High-resolution Data Acquisition Research Articles

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

Noise attenuation in distributed acoustic sensing data using a guided unsupervised deep learning network

Distributed acoustic sensing (DAS) is a promising technology introducing a new paradigm in the acquisition of high-resolution seismic data. However, DAS data often show weak signals compared with the background noise, especially in challenging installation environments. In this study, we develop a new approach to denoise DAS data that leverages an unsupervised deep learning (DL) model, eliminating the need for labeled training data. The input DAS data undergo band-pass filtering to eliminate high-frequency content. Subsequently, a continuous wavelet transform (CWT) is performed, and the finest scale is used to guide the DL model in reconstructing the DAS signal. First, we extract 2D patches from the band-pass filtered data and the CWT scale of the data. Then, these patches are converted using an unrolling mechanism into 1D vectors to form the input of the DL model. A self-attention layer is included in each layer to extract the spatial relation between the band-pass filtered data and the CWT scale. Through an iterative process, the DL model tunes its parameters to suppress DAS noise, with the band-pass filtered data serving as the target for the network. The denoising performance of our framework is validated using field examples from the San Andreas Fault Observatory at Depth and Frontier Observatory for Research in Geothermal Energy data sets, where the data are recorded by a fiber-optic cable. Comparative analyses against three benchmark methods reveal the robust denoising performance of our framework.

Cryo-Electron Microscopy in the Study of Antiviral Innate Immunity.

Cryo-electron microscopy is a powerful methodology in structural biology and has been broadly used in high-resolution structure determination for challenging samples, which are not readily available for traditional techniques. In particular, the strength of super macro-complexes and the lack of a need for crystals for cryo-EM make this technique feasible for the structural study of complexes involved in antiviral innate immunity. This chapter presents detailed information and experimental procedures of Cryo-EM for determining the structures of the complexes using STING as an example. The procedures included a sample quality check, high-resolution data acquisition, and image processing for Cryo-EM 3D structure determination.

Rapid-scan broadband frequency-domain terahertz spectroscopy via dynamic optical phase lock

Frequency-domain terahertz (THz) spectroscopy using photomixing devices has unique advantages such as high dynamic range and high spectral resolution. Thus, many applications for solid-state and gas-phase spectroscopy have been proposed. In this study, we developed a feedback controlled technique to dynamically compensate for the optical phase accompanied by frequency sweep, enabling both fast and high-resolution data acquisition across a wide frequency region. From gas-phase THz spectroscopy measurements of dilute acetonitrile gas in a wide frequency range up to 1.1 THz, fine structures with linewidths less than 10 MHz were clearly resolved, while the data acquisition rate was improved by two orders compared to the previously reported value.

N-DEPTH: Neural Depth Encoding for Compression-Resilient 3D Video Streaming

Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling this data problem thus far have been image-based depth encoding schemes that leverage modern image and video codecs. To our knowledge, no published work has directly optimized the end-to-end losses of a depth encoding scheme sandwiched around a lossy image compression codec. We present N-DEPTH, a compression-resilient neural depth encoding method that leverages deep learning to efficiently encode depth maps into 24-bit RGB representations that minimize end-to-end depth reconstruction errors when compressed with JPEG. N-DEPTH’s learned robustness to lossy compression expands to video codecs as well. Compared to an existing state-of-the-art encoding method, N-DEPTH achieves smaller file sizes and lower errors across a large range of compression qualities, in both image (JPEG) and video (H.264) formats. For example, reconstructions from N-DEPTH encodings stored with JPEG had dramatically lower error while still offering 29.8%-smaller file sizes. When H.264 video was used to target a 10 Mbps bit rate, N-DEPTH reconstructions had 85.1%-lower root mean square error (RMSE) and 15.3%-lower mean absolute error (MAE). Overall, our method offers an efficient and robust solution for emerging 3D streaming and 3D telepresence applications, enabling high-quality 3D depth data storage and transmission.

Recent advances in vertical temperature profiler instrumentation and flux estimation methods facilitate groundwater – Surface water exchange studies in environments with strong discharge zones

Groundwater fluxes to many surface water systems are spatially heterogeneous with discharge focused into discrete, high-flux zones. Quantifying fluxes in these preferential discharge zones is critical to a range of surface water habitat and water quality processes, but characterization can be difficult due to short-scale spatial and temporal variability. Passive heat-as-a-tracer methods employing vertical temperature profiler (VTP) data can provide the necessary spatial and temporal resolution, but upward fluid flow strongly attenuates the thermal signals used for estimating fluxes. In preferential discharge zones it becomes difficult to measure the signals in the subsurface and the flux parameter can become insensitive in the analysis models, leading to large uncertainties. We use data from a high-flux site of contaminant-loaded groundwater discharge to the Quashnet River on Cape Cod, Massachusetts, USA, to demonstrate how recent advances in VTP instrumentation that allow for the acquisition of high-resolution (0.001 °C) temperature data at short (1 cm) offsets near the ground surface, combined with advances in flux estimation methods that exploit the information content of the high-resolution data, facilitate heat-as-a-tracer approaches for characterizing groundwater-surface water exchanges and make it possible to obtain accurate and statistically robust results in a preferential discharge zone with a specific discharge of ∼1 m/d.

Targeted and untargeted screening for impurities in losartan tablets marketed in Germany by means of liquid chromatography/high resolution mass spectrometry

Technical advances in the field of quality analysis allow an increasingly deeper look into the impurity profile of drugs. The ability to detect unexpected impurities in addition to known impurities ensures the supply of high-quality drugs and can prevent recalls due to the detection of harmful unexpected impurities, as has happened recently with the N-nitrosamine and azido impurities in losartan (LOS) drug products. In the present study, the LC-MS/HRMS approach described by Backer et al. was applied to an even more complex system, being the investigation of 35 LOS drug products and combination preparations purchased in 2018 and 2022 in German pharmacies. The film-coated tablets were analysed by means of four LC-MS/HRMS method variants. For the separation a Zorbax RR StableBond C18 column (3.0 ×100 mm, particle size of 3.5 µm, pore size of 80 Å), a gradient elution and for mass spectrometric detection a qTOF mass spectrometer with electrospray ionization in positive and negative mode was used. An information-dependent acquisition method was applied for the acquisition of high-resolution mass spectrometry data. The combination of an untargeted and a targeted screening approach revealed the finding of eight impurities in total. Beside the five LOS related compounds, LOS impurity F, J, K, L, M, and related compound D from amlodipine besilate, LOS azide and an unknown derivative thereof were detected. Identification and structure elucidation, respectively, were successfully performed using in silico fragmentation. Differences in the impurity profiles of drug products from 2018 and 2022 could be observed. This study shows that broad screening approaches like this are applicable to the analysis of drug products and can be an important enhancement of the quality assurance of medicinal products.

Evaluating the accuracy of cerebrovascular computational fluid dynamics modeling through time-resolved experimental validation

Accurate modeling of cerebral hemodynamics is crucial for better understanding the hemodynamics of stroke, for which computational fluid dynamics (CFD) modeling is a viable tool to obtain information. However, a comprehensive study on the accuracy of cerebrovascular CFD models including both transient arterial pressures and flows does not exist. This study systematically assessed the accuracy of different outlet boundary conditions (BCs) comparing CFD modeling and an in-vitro experiment. The experimental setup consisted of an anatomical cerebrovascular phantom and high-resolution flow and pressure data acquisition. The CFD model of the same cerebrovascular geometry comprised five sets of stationary and transient BCs including established techniques and a novel BC, the phase modulation approach. The experiment produced physiological hemodynamics consistent with reported clinical results for total cerebral blood flow, inlet pressure, flow distribution, and flow pulsatility indices (PI). The in-silico model instead yielded time-dependent deviations between 19–66% for flows and 6–26% for pressures. For cerebrovascular CFD modeling, it is recommended to avoid stationary outlet pressure BCs, which caused the highest deviations. The Windkessel and the phase modulation BCs provided realistic flow PI values and cerebrovascular pressures, respectively. However, this study shows that the accuracy of current cerebrovascular CFD models is limited.

Cryo-EM advances in GPCR structure determination.

G-protein-coupled receptors (GPCRs) constitute a prominent superfamily in humans and are categorized into six classes (A-F) that play indispensable roles in cellular communication and therapeutics. Nonetheless, their structural comprehension has been limited by challenges in high-resolution data acquisition. This review highlights the transformative impact of cryogenic electron microscopy (cryo-EM) on the structural determinations of GPCR-G-protein complexes. Specific technologies, such as nanobodies and mini-G-proteins, stabilize complexes and facilitate structural determination. We discuss the structural alterations upon receptor activation in different GPCR classes, revealing their diverse mechanisms. This review highlights the robust foundation for comprehending GPCR function and pave the way for future breakthroughs in drug discovery and therapeutic targeting.

Evolution characteristics of deflagration wave system and flame acceleration effect of combustible gas in variable-caliber pipelines

To understand the deflagration phenomenon of combustible gas in gas pipelines and improve the design of pipeline structural strength, this work adopted the self-developed experimental platform with a length of 12 m and a diameter of 90 mm to simulate the transportation process of urban variable-caliber gas pipelines. Experiments were conducted in a 9.5% methane/air premixed gas environment using a high-voltage ignition system, high-resolution data acquisition system, multiple piezoelectric sensors, fiber-optic flame speed sensors, ultra-high-speed laser schlieren system, and laser texture technology. The effects of blocking ratios of 0.4, 0.6, and 0.8 on the evolution characteristics of the deflagration wave system and the acceleration effect of combustible gas flames were comprehensively investigated. The blocking ratio was varied by placing an annular block somewhere along the pipeline. The results reveal that: (1) As the blocking ratio increased from 0 to 0.8, the pressure peak of the flow field did not appear near the ignition location. Comparing different blocking ratios, 0.4 blocking ratio had the maximum pressure, reaching 302 kPa, 0.6 blocking ratio reached the pressure peak position earliest, and the maximum pressure occurred at 83 times the cross-sectional diameter; (2) The blocking ratio of 0.6 had the most obvious effect on the flame acceleration, it could accelerate nearly three times within a range of 2 m, with a peak velocity of 130 m/s and an acceleration of 829 m/s2; (3) By identifying the laser schlieren image, the flame acceleration process and the coupling relationship between the reflected wave and the flame were able to be clearly understood, and the obtained flame speed was consistent with the measurement speed of the fiber optic sensors. The research results can provide a basis for urban variable-caliber gas pipeline accident prevention and structural design, and provide new ideas for re-understanding the gas deflagration mechanism.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

An Optimized Inversion Method for Hyperspectral Image Fusion Based on a Hue–Intensity–Saturation, Wavelet, and Trust-Region Conjugate Gradient Method

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application.

Peptide-Based Mass Spectrometry for the Investigation of Protein Complexes.

In the last two decades, biological mass spectrometry has become the gold standard for the identification of proteins in biological samples. The technological advancement of mass spectrometers and the development of methods for ionization, gas phase transfer, peptide fragmentation as well as for acquisition of high-resolution mass spectrometric data marked the success of the technique. This chapter introduces peptide-based mass spectrometry as a tool for the investigation of protein complexes. It provides an overview of the main steps for sample preparation starting from protein fractionation, reduction, alkylation and focus on the final step of protein digestion. The basic concepts of biological mass spectrometry as well as details about instrumental analysis and data acquisition are described. Finally, the most common methods for data analysis and sequence determination are summarized with an emphasis on its application to protein-protein complexes.

Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding.

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

Novel Compact Polarized Martian Wind Imaging Interferometer

The Mars Atmospheric Wind Imaging Interferometer offers several advantages, notably its high throughput, enabling the acquisition of precise and high vertical resolution data on the temperature and wind fields in the Martian atmosphere. Considering the current absence of such an Interferometer, this paper introduces a novel Mars wind field imaging interferometer. In analyzing the photochemical model of O2 (a1Δg) 1.27 μm molecular airglow radiation in the Martian atmosphere and considering the impact of instrument signal-to-noise ratio (SNR), we have chosen an optical path difference (OPD) of 8.6 cm for the interferometer. The all-solid-state polarized wind imaging interferometer is miniaturized by incorporating two arm glasses as the compensation medium in its construction, achieving the effects of field-widening and temperature compensation. Additionally, an F-P Etalon is designed to selectively filter the desired three spectral lines of O2 dayglow, and its effect is evaluated through simulations. The accuracy of the proposed compact Mars polarized wind imaging interferometer for detecting Mars’ wind field and temperature field has been validated through rigorous theoretical derivation and comprehensive computer simulations. The interferometer boasts several advantages, including its compact and small size, static stability, minimal stray light, and absence of moving parts. It establishes the theoretical, technological, and instrumental engineering foundations for future simultaneous static measurement of Martian global atmospheric wind fields, temperature fields, and ozone concentrations from spacecraft, thereby significantly contributing to the dataset for investigating Martian atmospheric dynamics.

Developing a distributed acoustic sensing seismic land streamer: Concept and validation

Motivated by existing cabled seismic land-streamer designs, we develop a distributed acoustic sensing (DAS) land-streamer system for high-resolution near-surface seismic data acquisition. The system consists of a DAS interrogator unit (IU), a fiber-optic cable attached beneath a fire-hose assembly for environmental isolation and improved fiber-ground coupling, and a vehicle-mounted accelerated weight-drop source. The DAS land streamer is easily deployed and towed along the ground surface, allowing for spatially dense data acquisition. We report two field tests with the developed hardware to evaluate the DAS land-streamer performance. The first test investigates the effects of hose weight on the surface-deployed fiber and indicates that this approach improves fiber-ground coupling and leads to an improved signal-to-noise ratio (S/N). The second test demonstrates that the DAS land streamer records waveforms with similar phase and moveouts as those recorded by horizontal geophones but offers a higher native spatial density advantage than standard geophone arrays, leading to spatially dense waveforms, improved S/N after postprocessing, and superior surface-wave acquisition array mobility. Our findings suggest that a DAS land streamer is a promising alternative to traditional geophone-based surveys and may offer several advantages, such as faster survey acquisition speed and lower field costs due to reduced acquisition hardware requirements. However, methodological limitations include recording a single horizontal ground-motion component, a dependence on favorable fiber-ground coupling conditions, and the upfront cost of IU procurement. A DAS land streamer may be useful in numerous subsurface applications, such as (pseudo) 1D multichannel analysis of surface waves or 2D surface-wave inversion for S-wave velocity model estimation for geophysical, geologic, geotechnical, and environmental investigations.

High-Resolution Data Acquisition and Joint Source-Channel Coding in Underwater IoT

Reliable and persistent water monitoring is a challenging problem in smart Underwater Internet of Things (UW IoT) due to its harsh, unexplored, and unpredictable nature. Given the need for high-resolution spatio-temporal sensing in such environments, traditional digital sensors are not suitable due to their high cost, high power consumption, and non-biodegradable nature. Further, reliable and low latency communication techniques that avoid data packet retransmissions, if the feedback is available, are crucial for reconstructing the phenomenon being monitored in a timely manner at the fusion center, such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that enable persistent sensing and continually transmitting data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing analog Joint Source-Channel Coding (JSCC), a low-complexity compression-communication technique, using biodegradable Field Effect Transistors (FETs). We then propose a correlation-aware Hybrid Automatic Repeat Request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages JSCC and the redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus saving energy and time.

Structural and Tectonic Evolution of the Porgera Gold Mine; Highlands of Papua New Guinea

The Porgera Transfer Zone (PTZ) is a major crustal and probably lithospheric structure across Papua New Guinea recording >50 km offset of ophiolites and very different patterns of geology and topography on either side. In the Late Jurassic, the PTZ probably separated oceanic crust and thick Jurassic Om shales to the west from a continental promontory to the east. During the Late Miocene to Recent orogenesis, the differential compression of these features is interpreted to have created a dextral strike slip fault across the fold belt with pull-apart basins at sites of fault relays. This facilitated the ascent of intrusions and mineralization at Porgera. The acquisition of high-resolution LIDAR data semi-regionally around the Porgera Gold Mine greatly improved interpretation of the regional geology and particularly the recognition of normal faults. By correlating with sparse dip data and paly-dated samples, it was possible to create stratigraphic sections and interpret structural cross-sections using the LIDAR data. As the area involved strike–slip offsets, it was important to construct sections in multiple orientations in order to interpret the 3D geology. Both dips and fault orientation could be directly inferred from the LIDAR data such that sections could be constructed orthogonally to them. A balanced, restored and forward-modelled cross-section illustrates the interaction between thrust faults and normal faults during compression and that it was synchronous with the development of a pull-apart basin. A semi-regional 3D geological model, which was developed mainly from the LIDAR data, supports the hypothesis of inversion of the thick Om beds to the west before or during compression of the continental promontory to the east resulting in dextral strike–slip offsets across the PTZ. A jog or relay in the faults occurred and caused a pull-apart collapse basin to develop in the area of the Porgera mine. Similar pull-apart graben, or negative flower structures, were detected nearby and may be areas for future exploration.

Highly coherent hybrid dual-comb spectrometer.

Dual comb spectroscopy (DCS) is a broadband technique offering high resolution and fast data acquisition. Current state-of-the-art designs are based on a pair of fiber or solid-state lasers, which allow broadband spectroscopy but require a complicated stabilization setup. Semiconductor lasers are tunable, cost-effective, and easily integrable while limited by a narrow bandwidth. This motivates a hybrid design combining the advantages of both systems. However, establishing sufficiently long mutual coherence time remains challenging. This work describes a hybrid dual-comb spectrometer comprising a broadband fiber laser (FC) and an actively mode-locked semiconductor laser (MLL) with a narrow but tunable spectrum. A high mutual coherence time of around 100 seconds has been achieved by injection locking the MLL to a continuous laser (CW), which is locked on a single line of the FC. We have also devised a method to directly stabilize the entire spectrum of FC to a high finesse cavity. This results in a long term stability of 5 × 10-12 at 1 second and 5 × 10-14 at 350 seconds. Additionally, we have addressed the effect of cavity dispersion on the locking quality, which is important for broadband comb lasers.

SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data.

Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions. Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features. Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes. Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.