When performing single-photon emission computed tomography (SPECT) bone imaging examination,physicians often reduce radiation damage by reducing radiation dose,resulting in low signal-to-noise ratio and resolution of bone imaging,which seriously affects the diagnosis and automatic detection of lesions.In order to improve the quality of bone imaging,a two-stage SPECT bone imaging noise reduction method based on U-Net is proposed.Firstly,a U-Net noise estimation network is designed to quickly estimate the noise level of each bone image,providing noise prior knowledge for the backbone noise reduction network. Secondly,the backbone noise reduction network also uses U-Net as the basic framework,and combines multi-scale feature fusion and channel-spatial attention mechanism structure to enhance the noise feature extraction ability of the network and predict the noise map.Finally,denoised bone imaging is obtained through residual learning.At the same time,in order to solve the problem that the reconstructed image using the mean square error (MSE) loss function is too smooth,a composite loss function is designed to retain the original detailed information of bone imaging.In the experiments,different noise levels are applied to the bone images in the training set for data augmentation,and a transfer strategy is used to solve the problem of model overfitting.The results show that,compared with the current mainstream algorithms,the proposed noise reduction method can effectively reduce the noise of bone imaging and preserve the detailed features of the lesions. In addition,blind noise reduction can improve the imaging quality of the original bone imaging and improve the automatic segmentation effect of lesions.