High Spatial Resolution Images Research Articles

Triple disentangled network with dual attention for remote sensing image fusion

Remote sensing applications, such as detection and recognition of objects, need a large number of high spatial resolution (HR) images. As a feasible technique, image fusion is considered to produce HR images. However, most of remote sensing image fusion methods based on deep neural network (DNN) are mainly limited by the extraction of spatial and spectral features. These methods ignore the redundancy among these features, which causes spatial and spectral distortions in the fused image. In this paper, we propose a novel image fusion method based on a triple disentangled network (TDNet) with dual attention to reduce the redundancy in the extracted features. In the proposed method, it is assumed that the information in panchromatic (PAN) and low spatial resolution multispectral (LRMS) images can be encoded as the spatial, spectral, and common features. Specifically, we construct a triple-stream network to extract these features. To efficiently model the spatial and spectral information in PAN and LRMS images, local–global attention and interdependency attention are designed and integrated into the network. Then, the redundancy among these features is reduced by disentangled learning, in which these features are recombined to reconstruct the PAN and LRMS images. Besides, these features should complement each other. So, we utilize the maximal coding rate reduction to balance the redundancy and complementarity among them. Finally, all features are recombined to synthesize the high spatial resolution multispectral image. The experimental results demonstrate that the proposed TDNet has a superior performance in terms of qualitative and quantitative evaluations. The code link is https://github.com/RSMagneto/TDNet.

Attention in Attention for Hyperspectral With High Spatial Resolution (H) Image Classification

Attention in Attention for Hyperspectral With High Spatial Resolution (H) Image Classification<sup />

Computed Tomography of Transcatheter Aortic Valve Replacement-Related Complications

Transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of severe aortic stenosis, with an impressive reduction in mortality and complication rates over the last decade, becoming a safer alternative in patients at high/moderate risk for surgical treatment. Complications are rare but can be life-threatening; thus, cardiac imagers must be familiar with their imaging findings. TAVR-related complications often require multimodality imaging, although ECG-gated cardiac computed tomography (CCT) is the technique of choice, given its high spatial resolution and multiplanar image reconstruction. This review will discuss the imaging findings of the most important post-TAVR complications and novel preventive measures.

Nitrogen-vacancy center magnetic imaging of Fe3O4 nanoparticles inside the gastrointestinal tract of Drosophila melanogaster.

Widefield magnetometry based on nitrogen-vacancy centers enables high spatial resolution imaging of magnetic field distributions without a need for spatial scanning. In this work, we show nitrogen-vacancy center magnetic imaging of Fe3O4 nanoparticles within the gastrointestinal tract of Drosophila melanogaster larvae. Vector magnetic field imaging based on optically detected magnetic resonance is carried out on dissected larvae intestine organs containing accumulations of externally loaded magnetic nanoparticles. The distribution of the magnetic nanoparticles within the tissue can be clearly deduced from the magnetic stray field measurements. Spatially resolved magnetic imaging requires the nitrogen-vacancy centers to be very close to the sample making the technique particularly interesting for thin tissue samples. This study is a proof of principle showing the capability of nitrogen-vacancy center magnetometry as a technique to detect magnetic nanoparticle distributions in Drosophila melanogaster larvae that can be extended to other biological systems.

TauSTED super-resolution imaging of labile iron in primary hippocampal neurons.

Iron dyshomeostasis is involved in many neurological disorders, particularly neurodegenerative diseases where iron accumulates in various brain regions. Identifying mechanisms of iron transport in the brain is crucial for understanding the role of iron in healthy and pathological states. In neurons, it has been suggested that iron can be transported by the axon to different brain regions in the form of labile iron; a pool of reactive and exchangeable intracellular iron. Here we report a novel approach to imaging labile ferrous iron, Fe(II), in live primary hippocampal neurons using confocal and TauSTED (stimulated emission depletion) microscopy. TauSTED is based on super-resolution STED nanoscopy, which combines high spatial resolution imaging (<40nm) with fluorescence lifetime information, thus reducing background noise and improving image quality. We applied TauSTED imaging utilizing biotracker FerroFarRed Fe(II) and found that labile iron was present as submicrometric puncta in dendrites and axons. Some of these iron-rich structures are mobile and move along neuritic pathways, arguing for a labile iron transport mechanism in neurons. This super-resolution imaging approach offers a new perspective for studying the dynamic mechanisms of axonal and dendritic transport of iron at high spatial resolution in living neurons. In addition, this methodology could be transposed to the imaging of other fluorescent metal sensors.

IMPACT OF UAV AND SENTINEL-2A IMAGERY FUSION ON VEGETATION INDICES PERFORMANCE

Abstract. Image fusion techniques can improve the quality of remote sensing images by combining high spatial resolution images with low spectral resolution images. This enhancement of the images may impact the performance of various vegetation indices (VI’s). This study investigates the impact of image fusion on the quality of vegetation indices by fusing UAV (Unmanned Aerial Vehicle) bands with Sentinel-2A images using Principal Component Analysis (PCA) and Brovery Transform (BT) fusion techniques.The fused images were used to calculate the Normalized Difference Vegetation Index, Normalized Difference Red Edge, Green Red Vegetation Index, and Normalized Difference Water Index. To assess the performance of the fused images, several image quality assessment metrics were used, including Root Mean Square Error (RMSE), Entropy, etc...The results showed that image fusion techniques can improve the quality of images which is important to assess crop health. The PCA image fusion technique showed higher quality than the BT technique. The PCA fused images had lower RMSE, ERGAS, and Entropy Difference and higher UIQI, CC, and SSIM values than the original images. Moreover, the fused images produced higher VIs values than the Sentinel-2A images.Finally, scatter plots were created to compare the correlation between the VIs calculated from the original and fused images. The results showed a strong correlation between the VIs calculated from the Sentinel-2A and fused images, indicating that the fused images can accurately estimate vegetation health parameters. Overall, this study demonstrates the potential of image fusion techniques to improve the quality of VI’s for monitoring vegetation health.

Hippocampal subfield volume in relation to cerebrospinal fluid Amyloidβ1‐42 in early Alzheimer’s disease: A 7T MRI study

AbstractBackground7T MRI can provide high contrast and spatial resolution images for studying the volume of hippocampal subfields which can be used in conjunction with Amyloidβ1‐42 and tau measures in the cerebrospinal fluid (CSF) to provide a non‐invasive alternative marker for Alzheimer’s disease (AD).Method41 participants (20 AD and 21 healthy age‐ and gender‐matched controls) were recruited [aged 40 ‐ 76 years; 64% female). The protocol for the analysis of the CSF for the Amyloidβ1‐42 (reference range: 627‐1322 pg/ml), total tau (reference range: 146‐595 pg/ml), Thr181‐phosphorylated tau (reference range &lt;68 pg/ml) have been previously published (Hosseini et al, 2022) and the measures were used for the ATN classification of Alzheimer’s disease. Imaging was carried out on a Philips Achieva 7T scanner using a Nova Medical (Wilmington MA, USA) single‐channel transmit, 32‐channel receive (1Tx32Rx) head coil. Images acquired are PSIR (TE/TR = 3.1/6.9ms; FA = 6o, isotropic 0.55 mm resolution) and T2‐weighted FSE (TE/TR = 119/59001ms, FA = 90o, 0.38×0.39×1.50 mm3 resolution).ResultFig 1 show the T2‐weighted image and the segmentation of the hippocampus obtained using ASHS (Yushkevich et al., 2015b)There was a significant decrease in the volume of each hippocampal subfield (Fig2) in AD patients compared to controls. The change in volume was not significant for the whole brain showing that the hippocampal volume loss was not simply part of general brain atrophy.There was an approximately linear relationship between the CSF Amyloidβ1‐42 and volume of the hippocampus for the AD participants (Fig 3). A positive linear relationship was observed in all the subfields of the hippocampus but a statistically significant relation was only achieved in the Entorhinal Cortex (ERC) [p&lt;0.022, r = 0.508].The ERC is known to represent perception of time, memory and navigation (Tsao et al., 2018)(Du et al., 2001), and AD pathology may start from the ERC before migrating along the hippocampus (Braak &amp; Braak, 1997). Further, the ERC volume loss on 1.5 T MRI has been reported greater in AD as compared with Mild Cognitive Impairment(Du et al., 2001).ConclusionThe association of the volume of ERC and CSF Amyloidβ1‐42 suggests the potential for using 7T MRI as a biomarker for an early identification of AD.

Maximizing Societal Benefit Across Multiple Hyperspectral Earth Observation Missions: A User Needs Approach

AbstractImaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services.

Avaliação de recursos para o mapeamento de cobertura do solo em sub-bacia do rio Capibaribe-PE usando imagem Kompsat-2

Land use and land cover mapping is an important factor in geospatial analysis in watershed management. The integration of remote sensing images and Machine Learning classification techniques enable the identification and environmental monitoring of landscape elements. The MSC (MultiSpectral Camara) sensor on the Kompsat-2 satellite captures images of high spatial resolution, which allows the identification of terrestrial resources on a local scale. Six data models were developed for classifying land use and land cover by Random Forest in a Capibaribe River sub-basin. These models were created based on spectral indices and ranking of variable importance. The evaluation of the results was done through spatial quantification and accuracy analysis. Products based on bands and spectral indices showed global accuracy ranging between 94 and 98%, where the classes of Arboreal and Shrubby Vegetation stood out with estimates of accuracy of the producer and user above 80%. Products with the lowest data resources showed poor accuracy performance with overall accuracy values clustered below 60%. This study is the first to use adaptations of Kompsat-2 spectral data and computer learning methods to demonstrate the application of high-performance land cover mapping. Thus, this article contributed to the monitoring of the soil surface in urban sub-basins that need precise spatial information about the state of environmental conversation.

The balance between spectral and spatial information to estimate straw cereal plant density at early growth stages from optical sensors

Estimating straw cereal plant density at early stages is important for field crop management and phenotyping. Usual plant density estimation methods include manual counting and image-based counting, both of which have limited throughput, due to the need for high spatial resolution images. In this study, we explored the potential of high-throughput estimations with spectral information. A large and diverse dataset was collected on micro plot field experiments, encompassing six sites, three leaf stages, and four species of straw cereals. Canopy spectral reflectance was acquired with a spectrometer, both in 0° or 45° view zenith angle, perpendicularly to the row direction. Two reflectance-based approaches were then tested. In the direct approach, density was directly estimated from reflectance using Gaussian process regression (GPR) and spectral bands selected based on Akaike’s information criterion. In the indirect approach, the green fraction derived from high spatial resolution RGB images (GF_rgb) was first estimated from reflectance using GPR and selected bands, and then linearly related to density. These reflectance-based methods were compared to a classical image-based baseline method, which estimates density directly from GF_rgb.An ablation study firstly showed the superiority of 45° observations, and the necessity to calibrate one model for each site, growth stage, and species. The band selection process recommended using no more than four bands as inputs to the GPR models. The resulting direct and indirect estimations had an overall relative error of 30 %. The image-based baseline method had a lower error of 22 % for submillimeter spatial resolutions, but it performed worse than reflectance-based methods when degrading the spatial resolution to more than 1 to 2 mm to mimic an increase in sensor altitude. These results showed that spectral information can compensate for spatial information and that spectral methods can potentially provide high-throughput and reasonably accurate estimates of straw cereal plant density.

Advances in Ultrafast Fiber Lasers for Multiphoton Microscopy in Neuroscience

Multiphoton microscopy (MPM) has emerged as a vital tool in neuroscience, enabling deeper imaging with a broader field of view, as well as faster and sub-cellular resolution. Recent innovations in ultrafast fiber laser technology have revolutionized MPM applications in living brains, offering advantages like cost-effectiveness and user-friendliness. In this review, we explore the progress in ultrafast fiber laser technology, focusing on its integration into MPM for neuroscience research. We also examine the utility of femtosecond fiber lasers in fluorescence and label-free two- and three-photon microscopy applications within the field. Furthermore, we delve into future possibilities, including next-generation fiber laser designs, novel laser characteristics, and their potential for achieving high spatial and temporal resolution imaging. We also discuss the integration of fiber lasers with implanted microscopes, opening doors for clinical and fundamental neuroscience investigations.

The high energy X-ray probe: resolved X-ray populations in extragalactic environments

We construct simulated galaxy data sets based on the High Energy X-ray Probe (HEX-P) mission concept to demonstrate the significant advances in galaxy science that will be yielded by the HEX-P observatory. The combination of high spatial resolution imaging (&lt;20 arcsec FWHM), broad spectral coverage (0.2–80 keV), and sensitivity superior to current facilities (e.g., XMM-Newton and NuSTAR) will enable HEX-P to detect hard (4–25 keV) X-ray emission from resolved point-source populations within ∼800 galaxies and integrated emission from ∼6,000 galaxies out to 100 Mpc. These galaxies cover wide ranges of galaxy types (e.g., normal, starburst, and passive galaxies) and properties (e.g., metallicities and star-formation histories). In such galaxies, HEX-P will: 1) provide unique information about X-ray binary populations, including accretor demographics (black hole and neutron stars), distributions of accretion states and state transition cadences; 2) place order-of-magnitude more stringent constraints on inverse Compton emission associated with particle acceleration in starburst environments; and 3) put into clear context the contributions from X-ray emitting populations to both ionizing the surrounding interstellar medium in low-metallicity galaxies and heating the intergalactic medium in the z &amp;gt; 8 Universe.

TMIC-75. SPATIALLY-RESOLVED TRANSCRIPTOME ANALYSIS OF BRAIN METASTATIC BREAST CANCER REVEAL KEY MEDIATORS OF BRAIN-TROPIC METASTATIC POTENTIAL

Abstract BACKGROUND In women with metastatic breast cancer, 30% (HER2+) to 50% (triple negative) develop brain metastases. Breast-to-brain metastasis carries a poor median survival of only 10 months and is accompanied by severe neurologic decline. Halting the progression of breast cancer brain metastases requires modulation of the tumor microenvironment (TME), yet little is known about how the primary breast TME affects brain tropism, or how, once there, metastatic cancer cells interact with normal brain cells (e.g., neurons, astrocytes, microglia). METHODS We constructed a large (n = 253 cores), diverse, clinically-annotated tissue microarray (TMA) of breast cancer brain metastases with surrounding brain (n = 127), associated primary breast tumors (n = 67, with 30 matched), normal brain (n = 23), and staining control (n = 18) tissue samples, from patients (n = 140) treated at Stanford Hospital between 2008 – 2018. We then leveraged a combination of genome-wide transcriptome analysis and high resolution spatial imaging (using GeoMx Digital Spatial Profiling) to interrogate the distinct, spatially-driven gene signatures of tumor, immune, and normal brain cells within the breast cancer brain metastasis TME. RESULTS We revealed novel gene regulatory programs that are associated with metastatic potential from the primary breast to the brain. We also identified not only diverging features of the breast and brain TMEs, but equivalent features which may contribute to preferential honing and outgrowth of metastatic breast cells in the brain microniche. Additionally, we uncovered significant cellular crosstalk between tumor, immune, and brain cell populations, including putative cell-cell interactions and intercellular signaling pathways. CONCLUSION These results served as a starting point for identifying key tumor-immune-brain interactions responsible for priming immune and brain tolerance of malignant cells, allowing outgrowth and progression of breast cancer brain metastases. Further investigation focused on tumor-brain crosstalk, specifically tumor-microglia interactions, will provide promising targets for therapeutic advancements.

High-Precision Manufacture and Alignment of Image Slicer Based on Thin Glass Bonding

Integral field spectroscopy (IFS) is capable of simultaneously collecting two-dimensional high-resolution spatial image information and spectral information in a target field of view (FOV). The image slicer is the key element in IFS for segmenting the target FOV. Current micro-image slicer fabrication methods such as molecular assembling need strictly accurate control during sticking, which is time-consuming and expensive. In this study, we improved a direct-adhesive method for thin glass slicer manufacture and alignment. Thin glass is fabricated via single polishing and direct stitching to form a micro-image slicer. Based on charge coupled device (CCD) camera tests on parallel light, the manufacture accuracy for a six-channel image slicer reaches that of the molecular assembling scheme. This method can be applied to the fabrication of image slicers with different apertures.

Single-pixel real-part and magnitude imaging system based on digital micromirror device

Single-pixel imaging (SPI) can capture images using a single-pixel detector. However, conventional SPI schemes only provide a magnitude image of the object, where the phase information is completely lost. Here, we present a dual-modal SPI (DMSPI) system capable of simultaneously capturing the real-part and magnitude images. Since the real-part image fuses the phase distribution, DMSPI has a strong information acquisition capability for all types of objects. The DMSPI system utilizes the two reflection arms provided by the digital micromirror device (DMD) to perform zero-frequency detection in one arm and bucket detection in the other to achieve dual-modal imaging. Benefiting from the unique modulation characteristics of DMD, the DMSPI system is simple and efficient, with high spatial resolution and fast imaging speed. Thus, it might find broad applications in biomedical diagnostics and industrial inspection.

Ultrastructural characterization of maturing iPSC-derived nephron structures upon transplantation.

Pluripotent stem cell-derived kidney organoids hold great promise as a potential auxiliary transplant tissue for individuals with end-stage renal disease and as a platform for studying kidney diseases and drug discovery. To establish accurate models, it is crucial to thoroughly characterize the morphological features and maturation stages of the cellular components within these organoids. Nephrons, the functional units of the kidney, possess distinct morphological structures that directly correlate with their specific functions. High spatial resolution imaging emerges as a powerful technique for capturing ultrastructural details that may go unnoticed with other methods such as immunofluorescent imaging and scRNA sequencing. In our study, we have applied software capable of seamlessly stitching virtual slides generated from electron microscopy, resulting in high-definition overviews of tissue slides. With this technology, we can comprehensively characterize the development and maturation of kidney organoids when transplanted under the renal capsule of mice. These organoids exhibit advanced ultrastructural developments upon transplantation, including the formation of the filtration barrier in the renal corpuscle, the presence of microvilli in the proximal tubule, and various types of cell sub-segmentation in the connecting tubule similarly to those seen in the adult kidney. Such ultrastructural characterization provides invaluable insights into the structural development and functional morphology of nephron segments within kidney organoids and how to advance them by interventions such as a transplantation. Research Highlights High-resolution imaging is crucial to determine morphological maturation of hiPSC-derived kidney organoids. Upon transplantation, refined ultrastructural development of nephron segments was observed, such as the development of the glomerular filtration barrier.

Iterative Image Reconstruction Methodology in Optical CT Radiochromic Gel Dosimetry

Modern advancements in radiation therapy require paralleled advancements in the dosimetric tools used to verify dose distributions. Optical computed tomography (CT) imaged radiochromic gel dosimeters provide comprehensive, tissue equivalent, 3D dosimetric information with high spatial resolution and low imaging times. Traditional CT image reconstruction methods (filtered backprojection) do not account for light refraction within the optical CT system reducing the image quality. Iterative reconstruction methods make use of a system matrix that describes this light refraction thus, improving the reconstructed image quality. However, use of iterative reconstruction methods is not widespread, largely due to the impractical storage size of the required system matrix. Furthermore, current iterative reconstruction methods do not address the issue of image degradation due to a single detector element collecting light from multiple raypaths. For optical CT radiochromic gel dosimetry to be used effectively as a radiation therapy treatment plan verification tool, the system must be both practical and accurate. Thus, this work has two main objectives: (i) reduce the size of system matrices by means of polar coordinate discretization in lieu of the traditional Cartesian coordinate discretization, and (ii) reduce image degradation due to multiple raypaths by a novel approach to populating the system matrix that accounts for multiple raypaths.

Method and Experiments for Acquiring High Spatial Resolution Images of Abnormal Rice Canopy by Autonomous Unmanned Aerial Vehicle Field Inspection

The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the “close-look” features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat.

Agri-Fuse: A novel spatiotemporal fusion method designed for agricultural scenarios with diverse phenological changes

Images of key phenological periods play a vital role in agricultural applications as they capture the unique spectral characteristics of crops. Unfortunately, acquiring high-spatial-resolution images of the key phenological period from a single satellite platform remains challenging due to its short duration and synchronization with the rainy season. Spatiotemporal fusion (STF) is an effective tool for the prediction of missing high spatial resolution images on the required date, however, most STF methods assume a uniform relationship between the reflectance on the base date and the predicted date for the same land-cover type, which fails to hold in agricultural scenarios because of the diverse phenological changes among different crop types or different growth processes, even within a single crop type. To address these challenges, we propose a novel spatiotemporal fusion method for agricultural scenarios called Agri-Fuse. Agri-Fuse emphasizes change information and classifies change types based on the difference image derived from the fine image on the base date and the coarse image on the predicted date. Considering the various regression relationships between reflectance on the base date and the predicted date for different change types, a category-based linear regression model was built, and we introduced an unmixing model to solve these regression coefficients. We validated the performance of Agri-Fuse at two experimental sites where diverse phenological changes occurred during the key phenological periods of the same and different crop types. Compared to the four typical fusion methods (FSDAF, RASDF, STARFM, and Fit-FC), Agri-Fuse achieved the best performance in preserving both spectral fidelity and spatial details. Ablation experiments further confirmed the effectiveness of the proposed change-type classification and category-based regression in capturing diverse phenological change information. Thus, Agri-Fuse is expected to precisely reconstruct high-spatial-resolution images of key phenological periods necessary for agricultural applications.

Characterizing the distribution of a STING agonist and its metabolites in mouse liver by MALDI imaging mass spectrometry.

A STING (stimulator of interferon genes) agonist GSK3996915 under investigation in early discovery for hepatitis B was orally dosed to a mouse model for understanding the parent drug distribution in liver, the target organ. MALDI imaging mass spectrometry (IMS) was used to quantify the distribution of GSK3996915 in liver collected from mice administered a single oral dose at 90 mg/kg. GSK3996915 was detected with a zonal distribution localized in the portal triad and highly concentrated in the main bile ducts, indicating clearance through biliary excretion. High spatial resolution imaging showed the distribution of the parent drug localized to the cellular populations in the sinusoids including the Kupffer cells. Additionally, a series of drug-related metabolites were observed to be localized in the central zones of the liver. These results exemplify the potential of utilizing MALDI IMS for measuring not only quantitative drug distribution and target exposure, but also drug metabolism and elimination in a single suite of experiments. Significance Statement An integrated imaging approach utilizing MALDI IMS, immunohistochemistry (IHC), and histology was used to measure MALDI IMS complemented with other imaging techniques such as immunohistochemistry addressed the question of target exposure at the cellular level. Localized quantification of the parent drug in the target organ and identificaitonidentification of potential metabolites in the context of tissue histology were also achieved in one experimental suite to support characterization of pharmacokinetic properties of the drug in the early discovery stage.