Photoacoustics

Light-emitting diode (LED)-based photoacoustic (PA) imaging offers a compact, safe, and cost-effective alternative to laser systems. However, low LED power leads to low optical fluence, which in turn limits penetration depth. We report a systematic optimization of LED-PA performance by jointly tuning ultrasound (US) probe frequency and LED pulse width, validated in both phantom and in vivo studies. Using a commercially available LED-PA/US platform, we compared a custom 5 MHz transducer with commercial 7 and 10 MHz probes under LED pulses of 30 to 100 ns. The 5 MHz probe with a 100 ns pulse achieved the best trade-off between depth sensitivity and resolution, enabling detection of targets up to 18 mm. In vivo experiments demonstrated, for the first time, clear visualization of the carotid artery and deep-seated breast vessels using LED-based PA imaging. These findings show that careful optimization of probe frequency and pulse width can substantially extend the depth performance of LED-PA, advancing its potential for vascular and oncologic applications.

AbstractO_ST_ABSSignificanceC_ST_ABSPhotoacoustic (PA) imaging has garnered considerable attention due to its capability to render vascular images in a label-free manner. Specifically, devices employing a hemispherical detector array (HDA) have been heralded for various clinical applications, owing to their potential to yield high reproducibility three-dimensional images. While high-resolution models utilizing high-frequency sensors have been introduced for animal experimentation, their evaluation has been constrained to a single wavelength. In this study, we demonstrate the applicability of in vivo mouse models for visualizing body oxygen saturation distribution using dual wavelengths. AimWith the aid of our uniquely developed device and analysis software, our primary objective is to map the spatial distribution of the hemoglobin oxygen saturation coefficient (S-factor) through non-invasive in vivo imaging. Subsequently, we aim to observe the temporal alterations within this distribution, specifically assessing changes in hemoglobin oxygen saturation in both normal and tumor vessels over time. ApproachHigh-quality S-factor images were obtained by integrating a newly developed scanning sequence for high contrast with alternate two-wavelength irradiation. Following validation with phantoms, in vivo images were procured in mice. Sequential scanning of the same mouse yielded information about temporal changes. S-factor evaluation was conducted with our photoacoustic image viewer to analyze trends in hemoglobin oxygen saturation. ResultsHigh-contrast images were achieved by increasing the number of integrations during scanning. S-factor images were acquired using both healthy and tumor-bearing mice. Vessels within the liver and kidneys were distinctly reconstructed, and differences in oxygen saturation discriminated between arteries and veins. Repeated measurements on the same mice, both live and post-euthanasia, provided spatiotemporal information, such as a decrease in oxygen saturation after euthanasia or a precipitous drop in oxygen saturation inside the tumor nine days post-cell line transplantation. ConclusionsBy analyzing S-factor images using a photoacoustic imaging system designed for animal experiments, we succeeded in discerning variations in in vivo oxygen saturation. The custom-built system holds promise as a versatile tool for diverse basic research endeavors, as it can seamlessly interface with human clinical applications.

Understanding brain-wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help study neuro-disorders and brain functions. However, the existing brain imaging technologies have limited resolution, sensitivity, imaging depth and provide information about only one or two hemodynamic parameters. To address this, we propose a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head-mountable device, to quantitatively map cerebral blood volume (CBV), cerebral blood flow (CBF), oxygen saturation (SO2) dynamics as well as contrast agent enhanced brain imaging with high spatiotemporal resolutions. After systematic characterization, the fUSPA system was applied to quantitatively study the changes in brain hemodynamics and vascular reactivity at single vessel resolution in response to hypercapnia stimulation. Our results show an overall increase in brain-wide CBV, CBF, and SO2, but regional differences in singular cortical veins and arteries and a reproducible anti-correlation pattern between venous and cortical hemodynamics, demonstrating the capabilities of the fUSPA system for providing multiparametric cerebrovascular information at high-resolution and sensitivity, that can bring insights into the complex mechanisms of neurodiseases.

SignificanceIndocyanine green (ICG) is the most widely used fluorophore in fluorescence-guided surgery (FGS), yet its spectral response depends on microenvironment, with implications for system design, inter-system comparisons, and phantom development. AimTo characterize ICG with excitation-emission matrices (EEMs) in the microenvironments of dimethyl sulfoxide (DMSO), bovine serum albumin (BSA) solutions, and 3D-printed (3DP) resin, and assess excitation-dependent emission, including red-edge excitation shifts (REES) and departures from Kashas rule of excitation-independent emission. ApproachEEMs and absorbance spectra were acquired with extracted excitation spectra, emission spectra, emission peaks, centroids, and integrated emission areas under the curve (AUCs). Concentration-dependent behavior was examined in DMSO, and albumin concentration dependence was assessed from 5-100 mg/mL. Data processing employed robust local regression to mitigate excitation scattering artifacts. ResultsICG in DMSO exhibited excitation-independent emission consistent with Kasha-Vavilov behavior. In contrast, ICG in BSA solution and 3DP resin displayed excitation-dependent emission with pronounced REES and additional non-linear departures from Kashas rule. To our knowledge, this represents the first documentation of REES and broader anti-Kasha effects for ICG or any FGS fluorophore. Within the excitation range most relevant to ICG-FGS ([~]760-805 nm), emission spectra of the BSA solution and 3DP resin overlapped closely, with similar AUC-based comparisons, suggesting that ICG in 3DP resin can serve as a suitable surrogate reference for albumin-bound ICG. ConclusionsThe EEM characterization shows that excitation-dependent behavior is a defining feature of ICG in biologically relevant environments, demonstrating that emission cannot be assumed to follow classical Kasha-Vavilov behavior. Reliable comparisons and imaging system design therefore require spectra acquired at defined excitation wavelengths with AUC integration within the emission detection band. Excitation-specific spectra from EEMs establish a consistent framework for inter-system comparisons and phantom standards, while the resulting datasets provide a practical reference for addressing excitation-dependent behavior in ICG sensing applications.

ObjectiveSuper-resolution ultrasound (SR-US) reveals microvascular structures with exquisite resolution, but clinical translation remains limited by the need for ultrafast frame rates, massive data volumes, and long reconstruction times. This work proposes a deep learning framework that reconstructs microvascular maps from low-frame-rate enhanced ultrasound sequences, bypassing explicit microbubble localization and tracking. MethodsA transformer-decoder network with learned linear projections was designed to model spatiotemporal dependencies across sparse contrast-enhanced ultrasound sequences and reconstruct vessel probability maps, refined via a post-processing enhancement stage. Single-head self-attention captures temporal correlations under challenging conditions including overlapping microbubbles and low signal-to-noise ratios. Binary cross-entropy loss guided training to preserve vascular topology across synthetic and in vivo datasets. In vivo rat brain bolus data from the PALA challenge was used to evaluate this approach under up to 500 - fold data reduction (341 frames at 2 FPS vs. 170400 frames at 1000 FPS in standard ULM). ResultsDespite aggressive undersampling, the proposed pipeline recovered coherent microvascular architecture where conventional ULM pipelines applied to the same sparse data failed to produce continuous vascular networks. Major branches and higher-order microvessels remained visible with apparent vessel widths broadened by approximately three-fold relative to reference SR-US. End-to-end reconstruction completed in 28-133 seconds on an NVIDIA H100 GPU depending on the number of frames employed. ConclusionThe reported approach preserved vascular topology with fast reconstruction and low data overhead, albeit at lower resolution. The substantial reduction in frames and computation time highlights the translational potential of this SR-US-inspired microvascular imaging approach.

Radiofrequency (RF) ablation is a minimally invasive therapy for heart arrhythmia, including atrial fibrillation (A-fib), which creates lesions using an electric current to isolate the heart from abnormal electrical signals. However, conventional RF procedures do not involve intraoperative monitoring of the area and extent of ablation-induced necrosis, making the assessment of the procedure completeness challenging. Previous studies have suggested that spectroscopic photoacoustic (sPA) imaging is capable of differentiating ablated tissue from its non-ablated counterpart based on PA spectrum variation. Here, we aim to demonstrate the applicability of sPA imaging in an in vivo environment, where the cardiac motion presents, and introduce a framework for mapping the necrotic lesion using cardiac-gated sPA imaging. We computed the degree of necrosis, or necrotic extent (NE), by dividing the quantified ablated tissue contrast by the total contrast from both ablated and non-ablated tissues, visualizing it as continuous colormap to highlight the necrotic area and extent. To compensate for tissue motion during the cardiac cycle, we applied the cardiac-gating on sPA data, based on the image similarity. The in vivo validation of the concept was conducted in a swine model. As a result, the ablation-induced necrotic lesion at the surface of the beating heart was successfully depicted throughout the cardiac cycle through cardiac-gated sPA (CG-sPA) imaging. The results suggest that the introduced CG-sPA imaging system has great potential to be incorporated into clinical workflow to guide ablation procedures intraoperatively.

Microbubble-mediated focused ultrasound (FUS) offers a non-invasive treatment for transient and localized blood-brain barrier (BBB) opening for drug delivery or immunostimulation. It is known that FUS-induced BBB opening is accompanied by blood flow changes, vasoconstriction, and vasodilation, as validated by optical microscopy through a cranial window. In this study, we introduce a novel method for quantifying vascular changes after FUS-induced BBB opening by employing ultrasound flow imaging in mice. We acquired pre-FUS and post-FUS ultrasound flow images with the same microbubble concentration in the brain. Contrast-enhanced power Doppler (CEPD) images and ultrasound localization microscopy images were obtained to evaluate changes in cerebral blood volume and vessel diameter at the sonicated region of the brain. Our findings demonstrate that FUS leads to a reduction in blood volume at the treated region, with vasoconstriction being more dominant than vasodilation. Furthermore, we show that transcranial CEPD can detect local blood reduction following FUS, which spatially coincides with the edema region identified in T2-weighted MRI. Our findings suggest that ultrasound flow imaging has the potential to serve as a cost-effective and immediate monitoring tool for evaluating the safety and efficacy of FUS-induced BBB opening.

Despite advances in diagnosis and management, cardiovascular diseases (CVDs) remain the leading cause of death in the US. Atherosclerosis is the most common form of CVD and the vulnerability of atherosclerotic plaques to rupture is a primary determinant for risk of catastrophic ischemic events. Current imaging of atherosclerotic disease focuses on assessing plaque size and the degree of luminal stenosis, which are not good predictors of plaque stability. Functional methods to identify biomarkers of inflammation in plaques could facilitate assessment of plaque instability to allow early intervention. In this study, we validate the use of a purpose-built, magnetic resonance imaging (MRI)-compatible positron emission tomography (PET) insert for multimodal, molecular imaging of vulnerable plaques in mice. We illustrate the application of PET to screen for inflamed regions to guide the application of MRI. Molecular MRI visualizes regions of vascular inflammation and is coupled with anatomical MRI to generate detailed maps of the inflammatory marker within the context of an individual vessel. As a testbed for this imaging methodology, we developed a multimodal, iron oxide nanoparticle (NP) targeting vascular cell adhesion molecule-1 (VCAM-1) for simultaneous PET/MRI of vascular inflammation performed on a mouse carotid ligation model. In vitro cell studies confirmed that the NPs are not cytotoxic to liver cells. In vivo simultaneous PET/MRI imaging identified regions of inflammation. Three-dimensional rendering of the MRI data facilitated high-resolution visualization of patterns of inflammation along the injured vessel. Histology validated the co-localization of the NPs with VCAM-1 expression at sites of induced inflammation. The results of this work validate the utility of the simultaneous PET/MR insert as a research tool for small animals and lays groundwork to further advance the potential clinical utility of integrated imaging systems.

SignificanceConventional spectral photoacoustic imaging (sPAI) to assess tissue oxygenation (sO2) uses optical wavelengths in the first near infrared window (NIR-I). This limits the maximum imaging depth ([~]1 cm) due to high spectral coloring of biological tissues. AimSecond near infrared or short-wave infrared (NIR-II or SWIR) wavelengths (950-1400 nm) show potential for deep tissue sPAI due to the exponentially reduced tissue scattering and higher maximum exposure threshold (MPE) in this wavelength range. However, to date, a systematic assessment of NIR-II wavelengths for sPAI of tissue sO2 has yet to be performed. ApproachThe NIR-II PA spectra of oxygenated and deoxygenated hemoglobin was first characterized using a phantom. Optimal wavelengths to minimize spectral coloring were identified. The resulting NIR-II PA imaging methods were then validated in vivo by measuring renal sO2 in adult female rats. ResultssPAI of whole blood under a phantom and of circulating renal blood in vivo, demonstrated PA spectra proportional to wavelength-dependent optical absorption. NIR-II wavelengths had a [~]50% decrease in error of spectrally unmixed blood sO2 compared to conventional NIR-I wavelengths. In vivo measurements of renal sO2 validated these findings and demonstrated a [~]30% decrease in error of estimated renal sO2 when using NIR-II wavelengths for spectral unmixing in comparison to NIR-I wavelengths. ConclusionssPAI using NIR-II wavelengths improved the accuracy of tissue sO2 measurements. This is likely due to the overall reduced spectral coloring in this wavelength range. Combined with the increased safe skin exposure fluence limits in this wavelength range, demonstrate the potential to use NIR-II wavelengths for quantitative sPAI of sO2 from deep heterogeneous tissues.

Background2D Quantitative Myocardial Perfusion (Qperf) MRI is limited by its inability to provide complete myocardial coverage within a heartbeat interval. This study developed and evaluated dual saturation multiband-accelerated Qperf imaging to achieve near-complete left ventricular coverage in free-breathing at an adequate in-plane spatial resolution, using a semi-automated Myocardial Blood Flow (MBF) framework for quantification deployable directly on the scanner console. MethodsA dual-saturation single-band QPerf sequence was modified for multiband imaging, enabling the acquisition of 6 high-resolution myocardial slices plus Arterial Input Function (AIF) during free-breathing. The technique was evaluated in 16 sedated pigs (13 healthy and 3 with LAD occlusion) under rest conditions on a 3T MRI scanner. Additionally, two healthy pigs underwent stress imaging as well. Statistical comparisons were performed between multiband and single-band MBF values in corresponding AHA segments. ResultsQualitatively, multiband QPerf provided superior left ventricular coverage and comparable image quality to single-band Qperf MBF maps, potentially enabling a more comprehensive detection of perfusion defects at rest. Quantitatively, multiband QPerf yielded lower MBF values than single-band QPerf (p < 0.01). However, Bland-Altman analysis (mean difference: -0.17 ml/min/g; 95% CI: -1.12 to 0.79 ml/min/g) and Passing-Bablok regression (intercept: -0.01 ml/min/g; 95% CI: - 0.37 to 0.28 ml/min/g) indicated that such discrepancy remained within the expected confidence intervals. Furthermore, the Passing-Bablok slope (0.88; 95% CI: 0.73-1.06) confirmed that mb-QPerf maintained sensitivity comparable to sb-QPerf in detecting perfusion changes under rest conditions. Finally, there was an overall increase in MBF values during stress vs rest conditions (average MBF Ratio 1.67 {+/-} 0.31) when comparing healthy pigs. ConclusionMultiband-accelerated Qperf is feasible, providing improved left ventricular coverage, adequate in-plane resolution, and a semi-automated MBF quantification framework directly on the scanner console. Compared to its single-band counterpart, multiband QPerf demonstrated a more comprehensive visualization of perfusion defects and comparable sensitivity and accuracy in detecting perfusion changes at rest. Further research and clinical validation in patient populations are needed to confirm its utility in the diagnosis of coronary artery disease.

Morphological changes of spheroids and organoids are widely used as in vitro indicators of healthy and diseased tissue functions; however, quantitative methods to classify spheroid and organoid morphology are limited. In clinical breast imaging, radiologists use tumor shape as a prognostic marker, with irregular margins associated with invasive disease and increased malignancy. Here, we adapted this approach for translational research and developed a custom MATLAB algorithm to quantify the variance in radial lengths of invasive protrusions in spheroids and organoids. First, we analyzed digital phantoms by both ImageJ/FIJI shape descriptors and our radial length analysis to evaluate the capabilities of each measurement method. Subsequently, we performed the same comparisons with images from experimental spheroid and organoid datasets. We demonstrate that multivariate shape factor analysis, including radial length analysis, enables more reliable and comprehensive quantification of spheroid and organoid morphologies than standard shape descriptors alone. By enabling numerical morphological readouts, shape factor analysis can enhance phenotypic profiling of spheroids and organoids and provide valuable metrics for in vitro studies including high-throughput and drug screening workflows.

Photoacoustic (PA) signals are typically broadband, with a correlation between their frequency characteristics and source dimension. The transducers that are commonly used for PA acquisition are optimized for pulse-echo ultrasound imaging and are primarily based on inorganic piezoelectrics in ceramic, single-crystal, or composite form. These transducers are band-limited which limits their functionality as receivers for broadband PA signals. Custom broadband transducers are expensive and complex to manufacture. In this work, we propose to use a poly vinylidene difluoride (PVDF) based transducer for PA acquisition in combination with a commercial single-crystal linear array for pulse-echo acquisition. An 8-element PVDF array with 20dB onboard amplification was built in-house. The PVDF receiver is transparent to the pulse-echo ultrasound, and both transducers were positioned such that they image the same volume. The combined PA raw data from the PVDF and the linear array demonstrated the feasibility of achieving a broader overall reception bandwidth. This study establishes a foundation for a simpler acquisition system that enhances PA signal quality, co-registered with conventional ultrasound imaging, which may support the clinical adoption of PA imaging.

For the effective operation of medical microrobots within living organisms and precise targeting, it is imperative to employ imaging techniques closely integrated with real-time deep-tissue tracking methods. However, due to a typically low Signal-to-Noise ratio images with strong background, it is hard for traditional tracking methods to achieve sufficient accuracy. This challenge can be addressed by deep learning-based tracking with a real-time detection model. However, a multitude of design choices and Hyperparameters influence the performance. In this study we compared the influence of the hyperparameters and model architecture versions of the "you only look once" (YOLO) network. We use experimental data from a magnetic microrobot imaged with Photoacoustics through 5 mm phantom tissue to evaluate the tracking in comparison with an optical reference. The deep-learning based methods consistently achieved lower missing-detection ratios. Regarding the Root Mean Square localization error, we observed that increasing the weight of the box loss function and utilizing the distribution focal loss can enhance the performance by 10%. Furthermore, it can be seen that YOLOv9 consistently outperformed its predecessor YOLOv8. This study quantifies the robustness of deep-learning based tracking of medical microrobots under tissues.

Ultrafast nonlinear ultrasound imaging of gas vesicles (GV) contrast agents promises high-sensitivity biomolecular visualization with applications such as targeted molecular imaging of tumor markers or real-time tracking of gene expression. However, separating GV-specific signal from tissue remains challenging and requires the implementation of complex transmit schemes. In this work we introduce harmonic amplitude-modulated singular value decomposition (HAM-SVD), which synergizes pulse inversion (PI) with amplitude-modulated singular value decomposition (AM-SVD) to isolate GV-specific second-harmonic signals. In HAM-SVD, single-cycle plane waves at 9.6 MHz and five tilted angles (at a pulse repetition frequency of 2500 Hz) are transmitted under four duty cycles with alternating polarity. Beamformed IQ data are reshaped along a "space x pressure" matrix and decomposed via SVD; tissue background is cancelled by discarding the first and lowest singular modes, yielding an image comprised solely of pressure-dependent second harmonic signals. HAM-SVD sequence enables wide-field, ultrafast imaging without complex transmit sequences. Validation via simulations, in vitro phantoms, and in vivo rat lower limb experiments demonstrates HAM-SVDs outperformance compared to PI and AM-SVD. HAM-SVD is shown to achieve a 19.16 {+/-} 1.63 dB signal-to-background ratio (SBR) in vivo, surpassing PI (14.19 {+/-} 1.41 dB) and AM-SVD (15.79 {+/-} 1.38 dB). HAM-SVD overcomes limitations of conventional nonlinear techniques (e.g., depth restrictions, tissue clutter) by combining PIs harmonic sensitivity with AM-SVDs adaptive clutter filtering of tissue signals. This approach enhances molecular imaging specificity for GVs and holds potential for ultrasound localization microscopy of slow-flowing agents.

Direct visualization of metabolic conversions within living systems is essential for understanding metabolic activities yet challenging due to the absence of reaction-specific reporters and the limited sensitivity of current imaging modalities. Herein, we report an approach to monitor fatty acids (FAs) desaturation, primarily catalyzed by stearoyl-CoA desaturase, in cancer cells using deuterium (D)-labeled palmitic acid (PA-d31) as the reaction-specific reporter and mid-infrared photothermal (MIP) microscopy as the bond-selective imaging modality. The desaturation of PA-d31 produced a peak at 2246 cm-1 in the cell-silent region, corresponding to the stretching vibration of unsaturated C-D bonds (D-C=C-D) in unsaturated fatty acids. Penalized least squares fitting was employed to remove water background for enhancing the visibility of this peak. Our study revealed heterogeneous spatial distributions of both saturated FAs and their desaturated metabolites within lipid droplet pools in cancer cells. Furthermore, we observed an increase in fatty acid unsaturation level in OVCAR5 cells under cisplatin-induced stress. By directly visualizing fatty acid desaturation, this study offers new insights into fatty acid metabolism and opens avenues for evaluating new therapeutic strategies targeting fatty acid metabolism.

We present here a new approach for non-invasive high resolution whole-body vascular imaging in depth by combining water-soluble and bright SWIR-emitting gold nanoclusters revealing an anisotropic surface charge with Monte Carlo image processing of the images. We applied and validated this approach to quantify vessel complexity in transgenic mice presenting vascular disorders.

Label-free, two-photon imaging captures morphological and functional metabolic tissue changes and enables enhanced understanding of numerous diseases. However, this modality suffers from low signal arising from limitations imposed by the maximum permissible dose of illumination and the need for rapid image acquisition to avoid motion artifacts. Recently, deep learning methods have been developed to facilitate the extraction of quantitative information from such images. Here, we employ deep neural architectures in the synthesis of a multiscale denoising algorithm optimized for restoring metrics of metabolic activity from low-SNR, two-photon images. Two-photon excited fluorescence (TPEF) images of reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavoproteins (FAD) from freshly excised human cervical tissues are used. We assess the impact of the specific denoising model, loss function, data transformation, and training dataset on established metrics of image restoration when comparing denoised single frame images with corresponding six frame averages, considered as the ground truth. We further assess the restoration accuracy of six metrics of metabolic function from the denoised images relative to ground truth images. Using a novel algorithm based on deep denoising in the wavelet transform domain, we demonstrate optimal recovery of metabolic function metrics. Our results highlight the promise of denoising algorithms to recover diagnostically useful information from low SNR label-free two-photon images and their potential importance in the clinical translation of such imaging.

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

Current advances in ultrasound imaging techniques including super-resolution ultrasound imaging allows us to visualize microvasculature in biological specimens using microbubbles. However, microbubbles diffuse in blood stream limiting imaging acquisition and frame subtraction scheme of super-resolution ultrasound imaging cannot improve spatial resolution without moving microbubbles. Fluorescent proteins revolutionized to understand molecular and cellular functions in biological systems. Here, we devised a panel of gas vesicles to realize multiplexed ultrasound imaging to uniquely visualize locations of different species of gas vesicles. Mid-band fit spectral imaging technique demonstrated that stationary gas vesicles were efficiently localized in gel phantom and murine liver specimens by visualizing three-dimensional vessel structures. Clustered and unclustered gas vesicles were phagocytosed by murine macrophages to serve as carriers and beacons for the proposed multiplexed and single cell level imaging technique. The spatial distribution of macrophages containing clustered and unclustered gas vesicles were reconstructed by mid-band fit spectral imaging with pseudo-coloring scheme.

SignificanceWhile shortwave infrared (SWIR) imaging provides superior tissue penetration and reduced autofluorescence for preclinical applications, quantitative fluorescence analysis is hindered by the limited dynamic range of InGaAs cameras, forcing a focus on either bright or dim anatomical features. AimWe develop a high dynamic range (HDR) imaging method specifically adapted for the high-noise characteristics of InGaAs detectors to enable quantitative fluorescence imaging across wide intensity ranges. We demonstrate that one-time camera calibration based on a series of images encompassing the range of radiance intensities enables all subsequent image processing. ApproachWe modified classical HDR algorithms with exposure-time-dependent dark current subtraction, preprocessing to exclude saturated and noisy pixels before camera response function recovery, and dynamic weighting range adjustment to account for shrinking intensity ranges at longer exposures. HDR image processing effects on preclinical imaging outcomes were analyzed using indocyanine green and SWIR-emitting PbS/CdS quantum dots in mouse models. ResultsHDR imaging achieved a 22 dB improvement in dynamic range over single exposures, enabling simultaneous quantification across more than three orders of magnitude of fluorophore concentration. In vivo studies showed improvements in contrast-to-noise ratios across all anatomical features, with improvements in vascular contrast while maintaining quantitative accuracy. After one-time camera calibrations, this approach enables rapid processing of subsequent datasets. ConclusionsThis software-based HDR SWIR imaging approach eliminates exposure parameter optimization and enables comprehensive biodistribution analysis across all anatomical structures from a single acquisition sequence, significantly streamlining preclinical imaging workflows while preserving quantitative accuracy.