Drift correction improves accuracy of multidimensional microscopy scans
Universal Drift Correction for Multidimensional Scanning Microscopy
Computer Vision and Pattern Recognition
Summary
When microscopes scan samples, small shifts called drift can cause images and data to be misaligned, making measurements less accurate. The authors developed a method to detect and fix these shifts in complex microscope data, including images with multiple color channels and patterns. Their approach works without needing a detailed model of the sample and runs very fast using GPU computing. This helps scientists get more reliable and precise information from their microscopes automatically.
What this means in practice
- •For microscopy facility staff: Automatically correct spatial misalignments in multidimensional microscope datasets to improve measurement reliability and speed up data processing.
- •For biomedical imaging engineers: Enhance the accuracy of complex spectroscopic and diffraction data from scanning microscopes without requiring prior sample models or manual alignment.
Authors
Sangjoon Lee, William Millsaps, Dasol Yoon, Caitlyn Obrero, Guoliang Hu, Corrie Barnes, Cedric Lim, Andrew Barnum, Arthur R. C. McCray, Colin Ophus
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.