Convolutional neural network based super resolution for brain cell mapping

Synchrotron based full-field nano-CT (computed tomography) holds the key to map the cellular composition of the entire brain cells. In particular, our in-house Transmission X-ray Microscope (TXM) at the Advanced Photon Source (sector 32-ID) in the Argonne National Laboratory is designed to achieve a spatial resolution of 20 nm. But our ability to resolve features of nervous system in few nanometers - required to properly discern the connectivity in the whole brain - is constrained by the laws of optics of the X-ray microscope (namely depth of focus). Accordingly, in this contribution, we propose the use of deep convolutional neural network (CNN) based super-resolution to achieve a resolution beyond the limit of the TXM. The mapping from low resolution to high resolution will be deduced accounting for degradations such as blur kernel and noise level to faithfully model the artifacts observed in TXM based CT results. Finally, the L2 based loss function will be combined with regularization to preserve the edges of minute structure seen in brain cells.