# AI-driven projection tomography with multicore fibre-optic cell rotation

arXiv: https://arxiv.org/abs/2312.07631 · HTML: https://mlsanity.com/paper/2312.07631/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 70 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Fringe pattern analysis using deep learning](https://mlsanity.com/paper/1807.02757/) (2018)
- [DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning](https://mlsanity.com/paper/2009.13777/) (2020)

## Built on by (most cited first, 1 of 1)

- [Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology](https://mlsanity.com/paper/2407.18456/) (2024)

## Abstract (arXiv)

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.