Universal Drift Correction for Multidimensional Scanning Microscopy

📅 2026-09-25
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🤖 AI Summary
Drift in scanning microscopy induces spatial misalignment of signals, severely limiting the accuracy of quantitative measurements. This study extends orthogonal scan drift correction to multidimensional datasets, including spectroscopic images and diffraction patterns, proposing a general-purpose drift correction method that operates without prior structural models. By integrating affine and non-rigid deformation algorithms, the approach precisely recovers probe positions and enables accurate data resampling. Implemented as an open-source, GPU-accelerated software package, the proposed method achieves processing speedups of two to three orders of magnitude. Consequently, this work provides an efficient, automated, and routine drift correction solution for quantitative microscopy imaging.
📝 Abstract
In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the spatial assignment of the recorded signals and biases quantitative measurements across two-dimensional imaging, channel-resolved spectroscopic mapping, and scan-position-resolved diffraction analysis. Here, we extend orthogonal-scan drift correction from 2D images to spectrum images and diffraction datasets. We demonstrate how to recover probe positions using either differently oriented multidimensional scans or structural reference images. The recovered positions are used either to resample the multidimensional data onto a regular grid or to assign each recorded signal to its corrected coordinate. Our method combines affine and non-rigid correction, requires no prior structural model, and is implemented as open-source, GPU-accelerated software that reduces processing times by two to three orders of magnitude, enabling routine and automated drift correction for quantitative multidimensional microscopy.
Problem

Research questions and friction points this paper is trying to address.

drift correction
scanning microscopy
multidimensional imaging
quantitative measurement
probe position
Innovation

Methods, ideas, or system contributions that make the work stand out.

Drift Correction
Multidimensional Scanning Microscopy
Orthogonal-scan
Non-rigid Correction
GPU Acceleration
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Affiliations1
Stanford University
Authors to Follow
S
Sangjoon Lee
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
W
William Millsaps
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
D
Dasol Yoon
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
C
Caitlyn Obrero
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
G
Guoliang Hu
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
C
Corrie Barnes
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
C
Cedric Lim
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
A
Andrew Barnum
Stanford Nano Shared Facilities, Stanford University, Stanford, CA 94305, USA
A
Arthur R. C. McCray
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
Colin Ophus
Colin Ophus
Stanford University
Materials ScienceElectron MicroscopySimulationDensity Functional Theory