Resolution Optical Tomography Research Articles

Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico.

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net ('bead') architectures (termed 'Θ-Net' in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).

Aboveground biomass inversion of forestland in a Jinsha River dry-hot valley by integrating high and medium spatial resolution optical images: A case study on Yuanmou County of Southwest China

It is crucial to develop a comprehensive method for estimating the aboveground biomass (AGB) of trees, shrubs, grasslands, and sparse tree areas in ecologically fragile dry, hot valley regions with vertical zonation. Multi-source remote-sensing data can fulfill this requirement, providing help in monitoring the health of ecosystems and providing a basis for regional biodiversity conservation and restoration. Sentinel-2A satellite imagery was used to classify the forests, shrubs, and grasslands in Yuanmou County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China. The Gaofen-2 satellite (GF-2) was used to extract the canopy width and calculate tree biomass in the valley-type savanna region. These data were combined with remote-sensing factors and measured survey data, and random forest (RF) and extreme gradient boosting (XGBoost) models were used to estimate the biomass. Using GF-2 images to segment sparse tree areas effectively reduced the overestimation of low-resolution remote-sensing images, enabling the AGB of sparse trees to be accurately estimated. The biomass estimations based on the Sentinel-2A images attained coefficient of determination (R2) values of 0.45 and 0.47 for the forest, 0.55 and 0.61 for the shrubs, and 0.32 and 0.37 for the grasslands using RF and XGBoost models, respectively, demonstrating variable effectiveness across vegetation types. In addition, the XGBoost model was more robust than the RF model for all three vegetation types. Our methodology provides scientific support for the sustainable development of ecologically fragile dry, hot valleys and savanna areas.

A survey for radio emission from white dwarfs in the VLA Sky Survey

ABSTRACT Radio emission has been detected from tens of white dwarfs, in particular in accreting systems. Additionally, radio emission has been predicted as a possible outcome of a planetary system around a white dwarf. We searched for 3 GHz radio continuum emission in 846 000 candidate white dwarfs previously identified in Gaia using the Very Large Array Sky Survey (VLASS) Epoch 1 Quick Look Catalogue. We identified 13 candidate white dwarfs with a counterpart in VLASS within 2 arcsec. Five of those were found not to be white dwarfs in follow-up or archival spectroscopy, whereas seven others were found to be chance alignments with a background source in higher resolution optical or radio images. The remaining source, WDJ204259.71+152108.06, is found to be a white dwarf and M-dwarf binary with an orbital period of 4.1 d and long-term stochastic optical variability, as well as luminous radio and X-ray emission. For this binary, we find no direct evidence of a background contaminant, and a chance alignment probability of only ≈2 per cent. However, other evidence points to the possibility of an unfortunate chance alignment with a background radio and X-ray emitting quasar, including an unusually poor Gaia DR3 astrometric solution for this source. With at most one possible radio emitting white dwarf found, we conclude that strong (≳1–3 mJy) radio emission from white dwarfs in the 3 GHz band is virtually non-existent outside of interacting binaries.

Reflective optical imaging for scattering medium using chaotic laser.

Optical imaging is a non-invasive imaging technology that utilizes near-infrared light, allows for the image reconstruction of optical properties like diffuse and absorption coefficients within the tissue. A recent trend is to use signal processing techniques or new light sources and expanding its application. We aim to develop the reflective optical imaging using the chaotic correlation technology with chaotic laser and optimize the quality and spatial resolution of reflective optical imaging. Scattering medium was measured using reflective configuration in different inhomogeneous regions to evaluate the performance of the imaging system. The accuracy of the recovered optical properties was investigated. The reconstruction errors of absorption coefficients and geometric centers were analyzed, and the feature metrics of the reconstructed images were evaluated. We showed how chaotic correlation technology can be utilized for information extraction and image reconstruction. This means that a higher signal-to-noise ratio and image reconstruction of inhomogeneous phantoms under different scenarios successfully were achieved. This work highlights that the peak values of correlation of chaotic exhibit smaller reconstruction error and better reconstruction performance in optical imaging compared with reflective optical imaging with the continuous wave laser.

Nd3+-sensitized multilayered rare-earth nanocrystals with enhanced NIR-IIb luminescence for high resolution optical imaging

Luminescence imaging in the second near-infrared (NIR-II) window, particularly within the NIR-IIb (1500–1700 nm) sub-window, has been utilized for both fundamental research and clinical applications due to its exceptional spatiotemporal resolution. Nevertheless, efficient contrast agents designed for NIR-IIb imaging are still scarce. Herein, a multilayered rare-earth nanocrystal (NaYF4:Yb,Er,Ce@NaYF4:Yb,Nd@NaYF4) with strong NIR-IIb luminescence was designed and synthesized. The doping of Ce3+ ions in the core effectively suppresses the upconversion and boosts the downconversion luminescence by populating the Er 4I13/2 level. Additionally, by separating the sensitizer (Nd3+) and the activator (Er3+) in different layers, we are able to maximize the light absorption around 800 nm and reduce the cross relaxations between rare-earth elements, which leads to a strong downconversion emission around 1530 nm under 808 nm excitation. After passivation with NaYF4 shell and phase transfer with highly PEGylated mesoporous silica layer, the nanocomposites with excellent colloidal stability and bright NIR-IIb emission were obtained. With long blood circulation time in vivo, the fabricated nanocomposites enable high resolution and contrast luminescence imaging of the blood vessels and tumors in living mice. Moreover, the rare-earth nanocrystals with superior optical properties also hold great promise for various applications in optical sensing and biomedical imaging.

An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience.

Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited. T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images. Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR. The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.

Nanoscale engineering of gold nanostars for enhanced photoacoustic imaging.

Photoacoustic (PA) imaging is a diagnostic modality that combines the high contrast resolution of optical imaging with the high tissue penetration of ultrasound. While certain endogenous chromophores can be visualized via PA imaging, many diagnostic assessments require the administration of external probes. Anisotropic gold nanoparticles are particularly valued as contrast agents, since they produce strong PA signals and do not photobleach. However, the synthesis of anisotropic nanoparticles typically requires cytotoxic reagents, which can hinder their biological application. In this work, we developed new PA probes based on nanostar cores and polymeric shells. These AuNS were obtained through one-pot synthesis with biocompatible Good's buffers, and were subsequently functionalized with polyethylene glycol, chitosan or melanin, three coatings widely used in (pre)clinical research. Notably, the structural features of the nanostar cores strongly affected the PA signal. For instance, despite displaying similar sizes (i.e. 45nm), AuNS obtained with MOPS buffer generated between 2 and 3-fold greater signal intensities in the region between 700 and 800nm than nanostars obtained with HEPES and EPPS buffers, and up to 25-fold stronger signals than spherical gold nanoparticles. A point source analytical model demonstrated that AuNS synthesized with MOPS displayed greater absorption coefficients than the other particles, corroborating the stronger PA responses. Furthermore, the AuNS shell not only improved the biocompatibility of the nanoconstructs but also affected their performance, with melanin coating enhancing the signal more than 4-fold, due to its own PA capacity, as demonstrated by both in vitro and ex vivo imaging. Taken together, these results highlight the strengths of gold nanoconstructs as PA probes and offer insights into the design rules for the nanoengineering of new nanodiagnostic agents.

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors.

Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

Why thermal images are blurry.

The resolution of optical imaging is limited by diffraction as well as detector noise. However, thermal imaging exhibits an additional unique phenomenon of ghosting which results in blurry and low-texture images. Here, we provide a detailed view of thermal physics-driven texture and explain why it vanishes in thermal images capturing heat radiation. We show that spectral resolution in thermal imagery can help recover this texture, and we provide algorithms to recover texture close to the ground truth. We develop a simulator for complex 3D scenes and discuss the interplay of geometric textures and non-uniform temperatures which is common in real-world thermal imaging. We demonstrate the failure of traditional thermal imaging to recover ground truth in multiple scenarios while our thermal perception approach successfully recovers geometric textures. Finally, we put forth an experimentally feasible infrared Bayer-filter approach to achieve thermal perception in pitch darkness as vivid as optical imagery in broad daylight.

Hybrid ultrasound and photoacoustic contrast agent designs combining metal phthalocyanines and PBCA microbubbles.

Photoacoustic (PA) imaging is an emerging diagnostic technology that combines the penetration depth of ultrasound (US) imaging and the contrast resolution of optical imaging. Although PA imaging can visualize several endogenous chromophores to obtain clinically-relevant information, multiple applications require the administration of external contrast agents. Metal phthalocyanines have strong PA properties and chemical stability, but their extreme hydrophobicity requires their encapsulation in delivery systems for biomedical applications. Hence, we developed hybrid US/PA contrast agents by encapsulating metal phthalocyanines in poly(butyl cyanoacrylate) microbubbles (PBCA MB), which display acoustic response and ability to efficiently load hydrophobic drugs. Six different metal chromophores were loaded in PBCA MB, showing greater encapsulation efficiency with higher chromophore hydrophobicity. Notably, while the US response of the MB was unaffected by the loading of the chromophores, the PA characteristics varied greatly. Among the different formulations, MB loaded with zinc and cobalt naphthalocyanines showed the strongest PA contrast, as a result of high encapsulation efficiencies and tunable optical properties. The strong US and PA contrast signals of the formulations were preserved in biological environment, as demonstrated by in vitro imaging in serum and whole blood, and ex vivo imaging in deceased mice. Taken together, these findings highlight the advantages of combining highly hydrophobic PA contrast agents and polymeric MB for the development of contrast agents for hybrid US/PA imaging, where different types of information (structural, functional, or potentially molecular) can be acquired by combining both imaging modalities.

Deep-learning-augmented microscopy for super-resolution imaging of nanoparticles.

Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement. A convolutional neural network (CNN) is constructed and pre-trained by the optical images of randomly distributed gold nanoparticles as input and the corresponding scanning-electron microscopy images as ground truth. The CNN is then learned to recover reversely the non-diffracted super-resolution images of both regularly arranged nanoparticle dimers and randomly clustered nanoparticle multimers from their blurry optical images. The profiles and orientations of these structures can also be reconstructed accurately. Moreover, the same network is extended to deblur the optical images of randomly cross-linked silver nanowires. Most sections of these intricate nanowire nets are recovered well with a slight discrepancy near their intersections. This deep-learning augmented framework opens new opportunities for computational super-resolution optical microscopy with many potential applications in the fields of bioimaging and nanoscale fabrication and characterization. It could also be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.

Sub-diffraction metalens for generating longitudinal bifoci and optical needles

Lenses are a fundamental component of optical systems. Bifoci and optical needle devices have excellent application potential in many optical systems. Conventional lenses are limited by their diffraction limits, and the spot size has a considerable influence on the resolution of optical and microscopic images. Optical super-oscillation is a novel technique, to the best of our knowledge, for far-field sub-diffraction focusing. In this study, we proposed a binary-amplitude super-oscillatory lens (SOL) approach for generating bifoci and optical needles (ON), and it was based on the angular spectrum method (ASM) and a binary-particle-swarm optimization (BPSO) algorithm. We reported a class of binary-amplitude-based Bifoci- and ON-SOLs that generated sub-diffraction bifoci and optical needles. Sub-diffraction bifoci with a transverse range of 0.401λ−0.522λ were recorded for a work wavelength of λ=632.8nm. The generated optical needle had a sub-diffraction length of 4.122λ, and the super-oscillation region was 2.083λ long. This provides potential applications for further super-resolution imaging, optical communication, and precision manufacturing.

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

Integrating object-based and pixel-based segmentation for building footprint extraction from satellite images

Accurately delineating building footprints from optical satellite imagery presents a formidable challenge, particularly in urban settings characterized by intricate and diverse structures. Consequently, enhancing the utility of these images for geospatial data updates demands meticulous refinement. Machine learning algorithms have made notable contributions in this context, yet the pursuit of precision remains an ongoing challenge. This paper aims to enhance the accuracy of building footprint extraction through the integration of object-based and pixel-based segmentation techniques. Additionally, it evaluates the performance of machine learning methodologies, specifically LightGBM, XGBoost, and Neural Network (NN) approaches. The model's evaluation employed low spectral resolution optical images, widely accessible and cost-effective for acquisition. The study's outcomes demonstrate a substantial enhancement in extraction accuracy compared to extant literature. The proposed methodology attains an overall accuracy of 99.39%, an F1 measurement of 0.9935, and a Cohen Kappa index of 0.9870. Thus, the proposed approach signifies a noteworthy advancement over existing techniques for building footprint extraction from high-resolution optical imagery.

Motor actions are spatially organized in motor and dorsal premotor cortex.

Frontal motor areas are central to controlling voluntary movements. In non-human primates, the motor areas contain independent, somatotopic, representations of the forelimb (i.e., motor maps). But are the neural codes for actions spatially organized within those forelimb representations? Addressing this question would provide insight into the poorly understood structure-function relationships of the cortical motor system. Here, we tackle the problem using high resolution optical imaging and motor mapping in motor (M1) and dorsal premotor (PMd) cortex. Two macaque monkeys performed an instructed reach-to-grasp task while cortical activity was recorded with intrinsic signal optical imaging (ISOI). The spatial extent of activity in M1 and PMd was then quantified in relation to the forelimb motor maps, which we obtained from the same hemisphere with intracortical microstimulation. ISOI showed that task-related activity was concentrated in patches that collectively overlapped <40% of the M1 and PMd forelimb representations. The spatial organization of the patches was consistent across task conditions despite small variations in forelimb use. Nevertheless, the largest condition differences in forelimb use were reflected in the magnitude of cortical activity. Distinct time course profiles from patches in arm zones and patches in hand zones suggest functional differences within the forelimb representations. The results collectively support an organizational framework wherein the forelimb representations contain subzones enriched with neurons tuned for specific actions. Thus, the often-overlooked spatial dimension of neural activity appears to be an important organizing feature of the neural code in frontal motor areas.

Full-angle high-reflection ultrathin composite film realizing high spatial resolution of scintillation crystal array

The performance of current nuclear medicine imaging systems is largely limited by the performance of detectors, and high spatial resolution detectors require high optical yield scintillator arrays. In this work, we simulated and designed for the first time a distributed Bragg reflector (multilayer dielectric film) that covers the entire lutetium yttrium oxyorthosilicate emission spectral band and consists of three 1/4 wavelength (λ/4) primary film systems centered at 420, 500, and 575 nm. In order to achieve ultrahigh reflectivity at the full incidence angle of the scintillator emitting surface, we propose a master optical configuration combining the dielectric film with a metal film/diffuse reflection adhesive. To explain this mechanism, we also simulated the change in reflectivity of the actual inner surface light collection. Experimental results show that a combination of a highly reflective reflector can achieve full-angle high reflectance at the total angle of incidence. We find that the dielectric film does not change the total reflection structure inside the crystal, while the light-blocking layer changes and increases the angular reflection of the dielectric film about the angle. These findings provide important insights into surface treatment as well as the design of scintillation crystal arrays, with far-reaching implications for high spatial resolution optical imaging systems.

Association Between Retinal Nerve Fiber Layer Thickness and Incident Dementia in the European Prospective Investigation into Cancer in Norfolk Cohort.

Retinal nerve fiber layer (RNFL) thickness may reflect cerebral status. This study assessed the relationship between RNFL thickness and incident all-cause dementia in the European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) Eye Study. Glaucoma detection with variable corneal compensation (GDx-VCC) and Heidelberg Retinal Tomograph II (HRT II) derived global mean RNFL thickness from dementia-free participants at baseline within the EPIC-Norfolk Eye Study were analyzed. Incident dementia was identified through linkage to electronic medical records. Cox proportional hazard mixed-effects regression models adjusted for key confounders were used to examine the associations between RNFL thickness and incident dementia in four separate models. 6,239 participants were included with 322 cases of incident dementia and mean age of 67.5-years old, with 49.7% women (median follow-up 13.2-years, interquartile range (11.7 to 14.6 years). Greater RNFL thickness (GDx-VCC) was not significantly associated with a lower risk of incident dementia in the full adjusted model [HR per quartile increase 0.95; 95% CI 0.82-1.10]. Similarly, RNFL thickness assessed with HRT II was also not associated with incident dementia in any model (full adjusted model; HR per quartile increase: 1.06; [95% CI 0.93-1.19]. Gender did not modify any associations under study. GDx-VCC and HRT II derived RNFL thickness are unlikely to be useful predictors of incident dementia. Higher resolution optical imaging technologies may clarify whether there are useful relationships between neuro-retinal morphology and brain measures.

3D printing of plasmonic nanofocusing tip enabling high resolution, high throughput and high contrast optical near-field imaging

Scanning near-field optical microscopy (SNOM) offers a means to reach a fine spatial resolution down to ~ 10 nm, but unfortunately suffers from low transmission efficiency of optical signal. Here we present design and 3D printing of a fiber-bound polymer-core/gold-shell spiral-grating conical tip that allows for coupling the inner incident optical signal to the outer surface plasmon polariton with high efficiency, which then adiabatically transport, squeeze, and interfere constructively at the tip apex to form a plasmonic superfocusing spot with tiny size and high brightness. Numerical simulations and optical measurements show that this specially designed and fabricated tip has 10% transmission efficiency, ~ 5 nm spatial resolution, 20 dB signal-to-noise ratio, and 7000 pixels per second fast scanning speed. This high-resolution, high throughput, and high contrast SNOM would open up a new frontier of high spatial-temporal resolution detecting, imaging, and monitoring of single-molecule physical, chemical, and biological systems, and deepen our understanding of their basic science in the single-molecule level.

Characterization of a commercial bioluminescence tomography-guided system for pre-clinical radiation research.

Widely used Cone-beam computed tomography (CBCT)-guided irradiators have limitations in localizing soft tissue targets growing in a low-contrast environment. This hinders small animal irradiators achieving precise focal irradiation. To advance image-guidance for soft tissue targeting, we developed a commercial-grade bioluminescence tomography-guided system (BLT, MuriGlo) for pre-clinical radiation research. We characterized the system performance and demonstrated its capability in target localization. We expect this study can provide a comprehensive guideline for the community in utilizing the BLT system for radiation studies. MuriGlo consists of four mirrors, filters, lens, and charge-coupled device (CCD) camera, enabling a compact imaging platform and multi-projection and multi-spectral BLT. A newly developed mouse bed allows animals imaged in MuriGlo and transferred to a small animal radiation research platform (SARRP) for CBCT imaging and BLT-guided irradiation. Methods and tools were developed to evaluate the CCD response linearity, minimal detectable signal, focusing, spatial resolution, distortion, and uniformity. A transparent polycarbonate plate covering the middle of the mouse bed was used to support and image animals from underneath the bed. We investigated its effect on 2D Bioluminescence images and 3D BLT reconstruction accuracy, and studied its dosimetric impact along with the rest of mouse bed. A method based on pinhole camera model was developed to map multi-projection bioluminescence images to the object surface generated from CBCT image. The mapped bioluminescence images were used as the input data for the optical reconstruction. To account for free space light propagation from object surface to optical detector, a spectral derivative (SD) method was implemented for BLT reconstruction. We assessed the use of the SD data (ratio imaging of adjacent wavelength) in mitigating out of focusing and non-uniformity seen in the images. A mouse phantom was used to validate the data mapping. The phantom and an in vivo glioblastoma model were utilized to demonstrate the accuracy of the BLT target localization. The CCD response shows good linearity with<0.6% residual from a linear fit. The minimal detectable level is 972 counts for 10×10 binning. The focal plane position is within the range of 13-18mm above the mouse bed. The spatial resolution of 2D optical imaging is<0.3mm at Rayleigh criterion. Within the region of interest, the image uniformity is within 5% variation, and image shift due to distortion is within 0.3mm. The transparent plate caused<6% light attenuation. The use of the SD imaging data can effectively mitigate out of focusing, image non-uniformity, and the plate attenuation, to support accurate multi-spectral BLT reconstruction. There is<0.5% attenuation on dose delivery caused by the bed. The accuracy of data mapping from the 2D bioluminescence images to CBCT image is within 0.7mm. Our phantom test shows the BLT system can localize a bioluminescent target within 1mm with an optimal threshold and only 0.2mm deviation was observed for the case with and without a transparent plate. The same localization accuracy can be maintained for the in vivo GBM model. This work is the first systematic study in characterizing the commercial BLT-guided system. The information and methods developed will be useful for the community to utilize the imaging system for image-guided radiation research.