Neuromorphic X-ray imaging

Time-resolved X-ray imaging has proven to be a crucial tool in advancing the study of dynamic processes within both biology and materials science. Synchrotron-based X-ray micro-computed tomography is usually considered the reference technique for high-speed studies investigating fundamental properties and functions of materials and organisms. High-performance detectors and powerful data streaming backends now achieve acquisition rates on the order of a few kilohertz (kHz) [1], [2], but the increased use of imaging technologies is becoming increasingly challenging, as high-speed dynamical imaging experiments either produce exorbitant amounts of data or remain limited in temporal resolution.

In this project, we are leveraging recent advances in neuromorphic imaging sensors and computing to develop an X-ray-based neuromorphic vision system. These sensors detect per-pixel brightness changes asynchronously and generate a stream of events encoding the triggering time, location, and polarity of each pixel [3] and hence are promising candidates to push current limits in temporal imaging resolution and data production rates. Together with our project partners, we will address and answer scientific questions from two independent fields that will benefit from a significant increase in temporal resolution: (a) the study of nano-architected metamaterials, and (b) the study of pulmonary functional biology.

The project entitled “Neuromorphic X-ray tomography for advanced biology and materials science applications (NeuroTom-X)” is conducted in collaboration with the Swiss Data Science Center (SDSC Hub at PSI) and is financed by the Swiss National Fund (SNSF). 

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References

  1. F. García-Moreno et al., “Tomoscopy: Time-Resolved Tomography for Dynamic Processes in Materials,” Advanced Materials, vol. 33, no. 45, p. 2104659, 2021, doi: 10.1002/adma.202104659.
  2. R. Mokso et al., “GigaFRoST: the gigabit fast readout system for tomography,” J Synchrotron Rad, vol. 24, no. 6, pp. 1250–1259, Nov. 2017, doi: 10.1107/S1600577517013522.
  3. G. Gallego et al., “Event-based Vision: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 1, pp. 154–180, Jan. 2022, doi: 10.1109/TPAMI.2020.3008413.

Collaboration

  • Dr. Benjamín Béjar Haro , SDSC, Paul Scherrer Institut, Switzerland
  • Dr. Matias Kagias, Lund University, Sweden

Funding

  • Swiss National Foundation (SNSF) – grant No. 200021_219704

Contact