Tissue optical clearing and expanding solution for improved nuclear segmentation

Activation of adaptive immunity is a complex process coordinated at multiple levels in both time and the three-dimensional context of reactive lymph nodes (LNs). Although microscopy-based visualization of its spatiotemporal dynamics unravels complexities of developing immune response, such approach is highly limited by light-obstructing nature of tissue components. Recently, tissue optical clearing (TOC) techniques were established to bypass this obstacle and now allow to image and quantify the entire murine organs with cellular resolution. However, the spectrum of TOC is represented by wide variety of chemically distinct methods, each having certain advantages and disadvantages that were unsatisfactorily compared for suitability to LNs clearing. In this study, we have systematically tested 13 typical TOC techniques and assessed their impact on a number of critical factors such as LN transparency, imaging depth, change in size, compatibility with proteinaceous fluorophores, immunostaining, H&E staining, and light-sheet fluorescence microscopy. Based on the detailed data specific to TOC process of murine LNs, we provide a reliable reference for most suitable methods in an application-dependent manner.

The tissue optical clearing (TOC) technique could significantly improve the biomedical optical imaging depth, but most current investigations are limited to in vitro studies. For in vivo applications, the TOC method must provide a rapid treatment process, sufficient transparency, and safety for animals, which makes it more difficult. Recently developed innovative optical clearing methods for in vivo use show great potential for enhancing the contrast and resolution of laser speckle contrast imaging (LSCI) for blood flow monitoring. This paper gives an overview of recent progress in the use of TOC for vascular visualization with LSCI. First, the principle of TOC-induced improvement of LSCI and a quantitative analysis method for evaluating the improvement are described briefly. Second, the paper introduces transparent windows, including various skin windows and a cranial window, that permit LSCI to monitor dermal or cortical blood flow, respectively, with high resolution and contrast. Third, preliminary investigations of the safety of TOC demonstrate that the transparent skin window is switchable, which enables LSCI to repeatedly image blood flow. However, research on in vivo TOC is currently less advanced than that on in vitro TOC. Future work should focus on developing a highly effective, safe method and extending its applications.

Seeing deep and clear in thick scattering tissues using fluorescence imaging is vital for exploring biological phenomena. Although various speckle illumination techniques have been developed to customize speckles to improve imaging resolution and depth in scattering tissues, the lack of a guiding physical model and well‐defined mechanisms for maintaining customized speckle statistics during propagation in scattering media limits their ability to guarantee quality improvement throughout the scattering volume. Here, the speckle with the generalized stationary distribution that can maintain the customized intensity correlation in 3D space is discovered through the derivation and analysis of speckle correlation in Fresnel propagation. The illumination system's correlation transmission model mapping the phase field to speckle intensity correlation is established, enabling the customization of speckles with consistent and expected intensity correlation in 3D space over a depth range of several thousand micrometers. With the enhanced anti‐scattering fluorescence imaging ability of the customized speckle, a more than twofold improvement in 3D average resolution and a twofold improvement in imaging depth have been achieved compared to random speckle under strong optical scattering conditions like those in mouse brain tissue. Fluorescence imaging through millimeter‐thick scattering layers has also been achieved, demonstrating the advantages of high‐resolution volumetric scattering imaging.

Accuracy and precision of segmentation and quantification of wrist bone microarchitecture using photon-counting computed tomography ex vivo.

Quantitative photoacoustic tomography (QPAT) is a hybrid imaging modality that simultaneously reconstructs absorption and scattering coefficients with multi-source or multi-wavelength setting. In contrast with PAT, QPAT eliminates the artifacts due to photon transfer in depth and characterizes the intrinsic biological attributes. However, data acquisition for QPAT is time consuming because for each optical source (or wavelength), a 360-degree acoustic measurement on the boundary of imaging region is required. In this work, we investigate a novel limited-view multi-source (LVMS) QPAT scheme and reconstruction algorithm based on coupled opto-acousto model. A unique setting that binds optical source and acoustic detector together is presented. Under each illumination, only a limited-view measurement is acquired, which is incomplete for PAT reconstruction but sufficient for direct QPAT reconstruction; then the source and detector rotate to next data acquisition position synchronously. In terms of reconstruction, a sparsity-regularized formulation based on total variation norm is adopted here and optimized through alternating direction method of multipliers with LBFGS solver, during which the adjoint method is used for rapid computation of numerical gradient of objective function. However, the aperture effect i.e. anisotropic angular sensitivity of a finite-dimension transducer would cause temporal distortion of receiving acoustic signal and further resolution reduction. Therefore, we numerically integrate ideal PAT system spatial impulse response (SIR) with the angular response from circular transducer to improve the modeling accuracy. In summary, the proposed LVMS-QPAT has the potential to shorten the data acquisition time, enhance system SNR and improve image resolution.

Aberrations in optical microscopy reduce image resolution and contrast, and can limit imaging depth when focusing into biological samples. Static correction of aberrations may be achieved through appropriate lens design, but this approach does not offer the flexibility of simultaneously correcting aberrations for all imaging depths, nor the adaptability to correct for sample-specific aberrations for high-quality tomographic optical imaging. Incorporation of adaptive optics (AO) methods have demonstrated considerable improvement in optical image contrast and resolution in noninterferometric microscopy techniques, as well as in optical coherence tomography. Here we present a method to correct aberrations in a tomogram rather than the beam of a broadband optical interferometry system. Based on Fourier optics principles, we correct aberrations of a virtual pupil using Zernike polynomials. When used in conjunction with the computed imaging method interferometric synthetic aperture microscopy, this computational AO enables object reconstruction (within the single scattering limit) with ideal focal-plane resolution at all depths. Tomographic reconstructions of tissue phantoms containing subresolution titanium-dioxide particles and of ex vivo rat lung tissue demonstrate aberration correction in datasets acquired with a highly astigmatic illumination beam. These results also demonstrate that imaging with an aberrated astigmatic beam provides the advantage of a more uniform depth-dependent signal compared to imaging with a standard gaussian beam. With further work, computational AO could enable the replacement of complicated and expensive optical hardware components with algorithms implemented on a standard desktop computer, making high-resolution 3D interferometric tomography accessible to a wider group of users and nonspecialists.

Maximizing the Effectiveness of Integrated Reservoir Studies: Practical Approaches to Improving the Process and Results

In vivo imaging of small animals is of wide interest to the biomedical community studying biological disease and developmental processes. However, optical imaging deep in tissue is severely limited by light scattering, posing restrictions on the imaging depth, image contrast, and spatial resolution. We demonstrate optical coherence projection tomography (OCPT) as a fast three-dimensional optical imaging technique for ballistic, non-scattered light, deep-tissue imaging. OCPT is based on a novel scanning transmission sample arm to rapidly measure ballistic light projections of amplitude and phase through thick biological tissues. We demonstrate the strength of OCPT by imaging an adult zebrafish in a total volume of 1000 m m 3 acquired in 24 min. We achieve an unprecedented imaging depth of 4 mm in biological tissue without using optical clearing (up to 27 mean free paths of photon transport). A new way of analyzing optical tomographic imaging depth is demonstrated and applied to OCPT. It shows that the strong light scattering suppression in OCPT is pivotal to reach the SNR limited imaging depth. OCPT allows for a full quantitative assessment of tissue parameters, which is demonstrated by quantifying the attenuation coefficient, refractive index, surface area, and volume of various organs deep inside the zebrafish. Our work opens up the way for longitudinal in vivo small animal studies from the larval to the adult stages.

Global measurements of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds are critically needed to reduce uncertainties in global weather and climate models. To address this need, the conically scanning Tropospheric Water and Cloud ICE (TWICE) millimeter and submillimeter radiometer instrument is being developed. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed to control TWICE data acquisition and other subsystems. The C&DH prototype board meets functional, noise and size, weight and power (SWaP) requirements for deployment in a 6U-class satellite. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, all of the components have been tested for radiation tolerance in low-Earth orbit.

Preface. Acknowledgments. Introduction. 1. Virtual Reconstruction. 1.1 A Virtual Reality Contest. 1.2 Virtual Reconstruction. 1.3 Computer-Assisted Paleontology. 1.3.1 Data Acquisition. 1.3.2 Data Segmentation and Three-Dimensional Reconstruction. 1.3.3 Virtual Fossil Reconstruction. 1.3.4 From Virtual Reality to Real Virtuality. 1.3.5 Databases and Morphometry. 1.3.6 Virtual Reconstruction in Space and Time. 1.4 Computer-Assisted Surgery. 1.5 Further Reading. 2. Data Representation. 2.1 World Food on a Chessboard. 2.2 Facts About Data to Get Data About Facts. 2.2.1 Analog and Digital Data. 2.2.2 Bits, Bytes, and Words. 2.2.3 Characters, Numbers, Pixels, and Voxels. 2.2.4 Representing Gray Tones and Colors. 2.2.5 Data Compression. 2.2.6 Some Common Image File Formats. 2.2.7 Implicit Versus Explicit Representation of Object Data. 2.2.8 Modeling Three-Dimensional Objects. 2.3 A Taxonomy of Biomedical Data. 2.3.1 Perspectives on Data. 2.3.2 Volume Data. 2.3.3 Surface Data. 2.3.4 Landmark Data. 2.3.5 Extent-Based Data. 2.3.6 Relational Data. 2.4 Further Reading. 3. Data Acquisition. 3.1 Data and the Physical World. 3.2 Vision and Photography as Data Acquisition: Performance Considerations. 3.3 Computed Tomography. 3.3.1 Frau RAntgena s Wedding Ring. 3.3.2 Radiographic Projections. 3.3.3 Reconstructing CT Images. 3.3.4 CT Scanning: Technical Considerations. 3.3.5 Limitations of CT Data Acquisition. 3.3.6 Slice-to-Slice, Helical, and Multislice CT. 3.3.7 Industrial and Micro Computed Tomography. 3.3.8 Three-Dimensional Data Acquisition with a Medical Scanner. 3.4 Magnetic Resonance Imaging. 3.5 Surface Scanners. 3.6 3D Digitizers. 3.7 Further Reading. 4. Image Data Processing. 4.1 Recovering Objects from Images. 4.2 Converting a CT Image into a Screen Image. 4.3 Filtering Images. 4.3.1 Coffee and Kernels. 4.3.2 Convolution and Fourier Analysis. 4.3.3 Statistical Filters. 4.3.4 Edge Detection Filters. 4.4 Extracting Isosurfaces. 4.4.1 Determining Boundaries in CT Images. 4.4.2 From Edges to Isocontours and Isosurfaces. 4.5 Interactive Segmentation. 4.6 Further Reading. 5. Visualization and Interaction. 5.1 Visualizing Data in Two and More Dimensions. 5.2 Interaction with Virtual Worlds. 5.3 The Graphics Rendering Pipeline. 5.4 Setting Up a Virtual Environment. 5.4.1 Object Materials, Lighting, and Shading. 5.4.2 Setting Up the Camera. 5.4.3 Object Manipulation and Interaction. 5.5 Volume Rendering. 5.6 Further Reading. 6. Virtual Fossil Reconstruction. 6.1 A Baroque Puzzle. 6.2 Principles of Reconstruction. 6.3 Physical and Virtual Reconstruction. 6.4 Preparing and Restoring Fossils on the Computer Screen. 6.5 Reconstructing Fossil Morphologies. 6.5.1 Recovering Implicit Anatomic Information. 6.5.2 Combining Computer Graphics and Anatomy: The Globe Paradigm. 6.5.3 Inferring Missing Information. 6.5.4 Interpolation and Extrapolation. 6.6 Correcting Fossil Deformation. 6.6.1 Taphonomic Scenarios. 6.6.2 Correcting Plastic Deformation. 6.7 Validating Virtual Reconstructions. 6.8 Paleodiagnostics and Paleoforensics. 6.9 Inferring Soft Tissue Structures. 6.9.1 Motivation. 6.9.2 Fossil Soft Tissue Reconstruction: Classic and Virtual Approaches. 6.9.3 What Shall Be Reconstructed? 6.9.4 Soft Tissue Reconstruction and Measurement. 6.10 Virtual Surgery: A Paleoanthropologista s Eye View. 6.10.1 Motivation. 6.10.2 Virtual Planning and Simulation of Surgical Interventions. 6.10.3 Custom Implant Design. 6.10.4 Soft Tissue Reconstruction. 6.11 Further Reading. 7. From Virtual Reality to Real Virtuality. 7.1 Reifying Virtual Objects. 7.2 Principles of Rapid Prototyping. 7.3 Combining Virtual Reality and Real Virtuality. 7.4 Further Reading. 8. Morphometric Analysis. 8.1 Morphometry as Reconstruction. 8.2 Morphometry and Geometry. 8.2.1 The Role of Geometry. 8.2.2 The Role of Size and Shape. 8.2.3 Multivariate Morphometry. 8.2.4 Principal Components Analysis and Dimension Reduction. 8.2.5 Classic Multivariate Morphometry: Geometry Lost. 8.2.6 Geometric Morphometrics: Geometry Recovered. 8.3 Shape Space Analysis. 8.3.1 From Da Arcy Thompson to Kendall. 8.3.2 The Workflow of Shape Space Analysis. 8.3.3 Determining a Reference Shape. 8.3.4 Analyzing Data in Shape Space. 8.3.5 Visualizing Patterns of Shape Difference and Shape Change. 8.3.5 Visualizing Patterns of Shape Difference and Shape Change. 8.4 Euclidean Distance Matrix Analysis. 8.4.1 In Search of the Golden Mean. 8.4.2 Exploring Form Variability with EDMA. 8.5 Outline Analysis. 8.6 A Comparison of Geometric Morphometric Methods. 8.6.1 Criteria for Comparison. 8.6.2 From Pattern to Process. 8.7 Exploring Morphometric Patterns. 8.8 Further Reading. Appendix A. Image Data Acquisition Systems: Performance Considerations. Appendix B. Parameters Influencing the Quality of CT Image Data. Appendix C. CT Scanning of Fossil Specimens and Recent Skeletal Specimens: How to Proceed? Appendix D. Object Manipulation in Virtual Space. Appendix E. A Parsimonious Approach to Correction of Taphonomic Deformation. Appendix F. Morphometry. References. Index.

Improvement of low-frequency ultrasonic image quality using a enhanced convolutional neural network

Quantitative analysis can be used in combination with fluorescence microscopy. Although the human eye is able to obtain good qualitative results, when analyzing the spatial organization of telomeres in interphase nuclei, there is a need for quantitative results based on image analysis. We developed a tool for analyzing three-dimensional images of telomeres stained by fluorescence in situ hybridization in interphase nuclei with DNA counterstained with 4',6-diamidino-2-phenylindole. After deconvolution of the image, we segmented individual telomeres. From the location of the telomeres we derived a distribution parameter rhoT, which indicated whether the telomeres were in a disk (rhoT >> 1) or not (rhoT approximately 1). We sorted mouse lymphocyte nuclei and measured rhoT. We also performed a bromodeoxyuridine synchronous cell sorting experiment on live cells and measured rhoT at several instances. Measuring rhoT for nuclei in G0/G1, S, and G2 produced 1.4 +/- 0.1, 1.5 +/- 0.2, and 14 +/- 2, respectively, showing a significant difference between G2 and G0/G1 or S. For the bromodeoxyuridine synchronous cell sorting experiment, we found a cell cycle dependency of rhoT and a correlation between rhoT and an observer. In this study we present a quantitative method to characterize the organization of telomeres using three-dimensional imaging, image processing, and image analysis.

This study evaluated accuracy of the reference frequency estimation (RFE) technique for attenuation estimation using focused ultrasound (US) imaging and varying time gain compensation (TGC) settings. Experiments were conducted using a bilayer tissue-mimicking phantom with known attenuation coefficients of -0.70 and -0.95 dB/cm/MHz. Multiple acquisitions were performed by adjusting the transmit focal depth from to 1.5, 2.5, or 3.5 cm using a US scanner (Vantage NXT, Verasonics Inc) equipped with a linear array L11-5v transducer. Two types of TGC settings were applied during data acquisition. One comprised a constant gain (denoted TGC 1) at axial depth and the second involved a progressive increasing gain setting at US image depth (denoted TGC 2). After US data acquisition, the RFE method was applied across multiple lateral zones. The relative change of the localized first-order derivative of power spectral density components was assessed in relation to the one from multiple reference depth to estimate the US attenuation coefficient. At each focal depth, the mean ± SD of the RFE-based results obtained using TGC 1 within the pre-focal region were compared to that acquired using the TGC 2 setting. Lastly, RFE performance was compared with a traditional envelope-based US attenuation estimation technique. The RFE-based estimations from the first layer (-0.70 dB/cm/MHz) for TGC 1 (vs TGC 2) settings were -0.67 ± 0.04 (-0.65 ± 0.04), -0.67 ± 0.09 (-0.68 ± 0.09), and -0.75 ± 0.01 (-0.76 ± 0.01) dB/cm/MHz at focal imaging depths of 1.5, 2.5, or 3.5 cm, respectively. Similarly, estimates from the second layer (-0.95 dB/cm/MHz) were also accurately evaluated and insensitive to TGC settings: -0.97 ± 0.14 (-0.96 ± 0.13), -0.86 ± 0.05 (-0.86 ± 0.05), and -0.90 ± 0.01 (-0.93 ± 0.01). RFE-based estimates from the pre-focal region were robust and closer to true values than traditional envelope-based US attenuation estimates.

Multifocal multiphoton microscopy (MMM) enhances imaging speed by parallelization. It is not well understood why the imaging depth of MMM is significantly shorter than conventional single-focus multiphoton microscopy (SMM). In this report, we show that the need for spatially resolved detectors in MMM results in a system that is more sensitive to the scattering of emission photons with reduced imaging depth. For imaging depths down to twice the scattering mean free path length of emission photons (2xl (s) (em)), the emission point spread function (PSF(em)) is found to consist of a narrow, diffraction limited distribution from ballistic emission photons and a broad, relatively low amplitude distribution from scattered photons. Since the scattered photon distribution is approximately 100 times wider than that of the unscattered photons at 2xl (s) (em), image contrast and depth are degraded without compromising resolution. To overcome the imaging depth limitation of MMM, we present a new design that replaces CCD cameras with multi-anode photomultiplier tubes (MAPMTs) allowing more efficient collection of scattered emission photons. We demonstrate that MAPMT-based MMM has imaging depth comparable to SMM with equivalent sensitivity by imaging tissue phantoms, ex vivo human skin specimens based on endogenous fluorophores, and green fluorescent protein (GFP) expressing neurons in mouse brain slices.

Wide field fluorescence microscopy is the most commonly employed fluorescence imaging modality. However, a major drawback of wide field imaging is the very limited imaging depth in scattering samples. By experimentally varying the control of illumination, we found that the optimized illumination profile can lead to large contrast improvement for imaging at a depth beyond four scattering path lengths. At such imaging depth, we found that the achieved image signal-to-noise ratio can rival that of confocal measurement. As the employed illumination control is very simple, the method can be broadly applied to a wide variety of wide field fluorescence imaging systems.