  Optical tomography has emerged as a non-invasive imaging method, providing
three-dimensional insights into subcellular structures and thereby enabling a
deeper understanding of cellular functions, interactions, and processes.
Conventional optical tomography methods are constrained by a limited
illumination scanning range, leading to anisotropic resolution and incomplete
imaging of cellular structures. To overcome this problem, we employ a compact
multi-core fibre-optic cell rotator system that facilitates precise optical
manipulation of cells within a microfluidic chip, achieving full-angle
projection tomography with isotropic resolution. Moreover, we demonstrate an
AI-driven tomographic reconstruction workflow, which can be a paradigm shift
from conventional computational methods, often demanding manual processing, to
a fully autonomous process. The performance of the proposed cell rotation
tomography approach is validated through the three-dimensional reconstruction
of cell phantoms and HL60 human cancer cells. The versatility of this
learning-based tomographic reconstruction workflow paves the way for its broad
application across diverse tomographic imaging modalities, including but not
limited to flow cytometry tomography and acoustic rotation tomography.
Therefore, this AI-driven approach can propel advancements in cell biology,
aiding in the inception of pioneering therapeutics, and augmenting early-stage
cancer diagnostics.
