Precise 3D image alignment in micro‐axial tomography

Abstract

SummaryMicro ( µ -) axial tomography is a challenging technique in microscopy which improves quantitative imaging especially in cytogenetic applications by means of defined sample rotation under the microscope objective. The advantage of µ -axial tomography is an effective improvement of the precision of distance measurements between point-like objects. Under certain circumstances, the effective (3D) resolution can be improved by optimized acquisition depending on subsequent, multi-perspective image recording of the same objects followed by reconstruction methods. This requires, however, a very precise alignment of the tilted views. We present a novel feature-based image alignment method with a precision better than the full width at half maximum of the point spread function. The features are the positions (centres of gravity) of all fluorescent objects observed in the images (e.g. cell nuclei, fluorescent signals inside cell nuclei, fluorescent beads, etc.). Thus, real alignment precision depends on the localization precision of these objects. The method automatically determines the corresponding objects in subsequently tilted perspectives using a weighted bipartite graph. The optimum transformation function is computed in a least squares manner based on the coordinates of the centres of gravity of the matched objects. The theoretically feasible precision of the method was calculated using computer-generated data and confirmed by tests on real image series obtained from data sets of 200 nm fluorescent nano-particles. The advantages of the proposed algorithm are its speed and accuracy, which means that if enough objects are included, the real alignment precision is better than the axial localization precision of a single object. The alignment precision can be assessed directly from the algorithm's output. Thus, the method can be applied not only for image alignment and object matching in tilted view series in order to reconstruct (3D) images, but also to validate the experimental performance (e.g. mechanical precision of the tilting). In practice, the key application of the method is an improvement of the effective spatial (3D) resolution, because the well-known spatial anisotropy in light microscopy can be overcome. This allows more precise distance measurements between point-like objects.

Micro axial tomography: A miniaturized, versatile stage device to overcome resolution anisotropy in fluorescence light microscopy

“…The precision of the tilting angle is measured from the bead images using an alignment algorithm based on a weighted bipartite graph where the optimum transformation function is computed in a least squares manner based on the coordinates of the bary centers of the matched objects. 11 The lateral movement of the fiber was below 0.15% relative to the visible fiber length of 2000 μm between the two fiber bearings. 9 Following the recording of the raw image stacks, the best focused image for each particle was included into a composite image constructed from each stack of 80 images, each representing a certain angular position.…”

“…8,10,11 In short, from each angle of view, a stack of typically 80 images was taken from focal planes incremented by two micrometers each. To use all objects distributed around the fiber, a full turn of the fiber with 20 individual angular positions was recorded.…”

“…This allows an automated multi-view 3D image acquisition 9, 10 and precise 3D image alignment of different perspectives of the same objects. 11 So far, micro axial tomography has been applied to 3D studies of cell nuclei after specific genome labeling 12 using a setup with an external stepping motor and a flexible shaft 8 which due to mechanical insufficiencies appeared to be too laborious to be implemented in a routinely applied microscope. Nevertheless, it was also used to precisely measure focal depth dependent chromatic shifts.…”

The role of chromatin condensation during granulopoiesis in the regulation of gene cluster expression

“…We performed deconvolution of raw data using Huygens software and chromatic aberration correction of the microscope images before image analysis. 25 Small 3D images of individual cells were cropped from images containing more cells.…”

Automated acquisition and processing of multidimensional image data in confocal in vivo microscopy

“…It stems from the previously published method for fast image registration of two points sets in multi-view imaging (Matula et al, 2003). The published algorithm used a Hungarian method (Kuhn-Munkres algorithm) from the graph theory to solve one-to-one correspondence problems between two point sets.…”