Three-dimensional tomography of red blood cells using deep learning


Joowon Lim and Ahmed B. Ayoub and Demetri Psaltis


“We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly
ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are
introduced on reconstructions obtained by the Wolf transform inversion method due to the ill-posed
measurements acquired from the limited numerical apertures (NAs) of the optical system. Despite the
recent success of DNNs in solving ill-posed inverse problems, the application to 3-D optical imaging is
particularly challenging due to the lack of the ground truth. We overcome this limitation by generating
digital phantoms that serve as samples for the discrete dipole approximation (DDA) to generate multiple
2-D projection maps for a limited range of illumination angles. The presented samples are red blood cells
(RBCs), which are highly affected by the ill-posed problems due to their morphology. The trained network
using synthetic measurements from the digital phantoms successfully eliminates the introduced distortions.
Most importantly, we obtain high fidelity reconstructions from experimentally recorded projections of real RBC
sample using the network that was trained on digitally generated RBC phantoms. Finally, we confirm the
reconstruction accuracy using the DDA to calculate the 2-D projections of the 3-D reconstructions and
compare them to the experimentally recorded projections.”

Link to Publications Page

Publication: Advanced Photonics
Issue/Year/DOI: Vol. 2, Issues 2
DOI: 10.1117/1.AP.2.2.026001