FAQs:

Q: What is alignEM?
A: alignEM is a software tool specialized for registering electron micrographs. It is able to generate scale image hierarchies, compute affine transforms, and generate aligned images using multi-image rendering.

Q: Can alignEM be used to register/"align" images other than electron micrographs?
A: Sure, however the Signal Whitening Image Matching (SWIM) was designed specifically with EM in mind. Such images are typically large in size and greyscale. alignEM provides functionality for downscaling and the ability to pass alignment results (affines)
  from lower scale levels to higher ones.

Q: What format is the output of alignEM?
A: Scaled image images and generated alignments are always stored in two formats: TIFF and Zarr.Zarr is an open-source format for the storage of chunked, compressed, N-dimensional arrays with an interface similar to NumPy. It has a Nature Methods paper:
https://www.nature.com/articles/s41592-021-01326-w

alignEM-disk-storage

For more information about Zarr: Help > Zarr Help

Q: What is meant by "scales"?
A: In alignEM a "scale" simply means a downsampled (of decreased resolution) copy of an image images.

Q: Why should data be scaled at all? Is it okay to align the full resolution images with brute force?
A: You could, but EM images tend to run large. A more efficient workflow is to:
  1) generate a hierarchy of downsampled images from the full resolution images
  2) align the lowest resolution images first
  3) pass the computed affines to the scale of next-highest resolution, and repeat until the full resolution images are in alignment. In these FAQs this is referred to as "climbing the scale hierarchy""

Q: Is it possible to generate a new scale pyramid without starting a new project? A: Yes. To rescale your dataset use the menu option Actions > Rescale... Caution (!) - Rescaling is effectually like starting over but using the same images imported previously.


Q: Why do SNR values not necessarily increase as we "climb the scale hierarchy"?
A: SNR values returned by SWIM are a relativistic readout of alignment quality which depends on image resolution. It is most useful when comparing the performance of the SWIM image matching algorithm between stationary/moving image pairs at the same scale.

Q: Why are the selected manual correlation regions not mutually independent? In other words, why does moving or removing an argument to SWIM affect the signal-to-noise ratio and resulting correlation signals of the other selected SWIM regions?
A: This is a consequence of the fact that SWIM is an iterative algorithm as it is implemented for the Default Grid, Custom Grid, and Manual Hint alignment methods

Q: What is Neuroglancer?
A: Neuroglancer is an open-source WebGL and typescript-based web application for displaying volumetric data. alignEM uses a Chromium-based API called QtWebEngine together with the Neuroglancer Python API to render large volumetric data efficiently and conveniently within the application window.

Q: What makes alignEM fast?
A: A few reasons:
  1) Multi-core processing, time-intensive processes are executed in parallel.
  2) Data scaling, SWIM alignment, and affine processing functions are all implemented in highly efficient C code written by computer scientist Arthur Wetzel.
  3) Fast Fourier Transform is a fast algorithm.

Q: How many CPUs or "cores" will alignEM use?
A: By default, as many cores as the system has available (greedy).

Q: What file types are supported?
A: Currently, only images formatted as TIFF are supported.

Q: Where can I learn more about the principles of Signal Whitening Fourier Transform Image Matching?
A: https://mmbios.pitt.edu/images/ScientificMeetings/MMBIOS-Aug2014.pdf