CHARACTERIZING AND CORRECTING IMAGE DISTORTIONS ON THE TECNAI MICROSCOPES

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Since starting to acquire data on our Tecnai microscopes, we have been concerned about image distortions impairing the quality of our tomographic reconstructions. The problem is particularly severe with images from the Gatan Imaging Filter (GIF), because of distortions induced in both the GIF and the low magnification lens mode needed to use the GIF. We developed a program to solve for the image distortion field from a set of pairs of overlapping images of a specimen at two stage positions. The two images of a pair should overlap by about 55%, and pairs at four distinct orientations must be obtained. Our data acquisition program, SerialEM, has a routine to acquire these pairs easily, so it is now straightforward to acquire such data and compute an image distortion field. Another kind of distortion occurs with a tilted specimen, because it is sitting in a gradient in which magnification and image rotation vary with Z height. The effect of this gradient shows up prominently when a montage is taken of overlapping images of a tilted specimen. There are substantial errors when trying to shift the frames into register; the errors are eliminated when gradient distortions are corrected for. The gradient can be measured easily by taking images with the specimen at two different Z heights and determining the size change and rotation between them. The gradient depends only on beam intensity, so by recording beam intensity during each tilt series it is possible to correct images for this gradient. Corrections for both kinds of gradients have been incorporated into our standard processing stream for tomography, so they are easy for users to apply. So far, correcting for the GIF image distortion field has been found to improve image alignment substantially, while correcting for the gradients has given insignificant improvements.