To resolve the fuzzy problem caused by the lack of high-frequency information in the super-resolution reconstruction of medical images, this study proposes a medical-image super-resolution reconstruction method based on a residual channel attention network. The proposed method removes the batch normalization layer from the basic unit of the residual network (ResNet) to stabilize its training. Furthermore, it removes the scaling layer and adds a channel-attention block that focuses the ResNet on channels with abundant high-frequency details. The feature maps are subsampled using a sub-pixel convolution layer,obtaining the final high-resolution images. Experimental results show that the proposed method significantly improves objective evaluation indexes such as the peak signal-to-noise ratio and structural similarity index compared with mainstream image super-resolution methods. The obtained medical images are sufficiently detailed with high visual quality.