7 research outputs found
AI-driven projection tomography with multicore fibre-optic cell rotation
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.Comment: 15 pages, 6 figure
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an
emerging in vivo label-free endoscopic imaging modality with minimal
invasiveness. However, the computational demands of conventional iterative
phase retrieval algorithms have limited their real-time imaging potential. We
demonstrate a learning-based MCF phase imaging method, that significantly
reduced the phase reconstruction time to 5.5 ms, enabling video-rate imaging at
181 fps. Moreover, we introduce an innovative optical system that automatically
generated the first open-source dataset tailored for MCF phase imaging,
comprising 50,176 paired speckle and phase images. Our trained deep neural
network (DNN) demonstrates robust phase reconstruction performance in
experiments with a mean fidelity of up to 99.8\%. Such an efficient fiber phase
imaging approach can broaden the applications of QPI in hard-to-reach areas.Comment: 5 pages. 5 figure
Quantitative phase imaging through an ultra-thin lensless fiber endoscope
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 with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample 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, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications
Compressive holographic sensing simplifies quantitative phase imaging
Abstract Quantitative phase imaging (QPI) has emerged as method for investigating biological specimen and technical objects. However, conventional methods often suffer from shortcomings in image quality, such as the twin image artifact. A novel computational framework for QPI is presented with high quality inline holographic imaging from a single intensity image. This paradigm shift is promising for advanced QPI of cells and tissues
Quantitative phase imaging through an ultra-thin lensless fiber endoscope
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 with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample 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, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications