We developed a flexible endomicroscopy (FEM) in reflection geometry for quantitative phase imaging of unlabeled thick samples. FEM provides the phase information by wavefront modulation using chessboard grating and phase-shifting. FEM system features high lateral and axial resolutions of 1.2 and 8.3 μm, respectively, with a probe diameter of 2.5 mm. By testing pathologic slices and thick opaque mammalian tissue, FEM identifies normal and tumor glandular structures, secreta, and tomographic skin layers. With the potential for direct morphological and phase measurement, high resolution, and thin fiber tip, the label-free FEM could be an attractive tool for various clinical applications.
We present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity non-destructively on unlabeled specimens. ACM is equipped with a laser scanning confocal microscopy with a quantitative phase imaging module, which provides optical path-length maps of the specimen colocalized with the fluorescence channel. Using pairs of phase and fluorescence images, a convolution neural network was trained to translate the former into the latter. The ACM images hold much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids.
We proposed a fast 3D RI construction method, based on the Wolf equations for propagating correlations of partially coherent light. This approach, referred to as Wolf phase tomography (WPT), involves minimal computational steps, renders high-resolution RI tomograms, without time-consuming deconvolution operations. WPT decouples the refractive index distribution from the thickness of the sample directly in the space-time domain, without the need for Fourier transformation. We demonstrate that, from three independent intensity measurements corresponding to each phase shift, the RI distribution is reconstructed directly from the Laplacian and second time derivative of the complex correlation functions.
We demonstrate that live-dead cell assay can be conducted in a label-free manner using quantitative phase imaging and deep learning. We apply the concept of our newly-developed phase imaging with computational specificity (PICS) to digitally stain for the live/dead markers. HeLa cultured mixed with viability fluorescent reagents (ReadyProbes, ThermoFisher) were imaged for 24 hours by spatial light interference microscopy (SLIM) and fluorescent microscopy. Based on the ratio of the two fluorescence signals, semantic segmentation maps were generated to label the state of the cell as either live, injured, or dead. We trained an EfficientNet to infer cell viability from SLIM images with semantic maps as ground truth. Validated on the testing dataset, the trained network reported an F1 score of 73.4%, 97.0%, and 94.3% in identifying live, injured, and dead cells, respectively.
We propose synthetic aperture gradient light interference microscopy (SA-GLIM) as a solution to avoid computational complexity in standard Fourier pytchographic microscopy. This new system combines direct phase measurements from GLIM with various illumination angles, and a synthetic aperture reconstruction method, to produce high resolution, large FOV quantitative phase maps. Using a 5× objective lens (NA = 0.15), SA-GLIM generates phase maps with a spatial resolution of 850 nm and FOV approximately 1.7×1.7 mm2. We tested the performance using a mixture of polystyrene beads (1 μm and 3 μm in diameter), and the smaller beads can be easily resolved in the final image. Compared with standard FPM, SA-GLIM records substantially fewer low-resolution images, which makes the data throughput highly efficient.
The early detection of cervical dysplasia enables early treatment, a critical factor in cancer prevention. In the United States, cervical cancer screening is age-based and includes cervical cytology with human papilloma virus (HPV) testing with referral to colposcopy for abnormal results. Colposcopy is used to visualize changes in the appearance of the transformation zone to direct biopsies which can confirm a diagnosis of dysplasia or cancer. Directed biopsies can be limited in detection of abnormalities because they represent a small area of the transformation zone and can be limited by provider expertise. Additionally, biopsies contribute to patient discomfort and anxiety awaiting for results.
We recently reported the first in vivo cervical data from angle-resolved low-coherence interferometry (a/LCI), an optical technique that measures nuclear size as a biomarker for dysplasia, which is well-suited for screening due to its high sensitivity and specificity and its non-invasive utilization. However, in order to target the single-point measurements of the a/LCI instrument, we aimed to construct a probe capable of mapping the cervical epithelium to identify the transformation zone between the ectocervical and endocervical epithelia, the location at which dysplasia is most likely to develop.
We termed this complementary technology multiplexed low-coherence interferometer (m/LCI). Thirty-six parallel fiber-optic interferometers were constructed to obtain optical depth profiles using spectral-domain LCI. Light from each channel is delivered to the cervix via a 6x6 fiber-optic bundle and a custom endoscopic probe. The depth-profile from each optical channel enables the identification of the ectocervix and endocervix.
A pilot study at Duke University (n=5) was followed by an ongoing clinical study at New York City Health + Hospitals/Jacobi (Bronx, New York) (current n=20, target n=50). We present the results from these first studies to demonstrate the feasibility of m/LCI as a means of identifying the transformation zone for screening of dysplasia.
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