Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning-based image super-resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U-net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (K(trans)), rate constant (K(ep)), volume fraction (V(e)), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U-net network model. Meanwhile, the difference was statistically significant (P < 0.05). K(trans), K(ep), and V(e) in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). The diagnostic sensitivity of DCE-MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE-MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE-MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE-MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.