High-resolution X-ray scanning with a diffuse Huffman-patterned probe to reduce radiation damage

Scanning objects with a tightly focused beam (of photons or electrons for example) can provide high-resolution images. However, rapid deposition of energy into a small area can damage tissues in organic samples or may rearrange the chemical structure or physical properties of inorganic materials. Scanning an object with a broad, or diffuse, beam can deliver an equivalent probe energy but spread it over a much wider footprint. However, typically the imaging resolution is proportional to the probe diameter and a diffuse probe sacrifices resolution. Here we propose a method to achieve `high resolution' imaging (in the sense that resolution is smaller than the probe diameter) using a diffuse probe. We achieve this by encoding a pattern onto the probe and employing a decoding step to recover a tight delta-like impulse response. Huffman sequences, by design, have the optimal delta-like autocorrelation for aperiodic (non-cyclic) convolution and are well conditioned. Here we adapt 1D Huffman sequences to design 2D Huffman-like discrete arrays as diffuse imaging probes that have spatially broad, relatively thin, uniform intensity profiles and have excellent aperiodic autocorrelation metrics. Examples of broad shaped diffuse beams were developed for the case of X-ray imaging. A variety of masks were fabricated by the deposition of finely structured layers of tantalum on a silicon oxide wafer. The layers form a pattern of discrete pixels that modify the shape of an incident uniform beam of low-energy X-rays as it passes through the mask. The intensity profiles of the X-ray beams after transmission through these masks were validated, first by acquiring direct-detector X-ray images of the masks, and second by raster scanning a pinhole over each mask pattern, pixel-by-pixel, collecting `bucket' signals as applied in traditional ghost imaging. The masks were then used to raster scan the shaped X-ray beam over several simple binary and `gray' test objects, again producing bucket signals, from which sharp reconstructed object images were obtained by deconvolving their bucket images.