A multi-scale pyramid residual weight network for medical image fusion

Motivation: At present, the multi-modal fusion image has the problems of weak functional information performance and much noise. Goal(s): Based on the existing technology, this research increases the retention of functional information and improves the display quality of the fused image. Approach: In this study, a multi-scale pyramid convolutional neural network model based on residual structure is constructed, which can extract deeper semantic information while retaining shallow context information. Results: By constructing a new convolutional neural network, the loss of functional information in the fused image is reduced, the noise of the fused image is reduced, and the image quality is improved. Impact: The multimodal image fusion technology proposed in this paper preserves the texture information of MRI and CT, and the functional information of PET/SPECT at the same time, which makes more dimensions available for clinical diagnosis in the future.