In medical collaboration with: |
Deutsches Zentrum für Neurodegenerative Erkrankungen e.V.
Scientific Director: Nassir Navab
Contact Person(s): Shadi Albarqouni
Reconstruction, Molecular Imaging, Machine Learning for Medical Applications, Microscopic Image Analysis
Cryo-ET has recently emerged as a leading technique to investigate the three-dimensional(3D) structure of biological specimens at close-to-native state. It also bridges the gap between low-resolution imaging techniques (e.g. light microscopy) and high-resolution techniques (e.g. single particle electron microscopy). The technique consists of acquiring many two-dimensional (2D) projections of the structure under scrutiny at various tilt angles under cryogenic conditions. The 3D structure is recovered through a number of steps including projection alignment, reconstruction and de-nosing. The resolution of the reconstructed tomogram, however, is aaffected by the low signal to noise ratio (SNR) of the projection images (typically 0.1 to 0.01) and the limited angular coverage (typically 60 to 70 ) resulting in wedge-shaped missing information in Fourier space, the so-called missing wedge; making the reconstruction process very challenging and demanding.
Visit our lab at Garching.
internal project page
Please contact Shadi Albarqouni
for available student projects within this research project.
The following presentation is a simple overview of the whole image processing pipeline in CryoET?
| Date || Topic || Conducted by || Materials |
| Feb. 2014 || Gradient Projection for Regularized Cryo-Electron Tomographic Reconstruction || || CMMI-MICCAI Paper |
| Oct. 2014 || Image Denoising in High Dimensional Space || MLMI Group || Project webpage |
| Feb. 2015 || Multi-scale Graph-based Guided Filter for De-noising Cryo-Electron Tomographic Data (MG2F) || Shadi || BMVC Paper, BMVC Poster, Toy Example |
| Jun. 2015 || RISE Internship || Claire || TBA |