Quantitative phase imaging through an ultra-thin lensless fiber endoscope
Not on the map. Why?
Abstract (arXiv)
Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging of biomedical samples with up to 1 μm lateral resolution and nanoscale axial resolution. The incident complex light field at the measurement side is precisely reconstructed from a single-shot far-field speckle pattern at the detection side, enabling digital focusing and 3D volumetric reconstruction without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, 3D imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2024All-optical complex field imaging using diffractive processorscited by 2
- 2023Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learningcited by 1
- 2024Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathologycited by 0
- 2024Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology26 Jul 2024
- 2024All-optical complex field imaging using diffractive processors30 Jan 2024
- 2023Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning12 Dec 2023