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Time-resolved tomography algorithm using one projection per time step: Non-monotonic case.

Maxim Grigoriev1,2, Alexey Buzmakov2,3

  • 1Institute of Microelectronics Technology and High-Purity Materials, Russian Academy of Sciences, Chernogolovka, 142432 Russia.

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Summary
This summary is machine-generated.

This study introduces a new 4D tomography algorithm for reconstructing dynamic processes using single projections per time step. The iterative method, processing data in projection space, offers improved speed and reconstruction quality for non-monotonic changes.

Keywords:
4D CTiterative reconstructiontime-resolved tomographytomography algorithm

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Area of Science:

  • Imaging science
  • Computational imaging
  • Dynamic process reconstruction

Background:

  • Dynamic processes in 3D objects (4D tomography) are challenging to reconstruct.
  • Existing algorithms for monotonic or non-monotonic processes often require extensive data or complex processing.
  • Reconstruction using limited projections, especially one per time step, is highly desirable.

Purpose of the Study:

  • To present a novel iterative algorithm for 4D tomography.
  • To enable reconstruction of dynamic non-monotonic processes using only one projection per time step.
  • To leverage a priori knowledge of the object's initial structure.

Main Methods:

  • An iterative algorithm processing intermediate data in the projection data space.
  • Utilizes a priori knowledge of the static initial structure of the 3D object.
  • Comparison with previous algorithms processing data in the reconstructed object space.

Main Results:

  • The new algorithm demonstrates superior speed and reconstruction quality compared to existing methods.
  • Virtual experiments successfully reconstructed dynamic voxel value changes over time.
  • Investigated the impact of internal structure in dynamic regions on reconstruction fidelity.

Conclusions:

  • The proposed 4D tomography algorithm enables dynamic process reconstruction in 3D volumes with minimal projection data (one per time step).
  • This method shows potential applicability in materials science, geology, and medicine.
  • Further research is needed for adaptation to complex experimental data.