Three-dimensional-generator U-net for dual-resonant scanning multiphoton microscopy image inpainting and denoising.

A dual-resonant scanning multiphoton (DRSM) microscope incorporating a tunable acoustic gradient index of refraction lens and a resonant mirror is developed for rapid volumetric bioimaging. It is shown that the microscope achieves a volumetric imaging rate up to 31.25 volumes per second (vps) for a scanning volume of up to 200 × 200 × 100 µm with 256 × 256 × 128 voxels. However, the volumetric images have a severe negative signal-to-noise ratio (SNR) as a result of a large number of missing voxels for a large scanning volume and the presence of Lissajous patterning residuals. Thus, a modified three-dimensional (3D)-generator U-Net model trained using simulated microbead images is proposed and used to inpaint and denoise the images. The performance of the 3D U-Net model for bioimaging applications is enhanced by training the model with high-SNR drosophila brain images captured using a conventional point scanning multiphoton microscope. The trained model shows the ability to produce clear drosophila brain images at a rate of 31.25 vps with a SNR improvement of approximately 20 dB over the original images obtained by the DRSM microscope. The training convergence time of the modified U-Net model is just half that of a general 3D U-Net model. The model thus has significant potential for 3D bioimaging transfer learning. Through the assistance of transfer learning, the model can be extended to the restoration of drosophila brain images with a high image quality and a rapid training time.