Open AccessSelf-supervised next view prediction for limited-angle optical projection tomography
Author(s) -
Hao Zhang,
Binbing Liu,
Peng Fei
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.472762
Subject(s) - tomography , projection (relational algebra) , computer science , artificial intelligence , computer vision , range (aeronautics) , optics , iterative reconstruction , computed tomography , diffuse optical imaging , sample (material) , algorithm , physics , materials science , medicine , composite material , radiology , thermodynamics
Optical projection tomography captures 2-D projections of rotating biological samples and computationally reconstructs 3-D structures from these projections, where hundreds of views with an angular range of π radian is desired for a reliable reconstruction. Limited-angle tomography tries to recover the structures of the sample using fewer angles of projections. However, the result is far from satisfactory due to the missing of wedge information. Here we introduce a novel view prediction technique, which is able to extending the angular range of captured views for the limited-angle tomography. Following a self-supervised technique that learns the relationship between the captured limited-angle views, unseen views can be computationally synthesized without any prior label data required. Combined with an optical tomography system, the proposed approach can robustly generate new projections of unknown biological samples and extends the angles of the projections from the original 60° to nearly 180°, thereby yielding high-quality 3-D reconstructions of samples even with highly incomplete measurement.