WikiSysop: Created page with "== Citation == Kimanius, Dari / Jamali, Kiarash / Scheres, Sjors. Sparse Fourier backpropagation in cryo-EM reconstruction. 2022. Advances in Neural Information Processing Systems, Vol. 35 p. 12395-12408 == Abstract == Electron cryo-microscopy (cryo-EM) is a powerful method for investigating the structures of protein molecules, with important implications for understanding the molecular processes of life and drug development. In this technique, many noisy, two-dimensi..."

2024-07-30T05:48:36Z

Created page with "== Citation == Kimanius, Dari / Jamali, Kiarash / Scheres, Sjors. Sparse Fourier backpropagation in cryo-EM reconstruction. 2022. Advances in Neural Information Processing Systems, Vol. 35 p. 12395-12408 == Abstract == Electron cryo-microscopy (cryo-EM) is a powerful method for investigating the structures of protein molecules, with important implications for understanding the molecular processes of life and drug development. In this technique, many noisy, two-dimensi..."

New page

== Citation ==

Kimanius, Dari / Jamali, Kiarash / Scheres, Sjors. Sparse Fourier backpropagation in cryo-EM reconstruction. 2022. Advances in Neural Information Processing Systems, Vol. 35
p. 12395-12408

== Abstract ==

Electron cryo-microscopy (cryo-EM) is a powerful method for investigating the
structures of protein molecules, with important implications for understanding
the molecular processes of life and drug development. In this technique, many
noisy, two-dimensional projection images of protein molecules in unknown poses
are combined into one or more three-dimensional reconstructions. The presence
of multiple structural states in the data represents a major bottleneck in existing
processing pipelines, often requiring expert user supervision. Variational autoencoders
(VAEs) have recently been proposed as an attractive means for learning
the data manifold of data sets with a large number of different states. These
methods are based on a coordinate-based approach, similar to Neural Radiance
Fields (NeRF), to make volumetric reconstructions from 2D image data in Fourierspace.
Although NeRF is a powerful method for real-space reconstruction, many of
the benefits of the method do not transfer to Fourier-space, e.g. inductive bias for
spatial locality. We present an approach where the VAE reconstruction is expressed
on a volumetric grid, and demonstrate how this model can be trained efficiently
through a novel backpropagation method that exploits the sparsity of the projection
operation in Fourier-space. We achieve improved results on a simulated data set
and at least equivalent results on an experimental data set when compared to the
coordinate-based approach, while also substantially lowering computational cost.
Our approach is computationally more efficient, especially in inference, enabling
interactive analysis of the latent space by the user.

== Keywords ==

== Links ==

https://proceedings.neurips.cc/paper_files/paper/2022/hash/50729453d56ecf6a8b7be78998776472-Abstract-Conference.html

== Related software ==

== Related methods ==

== Comments ==

2022Kimanius Sparse - Revision history