Markov Random Field Based Automatic Image Alignment for ElectronTomography
Cryo electron tomography (cryo-ET) is the primary method for obtaining 3D reconstructions of intact bacteria, viruses, and complex molecular machines ([7],[2]). It first flash freezes a specimen in a thin layer of ice, and then rotates the ice sheet in a transmission electron microscope (TEM) record...
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ftunivnotexas:info:ark/67531/metadc896693 2023-05-15T16:41:29+02:00 Markov Random Field Based Automatic Image Alignment for ElectronTomography Moussavi, Farshid Amat, Fernando Comolli, Luis R. Elidan, Gal Downing, Kenneth H. Horowitz, Mark 2007-11-30 Text http://digital.library.unt.edu/ark:/67531/metadc896693/ English eng COLLABORATION - StanfordU. rep-no: LBNL--63633 grantno: DE-AC02-05CH11231 osti: 925600 http://digital.library.unt.edu/ark:/67531/metadc896693/ ark: ark:/67531/metadc896693 Neural Information Processing Systems Conference,Hyatt Regency, Vancouver, B.C., Canada, December 3-6,2007 59 Gold Processing Electron Microscopes Cryogenics Tomography Electrons Resolution Viruses Bacteria Alignment Accuracy Lenses Article 2007 ftunivnotexas 2016-10-01T22:11:43Z Cryo electron tomography (cryo-ET) is the primary method for obtaining 3D reconstructions of intact bacteria, viruses, and complex molecular machines ([7],[2]). It first flash freezes a specimen in a thin layer of ice, and then rotates the ice sheet in a transmission electron microscope (TEM) recording images of different projections through the sample. The resulting images are aligned and then back projected to form the desired 3-D model. The typical resolution of biological electron microscope is on the order of 1 nm per pixel which means that small imprecision in the microscope's stage or lenses can cause large alignment errors. To enable a high precision alignment, biologists add a small number of spherical gold beads to the sample before it is frozen. These beads generate high contrast dots in the image that can be tracked across projections. Each gold bead can be seen as a marker with a fixed location in 3D, which provides the reference points to bring all the images to a common frame as in the classical structure from motion problem. A high accuracy alignment is critical to obtain a high resolution tomogram (usually on the order of 5-15nm resolution). While some methods try to automate the task of tracking markers and aligning the images ([8],[4]), they require user intervention if the SNR of the image becomes too low. Unfortunately, cryogenic electron tomography (or cryo-ET) often has poor SNR, since the samples are relatively thick (for TEM) and the restricted electron dose usually results in projections with SNR under 0 dB. This paper shows that formulating this problem as a most-likely estimation task yields an approach that is able to automatically align with high precision cryo-ET datasets using inference in graphical models. This approach has been packaged into a publicly available software called RAPTOR-Robust Alignment and Projection estimation for Tomographic Reconstruction. Article in Journal/Newspaper Ice Sheet University of North Texas: UNT Digital Library |
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Open Polar |
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University of North Texas: UNT Digital Library |
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ftunivnotexas |
language |
English |
topic |
59 Gold Processing Electron Microscopes Cryogenics Tomography Electrons Resolution Viruses Bacteria Alignment Accuracy Lenses |
spellingShingle |
59 Gold Processing Electron Microscopes Cryogenics Tomography Electrons Resolution Viruses Bacteria Alignment Accuracy Lenses Moussavi, Farshid Amat, Fernando Comolli, Luis R. Elidan, Gal Downing, Kenneth H. Horowitz, Mark Markov Random Field Based Automatic Image Alignment for ElectronTomography |
topic_facet |
59 Gold Processing Electron Microscopes Cryogenics Tomography Electrons Resolution Viruses Bacteria Alignment Accuracy Lenses |
description |
Cryo electron tomography (cryo-ET) is the primary method for obtaining 3D reconstructions of intact bacteria, viruses, and complex molecular machines ([7],[2]). It first flash freezes a specimen in a thin layer of ice, and then rotates the ice sheet in a transmission electron microscope (TEM) recording images of different projections through the sample. The resulting images are aligned and then back projected to form the desired 3-D model. The typical resolution of biological electron microscope is on the order of 1 nm per pixel which means that small imprecision in the microscope's stage or lenses can cause large alignment errors. To enable a high precision alignment, biologists add a small number of spherical gold beads to the sample before it is frozen. These beads generate high contrast dots in the image that can be tracked across projections. Each gold bead can be seen as a marker with a fixed location in 3D, which provides the reference points to bring all the images to a common frame as in the classical structure from motion problem. A high accuracy alignment is critical to obtain a high resolution tomogram (usually on the order of 5-15nm resolution). While some methods try to automate the task of tracking markers and aligning the images ([8],[4]), they require user intervention if the SNR of the image becomes too low. Unfortunately, cryogenic electron tomography (or cryo-ET) often has poor SNR, since the samples are relatively thick (for TEM) and the restricted electron dose usually results in projections with SNR under 0 dB. This paper shows that formulating this problem as a most-likely estimation task yields an approach that is able to automatically align with high precision cryo-ET datasets using inference in graphical models. This approach has been packaged into a publicly available software called RAPTOR-Robust Alignment and Projection estimation for Tomographic Reconstruction. |
format |
Article in Journal/Newspaper |
author |
Moussavi, Farshid Amat, Fernando Comolli, Luis R. Elidan, Gal Downing, Kenneth H. Horowitz, Mark |
author_facet |
Moussavi, Farshid Amat, Fernando Comolli, Luis R. Elidan, Gal Downing, Kenneth H. Horowitz, Mark |
author_sort |
Moussavi, Farshid |
title |
Markov Random Field Based Automatic Image Alignment for ElectronTomography |
title_short |
Markov Random Field Based Automatic Image Alignment for ElectronTomography |
title_full |
Markov Random Field Based Automatic Image Alignment for ElectronTomography |
title_fullStr |
Markov Random Field Based Automatic Image Alignment for ElectronTomography |
title_full_unstemmed |
Markov Random Field Based Automatic Image Alignment for ElectronTomography |
title_sort |
markov random field based automatic image alignment for electrontomography |
publisher |
COLLABORATION - StanfordU. |
publishDate |
2007 |
url |
http://digital.library.unt.edu/ark:/67531/metadc896693/ |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
Neural Information Processing Systems Conference,Hyatt Regency, Vancouver, B.C., Canada, December 3-6,2007 |
op_relation |
rep-no: LBNL--63633 grantno: DE-AC02-05CH11231 osti: 925600 http://digital.library.unt.edu/ark:/67531/metadc896693/ ark: ark:/67531/metadc896693 |
_version_ |
1766031925320876032 |