<0.05) and produced the highest SSIM of 0.89 and PSNR of 23.84. These results indicate that BeMRI can display bone structure in higher contrast than conventional MRI.","Conclusions: A new image modality BeMRI, which is a composite image of CT and MRI, was proposed. With high image contrast of both bone structure and soft tissues, BeMRI will facilitate tumor localization and patient positioning and eliminate the need to frequently check between separate MRI and CT images during NPC radiotherapy."]

Computed tomography (CT) deep learning image synthesis magnetic resonance imaging (MRI) nasopharyngeal carcinoma (NPC) radiation therapy planning (RTP)

SCI

英语

其他

National Key Research and Develop Program of China[2016YFC0105102] ; Leading Talent of Special Support Project in Guangdong[2016TX03R139] ; Shenzhen matching project[GJHS20170314155751703] ; Science Foundation of Guangdong["2020B1111140001","2017B020229002","2015B020233011"] ; National Natural Science Foundation of China["U20A20373",61871374,61901463] ; Sanming Project of Shenzhen[SZSM201612041] ; Shenzhen Science and Technology Program of China[JCYJ20200109115420720]

Radiology, Nuclear Medicine & Medical Imaging

Radiology, Nuclear Medicine & Medical Imaging

WOS:000677692000001

AME PUBL CO

Web of Science

1.Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, 8 Guangrongdao, Tianjin, Peoples R China
2.Hebei Univ Technol, Hebei Key Lab Bioelectromagnet & Neural Engn, Tianjin, Peoples R China
3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Inst Biomed & Hlth Engn, 1068 Xueyuan Ave, Shenzhen, Peoples R China
4.Peking Univ, Shenzhen Hosp, Dept Radiat Oncol, 1120 Lianhua Rd, Shenzhen, Peoples R China
5.Jinan Univ, Med Coll, Clin 2, Shenzhen Peoples Hosp, Shenzhen, Peoples R China
6.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen, Peoples R China
7.Shenzhen Univ, Gen Hosp, Shenzhen, Peoples R China

Song, Liming,Li, Yafen,Dong, Guoya,et al. Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy[J]. Quantitative Imaging in Medicine and Surgery,2021.

Song, Liming.,Li, Yafen.,Dong, Guoya.,Lambo, Ricardo.,Qin, Wenjian.,...&Xie, Yaoqin.(2021).Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy.Quantitative Imaging in Medicine and Surgery.

Song, Liming,et al."Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy".Quantitative Imaging in Medicine and Surgery (2021).