Double paths network with residual information distillation for improving lung CT image super resolution

Abstract

ObjectiveMedical image analysis is particularly important for doctors to differential diagnosis of diseases. Due to the outbreak of COVID-19, how to diagnose COVID-19 accurately has become a key issue. High-resolution lung CT images can provide more diagnostic information, so there is an urgent need to develop a super-resolution method to improve the resolution of medical images. MethodsIn this paper, a method based on double paths with residual information distillation for medical images super resolution (DRIDSR) is established. In the low-frequency path, shallow convolutional network is used to get low-frequency features, while in the high-frequency path, a residual information distillation module (RIDM) is designed to obtain clearer high-frequency features. RIDM cascades multiple residual blocks, and uses the output of each residual block as the input of IDB for further information distillation. Finally, it merges the information left by multiple IDBs as output. ResultsThe proposed method is tested on the public dataset COVID-CT. The DRIDSR reconstruction quality of the algorithm is higher than that of the SRCNN, ESPCN, VDSR, IMDN and PAN method (+2.21 dB, +2.41 dB, +1.42 dB, +0.43 dB, +0.54 dB improvement, respectively) at × 3 upscale factor and (+2.35 dB, +2.17 dB, +1.59 dB, +0.48 dB, +0.56 dB increase, respectively) at ×4 upscale factor. While the number of parameters and analysis time of our model are reduced. ConclusionsIt is demonstrated that DRIDSR network can obtain better performance and better HR medical images than several state-of-the-art SR methods in terms of objective indicators and subjective evaluation.