TY - JOUR
T1 - A Consensus Framework of Distributed Multiple-Tilt Reconstruction in Electron Tomography
AU - Wang, Zihao
AU - Zhang, Jingrong
AU - Gao, Weifang
AU - Liu, Zhiyong
AU - Wan, Xiaohua
AU - Zhang, Fa
N1 - Publisher Copyright:
© Copyright 2020, Mary Ann Liebert, Inc.
PY - 2020/2
Y1 - 2020/2
N2 - The "missing wedge" of a single tilt in electron tomography introduces severe artifacts into the reconstructed results. To reduce the "missing wedge" effect, a widely used method is "multiple-tilt reconstruction," which collects projections using multiple axes. However, as the number of tilt series increases, the computing and memory costs also rise. The degree of parallelism is limited by the sample thickness, and a large memory requirement cannot be met by most multicore computers. In our study, we present a new fully distributed multiple-tilt simultaneous iterative reconstruction technique (DM-SIRT). To improve the parallelism of the reconstruction process and reduce the memory requirements of each process, we formulate the multiple-tilt reconstruction as a consensus optimization problem and design a DM-SIRT algorithm. Experiments show that in addition to slightly better resolution, DM-SIRT can obtain a 13.9 × accelerated ratio compared with the full multiple-tilt reconstruction version. It also has a 97% decrease in memory overhead and is 16 times more scalable than the full reconstruction version.
AB - The "missing wedge" of a single tilt in electron tomography introduces severe artifacts into the reconstructed results. To reduce the "missing wedge" effect, a widely used method is "multiple-tilt reconstruction," which collects projections using multiple axes. However, as the number of tilt series increases, the computing and memory costs also rise. The degree of parallelism is limited by the sample thickness, and a large memory requirement cannot be met by most multicore computers. In our study, we present a new fully distributed multiple-tilt simultaneous iterative reconstruction technique (DM-SIRT). To improve the parallelism of the reconstruction process and reduce the memory requirements of each process, we formulate the multiple-tilt reconstruction as a consensus optimization problem and design a DM-SIRT algorithm. Experiments show that in addition to slightly better resolution, DM-SIRT can obtain a 13.9 × accelerated ratio compared with the full multiple-tilt reconstruction version. It also has a 97% decrease in memory overhead and is 16 times more scalable than the full reconstruction version.
KW - TxBR
KW - consensus optimization
KW - cryoelectron tomography
KW - multiple-tilt reconstruction
KW - parallel computing
UR - http://www.scopus.com/inward/record.url?scp=85079640493&partnerID=8YFLogxK
U2 - 10.1089/cmb.2019.0287
DO - 10.1089/cmb.2019.0287
M3 - Article
C2 - 31794252
AN - SCOPUS:85079640493
SN - 1066-5277
VL - 27
SP - 212
EP - 222
JO - Journal of Computational Biology
JF - Journal of Computational Biology
IS - 2
ER -