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Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues.

Spatial frequency domain imaging (SFDI) has witnessed very rapid growth over the last decade, owing to its unique capabilities for imaging optical properties and chromophores over a large field-of-view and in a rapid manner. We provide a comprehensive review of the principles of this imaging method as of 2019, review the modeling of light propagation in this domain, describe acquisition methods, provide an understanding of the various implementations and their practical limitations, and finally review applications that have been published in the literature. Importantly, we also introduce a group effort by several key actors in the field for the dissemination of SFDI, including publications, advice in hardware and implementations, and processing code, all freely available online.

We present a Monte Carlo (MC) method to determine depth-dependent probability distributions of photon visitation and detection for optical reflectance measurements performed in the spatial frequency domain (SFD). These distributions are formed using an MC simulation for radiative transport that utilizes a photon packet weighting procedure consistent with the two-dimensional spatial Fourier transform of the radiative transport equation. This method enables the development of quantitative metrics for SFD optical sampling depth in layered tissue and its dependence on both tissue optical properties and spatial frequency. We validate the computed depth-dependent probability distributions using SFD measurements in a layered phantom system with a highly scattering top layer of variable thickness supported by a highly absorbing base layer. We utilize our method to establish the spatial frequency-dependent optical sampling depth for a number of tissue types and also provide a general tool to determine such depths for tissues of arbitrary optical properties.

Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics – relative contrast enhancement factor (F), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

Imaging Cherenkov emission during radiotherapy permits real-time visualization of external beam delivery on superficial tissue. This signal is linear with absorbed dose in homogeneous media, indicating potential for quantitative dosimetry. In humans, the inherent heterogeneity of tissue optical properties (primarily from blood and skin pigment) distorts the linearity between detected Cherenkov signal and absorbed dose. We examine the potential to correct for superficial vasculature using spatial frequency domain imaging (SFDI) to map tissue optical properties for large fields of view. In phantoms, applying intensity corrections to simulate blood vessels improves Cherenkov image (CI) negative contrast by 24% for a vessel 1.9-mm-in diameter. In human trials, SFDI and CI are acquired for women undergoing whole breast radiotherapy. Applied corrections reduce heterogeneity due to vasculature within the sampling limits of the SFDI from a 22% difference as compared to the treatment plan, down to 6% in one region and from 14% down to 4% in another region. The optimal use for this combined imaging system approach is to correct for small heterogeneities such as superficial blood vessels or for interpatient variations in blood/melanin content such that the corrected CI more closely represents the surface dose delivered.

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

We introduce a method for quantitative hyperspectral optical imaging in the spatial frequency domain (hs-SFDI) to image tissue absorption (μa) and reduced scattering (μs′) parameters over a broad spectral range. The hs-SFDI utilizes principles of spatial scanning of the spectrally dispersed output of a supercontinuum laser that is sinusoidally projected onto the tissue using a digital micromirror device. A scientific complementary metal–oxide–semiconductor camera is used for capturing images that are demodulated and analyzed using SFDI computational models. The hs-SFDI performance is validated using tissue-simulating phantoms over a range of μa and μs′ values. Quantitative hs-SFDI images are obtained from an ex-vivo beef sample to spatially resolve concentrations of oxy-, deoxy-, and met-hemoglobin, as well as water and fat fractions. Our results demonstrate that the hs-SFDI can quantitatively image tissue optical properties with 1000 spectral bins in the 580- to 950-nm range over a wide, scalable field of view. With an average accuracy of 6.7% and 12.3% in μa and μs′, respectively, compared to conventional methods, hs-SFDI offers a promising approach for quantitative hyperspectral tissue optical imaging.

A method to correct for surface scattering in spatial frequency domain imaging (SFDI) is presented. The use of a modified analytical solution of the radiative transfer equation allows calculation of the reflectance and the phase of a rough semi-infinite geometry so that both spatial frequency domain reflectance and phase can be applied for precise retrieval of the bulk optical properties and the surface scattering. For validation of the method, phantoms with different surface roughness were produced. Contrarily, with the modified theory, it was possible to dramatically reduce systematic errors due to surface scattering. The evaluation of these measurements with the state-of-the-art theory and measuring modality, i.e., using crossed linear polarizers, reveals large errors in the determined optical properties, depending on the surface roughness, of up to ≈100  %  . These results were confirmed with SFDI measurements on a phantom that has a structured rough surface.

This paper presents a detection method for characterizing the bonded interface of O2 plasma activated silicon wafer direct bonding. The images, obtained by infrared imaging system, were analyzed by the software based on spatial domain and morphology methods. The spatial domain processing methods, including median filtering and Laplace operator, were applied to achieve de-noising and contrast enhancement. With optimized parameters of sharpening operator patterns, disk size, binarization threshold, morphological parameter A and B, the void contours were clear and convenient for segmentation, and the bonding rate was accurately calculated. Furthermore, the void characteristics with different sizes and distributions were also analyzed, and the detailed statistics of the void’s number and size are given. Moreover, the orthogonal experiment was designed and analyzed, indicating that O2 flow has the greatest influence on the bonding rate in comparison with activated time and power. With the optimized process parameters of activated power of 150 W, O2 flow of 100 sccm and time of 120 s, the testing results show that the bonding rate can reach 94.51% and the bonding strength is 12.32 MPa.