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

Liming Song; Yafen Li; Guoya Dong; Ricardo Lambo; Wenjian Qin; Yuenan Wang; Guangwei Zhang; Jing Liu; Yaoqin Xie

doi:10.21037/qims-20-1239

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

Quant Imaging Med Surg. 2021 Dec;11(12):4709-4720. doi: 10.21037/qims-20-1239.

Authors

Liming Song^{1

2

3}, Yafen Li³, Guoya Dong^{1

2}, Ricardo Lambo³, Wenjian Qin³, Yuenan Wang⁴, Guangwei Zhang⁵, Jing Liu⁶, Yaoqin Xie³

Affiliations

¹ State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.
² Hebei Key Laboratory of Bioelectromagnetics and Neural Engineering, Hebei University of Technology, Tianjin, China.
³ Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
⁴ Department of Radiation Oncology, Peking University Shenzhen Hospital, Shenzhen, China.
⁵ Shenzhen People's Hospital (The Second Clinical Medical College of Jinan University; The first Affiliated Hospital of Southern University of Science and Technology), Shenzhen, China.
⁶ Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.

Abstract

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 (~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.

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