High-definition fusion of multi-modal images of brain gliomas based on AKS-SRNet.

Abstract

Emission computed tomography provides information on the growth and metabolic status of brain gliomas, but its use of radiopharmaceuticals brings radiation risks and financial costs to patients. These problems will be improved by reducing the dose of radiopharmaceuticals, but it will reduce the quality of brain emission computed tomography (ECT) images, which will lead to low resolution, unclear lesion areas, and blurred boundaries of parts of the lesion areas in the magnetic resonance imaging-emission computed tomography (MRI-ECT) fusion image of gliomas. Therefore, a high-definition fusion of multi-modal images of brain glioma based on a super-resolution reconstruction network based on the adaptive kernel selection (AKS-SRNet) is proposed. First, to solve the problem of loss of detailed structural information in the disease region of fusion images, a correlation-driven feature decomposition fusion network based on multi-scale and fine-grained feature extraction was constructed. The multi-scale feature extraction module and the outlook attention module are introduced into the fusion network to extract multi-scale features and retain more detailed features. Second, to solve the problems of low resolution and unclear lesion areas of MRI and ECT fusion images, a super-resolution reconstruction network based on adaptive kernel selection was constructed. A selective kernel attention module is added to the generator of PAUP-ESRGAN to improve the efficiency of the super-resolution reconstruction network generator. Finally, the experimental results show that the fusion image generated by the proposed high-definition fusion network has a less burr-prone and a clearer contour of the lesion area, which is helpful for doctors in analyzing and diagnosing the glioma accurately.