TY - GEN
T1 - Joint Motion-Correction and Reconstruction in Cryo-Em Tomography
AU - Wang, Yuanhao
AU - Idoughi, Ramzi
AU - Heidrich, Wolfgang
N1 - KAUST Repository Item: Exported on 2022-10-20
PY - 2022
Y1 - 2022
N2 - Tilt-series cryo-electron tomography (cryoET) is an established imaging technique used in several scientific fields to determine samples’ three-dimensional (3D) structures at nearatomic resolutions. However, the motion and misalignment that occur during the acquisition stage are major limiting factors to reaching smaller resolutions. Indeed, they introduce blur and artifacts, which deteriorate the reconstruction quality. In this paper, we propose a joint motion-correction and reconstruction framework to improve the quality of the output volume and, consequently, its resolution. Our framework first estimates the motion field in the sample in order to correct the captured data. Then an iterative plug-and-play prior approach is used to reconstruct the sample. The validation of our approach on real captured datasets shows a good quality reconstruction translated in a resolution improvement.
AB - Tilt-series cryo-electron tomography (cryoET) is an established imaging technique used in several scientific fields to determine samples’ three-dimensional (3D) structures at nearatomic resolutions. However, the motion and misalignment that occur during the acquisition stage are major limiting factors to reaching smaller resolutions. Indeed, they introduce blur and artifacts, which deteriorate the reconstruction quality. In this paper, we propose a joint motion-correction and reconstruction framework to improve the quality of the output volume and, consequently, its resolution. Our framework first estimates the motion field in the sample in order to correct the captured data. Then an iterative plug-and-play prior approach is used to reconstruct the sample. The validation of our approach on real captured datasets shows a good quality reconstruction translated in a resolution improvement.
UR - http://hdl.handle.net/10754/684200
UR - https://ieeexplore.ieee.org/document/9897501/
U2 - 10.1109/ICIP46576.2022.9897501
DO - 10.1109/ICIP46576.2022.9897501
M3 - Conference contribution
SN - 978-1-6654-9621-6
BT - 2022 IEEE International Conference on Image Processing (ICIP)
PB - IEEE
ER -