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

Medical Imaging of Microrobots: Toward In Vivo Applications

Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications

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

Volumetric HiLo microscopy employing an electrically tunable lens

Electrically tunable lenses exhibit strong potential for fast motion-free axial scanning in a variety of microscopes. However, they also lead to a degradation of the achievable resolution because of aberrations and misalignment between illumination and detection optics that are induced by the scan itself. Additionally, the typically nonlinear relation between actuation voltage and axial displacement leads to over- or under-sampled frame acquisition in most microscopic techniques because of their static depth-of-field. To overcome these limitations, we present an Adaptive-Lens-High-and-Low-frequency (AL-HiLo) microscope that enables volumetric measurements employing an electrically tunable lens. By using speckle-patterned illumination, we ensure stability against aberrations of the electrically tunable lens. Its depth-of-field can be adjusted a-posteriori and hence enables to create flexible scans, which compensates for irregular axial measurement positions. The adaptive HiLo microscope provides an axial scanning range of 1 mm with an axial resolution of about 4 μm and sub-micron lateral resolution over the full scanning range. Proof of concept measurements at home-built specimens as well as zebrafish embryos with reporter gene-driven fluorescence in the thyroid gland are shown

Structured illumination 3D microscopy using adaptive lenses and multimode fibers

Microscopic techniques with high spatial and temporal resolution are required for in vivo studying biological cells and tissues. Adaptive lenses exhibit strong potential for fast motion-free axial scanning. However, they also lead to a degradation of the achievable resolution because of aberrations. This hurdle can be overcome by digital optical technologies. We present a novel High-and-Low-frequency (HiLo) 3D-microscope using structured illumination and an adaptive lens. Uniform illumination is used to obtain optical sectioning for the high-frequency (Hi) components of the image, and nonuniform illumination is needed to obtain optical sectioning for the low-frequency (Lo) components of the image. Nonuniform illumination is provided by a multimode fiber. It ensures robustness against optical aberrations of the adaptive lens. The depth-of-field of our microscope can be adjusted a-posteriori by computational optics. It enables to create flexible scans, which compensate for irregular axial measurement positions. The adaptive HiLo 3D-microscope provides an axial scanning range of 1 mm with an axial resolution of about 4 microns and sub-micron lateral resolution over the full scanning range. In result, volumetric measurements with high temporal and spatial resolution are provided. Demonstration measurements of zebrafish embryos with reporter gene-driven fluorescence in the thyroid gland are presented

Complex Wavefront Shaping through a Multi-Core Fiber

Wavefront shaping through a multi-core fiber (MCF) is turning into an attractive method for endoscopic imaging and optical cell-manipulation on a chip. However, the discrete distribution and the low number of cores induce pixelated phase modulation, becoming an obstacle for delivering complex light field distributions through MCFs. We demonstrate a novel phase retrieval algorithm named Core–Gerchberg–Saxton (Core-GS) employing the captured core distribution map to retrieve tailored modulation hologram for the targeted intensity distribution at the distal far-field. Complex light fields are reconstructed through MCFs with high fidelity up to 96.2%. Closed-loop control with experimental feedback denotes the capability of the Core-GS algorithm for precise intensity manipulation of the reconstructed light field. Core-GS provides a robust way for wavefront shaping through MCFs; it facilitates the MCF becoming a vital waveguide in endoscopic and lab-on-a-chip applications