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Alignment methods for nanotomography with deep subpixel accuracy.

Michal Odstrčil, Mirko Holler, Jörg Raabe

|December 25, 2019

Summary

This summary is machine-generated.

Accurate 3D reconstructions from nanoimaging require alignment, but current methods are slow. This study introduces an efficient toolkit for deep-subpixel alignment, reducing computational demands for improved X-ray tomography and laminography.

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Area of Science:

Materials Science
Imaging Physics
Computational Science

Background:

High-resolution X-ray tomography requires artifact-free 3D reconstructions.
Nanoimaging tools often lack long-term stability and accuracy, necessitating projection alignment.
Existing iterative alignment methods are computationally intensive, exceeding reconstruction time.

Purpose of the Study:

To develop a computationally efficient toolkit for accurate numerical alignment of projection data.
To address the limitations of current alignment methods in terms of computational cost and memory requirements.
To enable artifact-free 3D reconstructions in X-ray tomography and laminography.

Main Methods:

Implementation of deep-subpixel accuracy alignment algorithms.
Utilizing a multi-resolution scheme to optimize computational efficiency.
Development of a GPU-accelerated framework for rapid processing.

Main Results:

Demonstrated robust and accurate alignment of projection data.
Significantly reduced computational and memory requirements compared to traditional methods.
Successful application to both simulated and measured datasets for tomography and laminography.

Conclusions:

The developed alignment toolkit offers a computationally efficient solution for high-resolution 3D reconstructions.
The methods enable accurate artifact correction in X-ray tomography and laminography.
The publicly available GPU-accelerated implementation facilitates broader adoption and research.