KEYWORDS: Image restoration, Education and training, X-ray imaging, Deep learning, X-rays, Reconstruction algorithms, Image quality, Data modeling, Point spread functions, Algorithm development
A novel method for image restoration is introduced that uses a synthetic prior intermediate (SPI) which is passed through a forward imaging operator, creating a data pair well-structured for inverse operator optimization, of which network training is of particular interest. This technique is applied to a critical problem in x-ray reconstruction: noise and artefact removal. We discuss the creation of the SPI through state-of-the-art Deep Learning Reconstruction (DLR), a spatially variant heuristic data-driven forward model for spectrally accurate noise and artefact modelling, and final image reconstruction via a convolutional neural network. Qualitative and quantitative performance is then benchmarked on a range of samples, comparing legacy reconstruction (FDK), state-of-the-art DLR, and SPI based reconstruction. SPI based reconstruction better recovers small features while also reducing residual sampling artefacts in large features. Quantitative analysis of SPI reconstruction showed a 40% throughput improvement relative to the state-of-the-art at a comparable image quality.
X-ray computed laminographic tomography (CLT) is a viable tool for creating high-throughput volumetric imaging of large, planar samples. In this work, we present a self-supervised deep image restoration workflow to produce noise-free, artifact-free volumetric reconstructions for laminographic tomography. We demonstrate our CLT method on a variety of samples scanned with an in-house prototype system, showing that our proposed method notably outperforms classic reconstruction methods, that has the potential for more accurate detection of defects and estimation of critical dimensions, thereby providing a feasible solution for rapid inline inspection and failure analysis in advanced integrated circuits packaging.
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.
KEYWORDS: Organisms, Microscopes, Tomography, Algorithm development, 3D modeling, Detection and tracking algorithms, 3D tracking, 3D image processing, Reconstruction algorithms, Muscles
It is challenging to study behavior of and track freely-moving model organisms using conventional 3D microscopy techniques. To overcome motion artifacts and prevent the organism from leaving the field of view (FOV), existing techniques require paralyzing or otherwise immobilizing the organism. Here, we demonstrate hemispheric Fourier light field tomography, featuring a parabolic objective that enables synchronized multi-view fluorescence imaging over ~2pi steradians at up to 120 fps and across multi-millimeter 3D FOVs. Our method is not only able to track the 6D pose of freely-moving zebrafish and fruit fly larvae, but also other properties such as heartbeat, fin motion, jaw motion, and muscle contractions. We also demonstrate simultaneous multi-organism imaging.
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.
We present a high-throughput computational imaging system capable of performing dense, volumetric fluorescence imaging of freely moving organisms at up to 120 volumes per second. Our method, termed 2pi Fourier light field tomography (2pi-FLIFT), consists of a planar array of 54 cameras and a parabolic mirror serving as the primary objective that allows for synchronized multi-view video capture over ~2pi steradians. 2pi-FLIFT features a novel 3D reconstruction algorithm that recovers both the 3D fluorescence distribution and attenuation map of dynamic samples. We demonstrate 2pi-FLIFT on important, freely moving model organisms, such as zebrafish and fruit fly larvae.
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
Microscopic imaging of anisotropic samples has many important applications in cytopathology. The endogenous contrast from the polarization properties of a specimen, such as its birefringence, provides valuable diagnostic information for several deadly diseases, including cardiac amyloidosis and squamous cell carcinoma, for example. In the past, polarized light microscopy (PLM) has been widely used as a diagnostic tool during the clinical review. However, in analogy with the standard microscope, the PLM typically has a restricted spatial-bandwidth product (SBP). As a consequence, one can either image a large area with low resolution or see the details of a very small area of the sample at the resolutions required for accurate analysis. To address the SBP issue of the PLM, we propose a computational microscopy method, termed vectorial Fourier ptychography, to illuminate the specimen with polarized light from different angles and detects different polarization states of the diffracted light. By illuminating a specimen with plane waves from different angles, our vectorial Fourier ptychography method effectively modulates the high-spatial-frequency components of the specimen into lower frequencies that can be detected by the optical system. With a Jones calculus-based forward model and a second-order phase retrieval method, we can reconstruct high-resolution, wide field-of-view(FOV) amplitude, phase, birefringence, retardance, and diattenuation of the specimen. To assess the reconstruction accuracy of our method, we imaged polystyrene beads submerged in immersion oils of different refractive index, as well as monosodium urate crystals. Further, To validate the diattenuation reconstruction accuracy, we reconstruct a USAF resolution test chart with a half blocked by a linear polarizer. These experiments confirm quantitatively accurate reconstruction results with a 1.25 um full-pitch resolution over a FOV of 6.6 x 4.4 mm^2, which is 5 times higher than the native (brightfield) resolution of the non-computational optical system. Finally, we demonstrate our technique by producing high SBP polarization images of several anisotropic biologic samples, includes collagen tissue, congo red stained cardiac tissue, and a bean root sample.
We propose a new sensitive diffuse correlation spectroscopy(DCS) method that can probe and identify different decorrelation events happens in sub-second, by acquiring parallelized measurements from 12 fiber detectors placed at different positions on the tissue-phantom surface with a 32 ×32 SPAD array, and process the data with deep learning methods. Both experimental and simulation phantom studies are conducted to evaluate the performance of our system in classifying and imaging decorrelation patterns presented under a 5mm thick tissue phantom made with rapidly decorrelating scattering media.
We present a simple low-cost microscope that uses a vectorial extension of Fourier ptychography to recover the absorption, phase and polarization properties of a sample at high-NA across a wide field-of-view. Our principle is validated by experimentally imaging quantitative test targets as well as a plant root and rabbit spinal cord cross-sections, with which we demonstrate the ability to record complex specimen birefringence over 10.4 mm2 field-of-view at 0.73um resolution. Our new Fourier ptychographic approach also enables the measurement and correction of polarization-dependent pupil aberrations. We hope this simplicity helps adapt joint polarization and phase imaging to a wider array of applications.
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