Speaker
Description
X-ray Fluorescence Ghost Imaging (XRF-GI) was recently demonstrated for x-ray lab sources. It has the potential to reduce acquisition time and deposited dose by choosing their trade-off with spatial resolution, while alleviating the focusing constraints of the probing beam. In this talk, we present the realization of synchrotron-based XRF-GI: We present both an adapted experimental setup and its corresponding required computational technique to process the data.
In particular, we present a new self-supervised deep-learning-based GI reconstruction method (called Noise2Ghost), which provides unparalleled reconstruction performance for noisy acquisitions among unsupervised methods. Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios.
In conclusion, the highlights of our work are:
• Extension of the above-mentioned potential advantages of GI to synchrotron XRF imaging.
• A new strategy to improve resilience against drifts at all scales, and the study of previously inaccessible samples, such as liquids.
• A potential new avenue for the development of micro- and nano-scale x-ray emission imaging with dose-sensitive samples.
Notable applications that could benefit from our work include in-vivo and in-operando case studies for biological samples and batteries.
