An interpolation algorithm based on multi-direction Neural Networks (NN) is presented to solve the problems on lost data and fuzzy boundary in CT images caused by the unevenness exposure and noise. The information in every section and between different sections is integrated for the super-resolution reconstruction of focal zones. Firstly

a forecast net is extended to a 3D space

then optimal initial weights are designed according to the special gray feature distribution of pulmonary nodules. Finally

lost data are forecasted to improve the resolution. The results of simulation experiments indicate that this approach can improve performance in several respects such as location

real-time and PSNRs as compared with the present representative three methods

PCGLS

180° linear interpolation and one-way neural network. It is shown that the deviations of centre and centroid are averagely reduced by 27.1% and 23.0% respectively

and the target area and the iterations are averagely reduced by 21.5% and 25.9%

respectively.Moreover

the average PSNR is increased by 1.59 dB. The proposed method can be used in not only pulmonary CT images but also biological and remote sensing images.