Diffuse Correlation Spectroscopy (DCS) allows the optical and label-free investigation of microvascular dynamics. Commonly, DCS is implemented with highly sensitive and ultra fast single-photon avalanche diodes (SPAD) for blood flow measurements from around 1-1.5cm deep inside tissue (source detector separation of 2.5-3 cm). In parallelized DCS (pDCS), we use arrays of multiple SPADs to boost the signal-to-noise ratio by averaging many independent DCS measurements. In this study, we explored the capabilities of an innovative, massively parallelized SPAD array with 500x500 single pixels for DCS for up to 250,000 parallel DCS measurements. We can show that this massively parallelized array enables viable blood flow measurements at 2cm depth (4cm source detector separation) in human subjects. Furthermore, we applied a dual detection strategy, where a secondary SPAD array probes the superficial blood flow simultaneously as a build-in reference measurement. In addition to our main results, we test and discuss methods to correct the deep flow measurement, by including simultaneously measured flow dynamics deep and superficial tissue layers via our novel dual-SPAD array measurement setup.
KEYWORDS: Deep learning, Tissues, In vivo imaging, Endoscopy, Education and training, Diseases and disorders, Biopsy, Biological samples, Tissue optics, Neural networks
Conventional imaging techniques target this problem by using specific antibody markers. Although such markers allow decent specificity, they are often limited in the field of application, especially for in vivo use, which limits the potential for clinical translations. In contrast to that, label-free optical technologies, like multiphoton microscopy (MPM), can generate highly resolved 3D images from unstained samples, by exploiting natural optical contrast. Label-free MPM can show epithelial damage and immune infiltration in unstained colon samples. Here, we imaged a mixture of T cells and neutrophils with label-free MPM. In order to obtain ground-truth images, we simultaneously recorded images of a Cd4+ specific fluorescent marker for T cells. A deep neural network was then trained for the segmentation of T cells and neutrophils based on such label-free MPM images. Upon training, this model can then be used to detect both cell types without relying on specific fluorescent markers, that were used to obtain ground truth. In the future, the augmentation of label-free MPM by such computational specificity could have great potential for in vivo endomicroscopy.
We propose a high-throughput phase-guided digital histological staining based on Fourier ptychographic microscopy using a generalizable deep neural network. Since the phase information includes the refractive index distribution of the specimen, we can digitally stain the unstained tissue slides from the quantitative phase images, which present the same color features that can be observed under a conventional microscope with the staining process. Here, we utilize Fourier Ptychographic Microscope that enables wide field and high-resolution quantitative phase imaging using multiple measurements by varying illumination angle. Additionally, we design a neural network that has remarkable generalization regarding sample dependence with the learned forward model. Along with this network architecture, we realize the efficient and effective digital staining process that does not require the labeled dataset from unstained tissue slides. We will report on the digital stained result from raw FPM images, the performance comparison, and discuss the future direction of our approach.
Diffuse correlation spectroscopy (DCS) is an optical technique that allows for non-invasive measurements of tissue perfusion, and is often used for neuromonitoring applications. However, a major challenge of DCS is low SNR for deep tissue measurements. Recent works have demonstrated the potential for SPAD arrays to provide significant SNR increases by averaging autocorrelation signals from individual speckles. Such methods may still be suboptimal for efficient signal extraction, as the individual signals may each be low fidelity. In this work, we explore alternative methods of integrating parallelized DCS signals in low photon regimes for accurate blood flow estimation.
We report tensorial tomographic Fourier ptychography (T2oFu), a nonscanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in 3D. Built upon Fourier ptychography, a quantitative phase imaging technique, T2oFu additionally highlights the vectorial nature of light. The imaging setup consists of a standard microscope equipped with an LED matrix, a polarization generator, and a polarization-sensitive camera. Permittivity tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three dimensions. We demonstrate T2oFu’s efficiency through volumetric reconstructions of refractive index, birefringence, and orientation for various validation samples, as well as tissue samples from muscle fibers and diseased heart tissue. Our reconstructions of healthy muscle fibers reveal their 3D fine-filament structures with consistent orientations. Additionally, we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.
“Anyone who uses a microscope has likely noticed the limitation of the trade-off between the field of view and the resolution”. To acquire highly resolved images at large fields of view, existing techniques typically record sequential images at different positions and then digitally stitch composite images. There are alternatives to this mechanical scanning procedure, such as structured illumination microscopy or Fourier ptychography that record sequential images at varying illuminations prevent mechanical scanning for high-resolution image composites. However, all of these approaches require sequential images and thus compromise speed, temporal resolution and experimental throughput. Here we present the Multi-Camera Array Microscope (MCAM), which is a microscope system that utilizes an array of many synchronized cameras, each with an individual imaging lens, for simultaneous image capture. The MCAM enables enhanced imaging capabilities and novel applications in various scientific and medical fields, by combining the images acquired from each individual camera-lens pair.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.