Self-supervised next view prediction for limited-angle optical projection tomography

Abstract

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.