Multi order correction algorithms to remove image distortions from mass spectrometry imaging datasets

Abstract

Time-of-flight secondary ion mass spectrometry imaging is a rapidly evolving technology. Its main application is the study of the distribution of small molecules on biological tissues. The sequential image acquisition process remains susceptible to measurement distortions that can render imaging data less analytically useful. Most of these artifacts show a repetitive nature from tile to tile. Here we statistically describe these distortions and derive two different algorithms to correct them. Both, a generalized linear model approach and the linear discriminant analysis approach are able to increase image quality for negative and positive ion mode datasets. Additionally, performing simulation studies with repetitive and non-repetitive tiling error we show that both algorithms are only removing repetitive distortions. It is further shown that the spectral component of the dataset is not altered by the use of these correction methods. Both algorithms presented in this work greatly increase the image quality and improve the analytical usefulness of distorted images dramatically.