Photoacoustics

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.

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.

It is increasingly necessary to both study biology in 3D and obtain quantitative measurements. Not all 3D-reconstructions are created equal, particularly when using the anatomical model as a basis for force calculations, i.e. computational modeling. Here, we compare 3D anatomical reconstructions from two emerging imaging modalities: 4D ultrasound (4DUS) and light sheet fluorescent microscopy (LSFM) against our previous nano-computed tomography (nanoCT) cohort data, using the tortuous highly intricate pharyngeal arch artery system of the chick embryo as a test bed. We highlight modality-specific morphological image acquisition discrepancies and their influence on subsequent computational fluid dynamics results. Overall, LSFM accurately captured quantitative volumetric measurements of small rapidly-changing vascular morphologies while 4DUS systematically inflated small tortuous vessels. Differences in image-based morphology changes led to significant changes in computationally-obtained force magnitudes and flow patterns linked to vessel angle and tortuosity. This validates LSFM as a comparative preclinical vascular quantitative imaging tool and suggests that 4DUS needs extensive 3D anatomical validation for non cardiac chamber vessels.

Non-invasive, high-resolution visualization of mouse brain vasculature remains challenging due to significant light scattering and absorption by mammalian tissues, hence many optical imaging protocols require scalp and/or skull excision. Here we present a fully reversible tartrazine-based optical clearing strategy that enables cortex-wide optical coherence tomography angiography (OCTA) through intact scalp and skull. We characterized tartrazine properties in the near infrared (NIR)-II band of the 1.3 {micro}m swept-source OCTA system, confirming minimal absorption across 1.25-1.35 {micro}m wavelength range and an effectively constant refractive index, suggesting negligible OCTA distortions. Spatially selective agent application showed that intracranial vessels emerge selectively within the treated region of interest (ROI), whereas untreated regions retain strong interference by the scalp vascular features. Depth-encoded projections and cross-sectional OCTA demonstrated an increased signal recovery at depth and an extended vessel-detection range after clearing. Vessel-map changes were quantified using intersection-over-union and Dice coefficients, yielding high similarity outside the ROI and reduced similarity within the ROI, consistent with a transition from scalp to brain vasculature. Reproducibility was confirmed in three independent 11-week-old mice and validated against scalp-removed reference OCTA. Screening tartrazine in the 0.3-0.8 Molar concentration range (7-min application) identified 0.6 M as optimal for whole-cortex scanning, balancing clearing efficacy and solution handling. Finally, the protocol generalized across mice aged 5-18 weeks. This approach provides a practical route to non-invasive structural cerebrovascular mapping with OCTA.

Accurate knowledge of tissue absorption (a) and reduced scattering [Formula] parameters are required to plan and monitor laparoscopic chemophototherapy (CPT) in ovarian cancer, including light dosimetry and quantitative fluorescence mapping of porphyrin-phospholipid (PoP) photobleaching and light-triggered doxorubicin (Dox) release. We implemented a depth-sensitive, multi-frequency laparoscopic spatial frequency domain imaging (SFDI) framework to improve optical-property estimation in layered tissue. A DMD-based laparoscope imaged two-layer phantoms with controlled optical contrasts and superficial thicknesses. Spatial-frequency subsets associated with different penetration depths were independently fit to recover a and [Formula], and compared with a two-layer diffusion model. Recovered [Formula] values remained bounded by the known layer references and shifted monotonically toward the superficial value as spatial frequency and top-layer thickness increased, approaching a single-layer response at high frequency/thick layers. Quantitative model comparison showed {delta}-P1 variants outperformed the standard diffusion approximation, reducing RMSPE between modeled and measured [Formula] to 0.8-6.5% (silicone/silicone) and 1.6-8.3% (silicone/intralipid), whereas SDA errors reached [~]13.8% and 21.1%, respectively. This approach demonstrates multi-frequency laparoscopic SFDI as a practical initial step for depth-sensitive fluorescence correction for individualized CPT treatment planning and monitoring.

Accurately assessing blood vascular networks is important in all organ systems in health, disease, and healing. However, methods to do so in a holistic fashion in post-mortem specimens have significant limitations. We have previously demonstrated proof of concept for a lead abbelaite nanoparticle and alginate nanocomposite contrast agent which would allow greatly improved vessel imaging using x-ray based imaging modalities like CT and microCT. Specifically the contrast is spectrally enhanced to easily allow segmentation from mineralized tissues and gelation is triggerable permitting better vascular perfusion. Here we expand upon that work by substituting lead tungstate nanoparticles. We found that delivery viscosity and radiopacity are largely unaffected. However, mechanical strength was negatively impacted as abellaite presence was lowered. In sum, these formulations have performed in bulk reasonably enough to warrant advancement to in vivo post-mortem evaluation in small animal models.

Handheld ultrasound devices have revolutionized point-of-care diagnostics, but their effectiveness remains limited by operator dependency and the need for specialized training. This paper presents an intelligent guidance and diagnostic assistance system for the handheld wireless ultrasound device, enabling automated carotid artery and thyroid examinations through handheld operation. Drawing inspiration from the Actor-Critic framework, we implement a simulation-based reinforcement learning approach for real-time probe navigation toward standard anatomical views. The system integrates YOLOv8n-based detection networks for carotid plaque and thyroid nodule identification, achieving real-time inference at 30 frames per second. Furthermore, we propose a hybrid measurement approach combining UNet segmentation with the Snake algorithm for precise biometric quantification, including carotid intima-media thickness (IMT), lumen diameter, and lesion dimensions. Experimental validation on clinical datasets demonstrates that the proposed system achieves 91.2% accuracy in standard plane acquisition, 87.5% mean average precision (mAP) for plaque detection, and 89.3% mAP for nodule identification. Measurement results show excellent agreement with expert sonographers, with IMT measurements exhibiting a mean absolute difference of 0.08 mm. These findings demonstrate the feasibility of intelligent handheld ultrasound examination, significantly reducing operator dependency while maintaining diagnostic accuracy comparable to experienced clinicians.

Tumour biomarkers such as CA125, CA15-3, CA19-9, AFP and CEA are routinely used in the oncology clinic to diagnose cancer, monitor response to therapy, and detect relapse. However, their quantification depends on immunoassay-based methods that are time-consuming, reagent-dependent, and poorly suited to resource-limited settings. Here, we present a machine learning-assisted ATR-FTIR spectroscopy approach for label-free tumour biomarker analysis to enable simple and rapid quantification at the bedside. Using principal component analysis (PCA), we first demonstrate that these five clinically relevant biomarkers are spectrally separable, with the protein-associated region (1200-1700 cm-1) providing the greatest discriminative information. We then develop partial least squares regression (PLSR) models to quantify CA125 in phosphate-buffered saline (R2 = 0.95) and in human serum across a clinically relevant concentration range, achieving reliable predictions at and above the clinical decision threshold of 35 U/mL. A semi-quantitative classification model further demonstrated robust identification of elevated CA125, with a macro-average sensitivity of 0.86 and specificity of 0.92. These results support ATR-FTIR spectroscopy as a rapid, reagent-free platform for cancer biomarker monitoring, with potential utility in resource-limited settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/714840v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1be9c03org.highwire.dtl.DTLVardef@f49e5eorg.highwire.dtl.DTLVardef@1c93e39org.highwire.dtl.DTLVardef@1141e6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Computed Tomography (CT) is one of the largest contributors to radiation exposure from medical imaging, which can induce DNA damage and increase cancer risk. Reducing CT radiation dose to improve patient safety inherently increases image noise and artifacts. Generative adversarial networks (GANs) have shown promise for unsupervised low-dose CT (LDCT) denoising. Building on this, RDBCycleGAN-CBAM, a CycleGAN-based model that integrates residual dense blocks (RDBs) and convolutional block attention modules (CBAM), was developed to effectively denoise quarter-dose CT images while preserving structural detail. The model was trained on unpaired quarter-dose and full-dose CT scans from the NIH-AAPM-Mayo dataset using adversarial (LSGAN), cycle-consistency, and identity losses. Evaluation on held-out test slices was performed using PSNR and SSIM as the primary image-quality metrics. The results demonstrate that the proposed RDBCycleGAN-CBAM method not only achieves higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values but also outperforms most existing deep learning-based methods, achieving mean improvements of +3.97 dB in PSNR and +0.053 in SSIM relative to quarter-dose inputs. Shapiro- Wilk tests for PSNR and SSIM motivated the use of the nonparametric Wilcoxon signed-rank test, by which highly significant improvements across both metrics (PSNR and SSIM) were demonstrated. The very large rank-biserial correlation values (1.0) indicate that nearly all test images experienced substantial quality improvement. Furthermore, the narrow bootstrap confidence intervals for the mean differences suggest that these improvements are consistent across the dataset. These advancements contribute to medical imaging by providing a viable, vendor-neutral tool for reducing patient radiation exposure without compromising diagnostic value.

Cardiac fibrosis is a key contributor to cardiomyopathy after myocardial infarction (MI). Existing imaging techniques can detect established fibrotic changes; however, they lack sensitivity for ongoing collagen turnover--a dynamic process involving the denaturation of collagen triple helix. Molecular imaging of this process could enhance risk assessment and aid in the development of anti-fibrotic treatments. This study aimed to evaluate 99mTc-(HE)-(GPO), a radiotracer designed to target denatured collagen, as a biomarker of collagen turnover after MI. Methods99mTc-(HE)-(GPO) incorporates glycine-proline-hydroxyproline (GPO) repeats and can hybridize with denatured single- or double-stranded collagen. MI was induced in mice by ligation of the left anterior descending artery; sham-operated animals served as controls. At 2 weeks post-MI, animals underwent myocardial perfusion imaging or contrast-enhanced CT to detect the infarct zone, followed by SPECT/CT imaging using 99mTc-(HE)-(GPO) or a control scrambled tracer. Tracer uptake was quantified in vivo and ex vivo with gamma counting and autoradiography. Different aspects of fibrosis were examined by tissue analysis, along with autoradiography with a matrix metalloproteinase-targeted radiotracer, 99mTc-RYM1. Tracer binding was also assessed in human cardiac tissue through ex vivo autoradiography. Results99mTc-(HE)-(GPO) SPECT/CT revealed significantly higher tracer uptake in the infarct zone of MI mice compared to the remote zone and sham controls (P < 0.0001 for both). Tracer uptake was confirmed by autoradiography, which showed a strong correlation between SPECT and autoradiography (R = 0.81, P < 0.01). The scrambled tracer exhibited minimal cardiac uptake, demonstrating the specificity of 99mTc-(HE)-(GPO) signal. Denatured collagen staining and 99mTc-RYM1 autoradiography showed similar patterns as ex vivo 99mTc-(HE)-(GPO) autoradiography, while the ratio of denatured collagen to procollagen in the infarct zone significantly increased from day 3 to 2 weeks post-MI. Finally, 99mTc-(HE)-(GPO) bound to human fibrotic (but not normal) cardiac tissue. Conclusion99mTc-(HE)-(GPO) enables non-invasive detection of denatured collagen as a marker of collagen remodeling in vivo, offering a promising tool for assessing fibrotic remodeling after MI. Collagen, procollagen, and denatured collagen, along with MMP activation, exhibit distinct patterns, and their combined imaging may provide a comprehensive molecular fingerprint of cardiac fibrosis, advancing personalized management of cardiomyopathy.

BackgroundTranscranial focused ultrasound (tFUS) is an emerging noninvasive neuromodulation modality with the ability to target deep brain structures with high spatial precision. Despite its promise, rigorous evaluation of its efficacy is limited by the absence of reliable, fully double-blind sham methodologies. ObjectiveTo develop and validate a pair of visually and mechanically indistinguishable acoustic coupling pads that enable true double-blind tFUS neuromodulation studies by providing either efficient ultrasound transmission or robust ultrasound blocking without altering participant or operator experience. MethodsTwo coupling pads were engineered: a transmitting pad designed to allow <5% pressure amplitude loss relative to free-water propagation, and a non-transmitting pad designed to attenuate ultrasound by [&ge;]40 dB. Both pads used a Dragon Skin 10 NV silicone base and were identical in size, appearance, flexibility, and handling. The non-transmitting pad incorporated an encapsulated air-based blocking layer using an open-cell polyethylene foam insert. Acoustic performance was evaluated in a water tank using a 650 kHz BrainSonix transducer and a calibrated needle hydrophone. Sound speed of the silicone material was measured using pulse-echo techniques. ResultsTwenty-three matched transmitting and non-transmitting pad pairs were fabricated and tested. Transmitting pads demonstrated a mean attenuation of -0.41 {+/-} 0.53 dB, satisfying the design criterion of minimal acoustic loss.Non-transmitting pads demonstrated a mean attenuation of -48.61 {+/-} 4.33 dB, exceeding the required -40 dB threshold for effective sham conditions. The Dragon Skin 10 NV substrate exhibited a sound speed of 964.72 m/s and produced <2 mm axial focal shift for standard pad thicknesses, with no measurable change in focal width. Both pad types were visually and tactually indistinguishable, could not be differentiated by experienced operators or participants, and maintained mechanical integrity after repeated cleaning ConclusionThese acoustically engineered coupling pads provide a practical and validated solution for achieving true single- and double-blind conditions in tFUS neuromodulation studies. By preserving identical sensory and procedural experiences while enabling precise control over ultrasound transmission, this approach addresses a critical methodological gap in human ultrasound neuromodulation research.

BackgroundTumor progression is associated with alterations in tissue mechanical properties. Experimental studies in cancer mechanobiology suggest that increased viscosity of the tumor habitat can promote tumor growth, while malignant tumors often exhibit pronounced mechanical heterogeneity with coexisting soft and rigid regions that facilitate cell motility. Elastography enables noninvasive viscoelastic profiling of soft-tissue properties in vivo and may therefore detect tumor malignancy. PurposeTo investigate whether multiparametric external vibration-based ultrasound time-harmonic elastography (THE) can differentiate benign from malignant liver tumors and identify viscoelastic parameters associated with tumor malignancy. Materials and MethodsIn this prospective study conducted from January 2025 to March 2026, 94 patients with focal liver lesions underwent THE. Eighty-four patients were included in the final analysis (41 benign, 39 malignant; 45 women; age range 30-87 years). Liver and tumor stiffness (shear wave speed; SWS), viscosity (loss angle; {phi}), and spatial mechanical heterogeneity (spatial standard deviation, SWS-SD) were quantified. Diagnostic performance for differentiating benign and malignant tumors was assessed using the area under the receiver operating characteristic curve (AUC). ResultsTumor heterogeneity and surrounding habitat viscosity provided the most pronounced differentiation between malignant and benign lesions. Malignant tumors demonstrated higher SWS-SD (0.41{+/-}0.20 vs. 0.28{+/-}0.11 m/s) and increased {phi} (0.76{+/-}0.09 vs. 0.71{+/-}0.05 rad) with a combined discriminative power of AUC=0.72. These viscoelastic differences were more pronounced in larger tumors of [&ge;]2.5 cm2 area (SWS-SD: 0.47{+/-}0.19 vs. 0.32{+/-}0.11 m/s; {phi}: 0.78{+/-}0.10 vs 0.70{+/-}0.04 rad) yielding AUC=0.88 while excellent discriminative power of AUC=0.97 for [&ge;]6 cm2 tumor area. ConclusionElevated viscosity of the tumor habitat combined with increased tumor stiffness-heterogeneity measured by multiparametric THE can differentiate liver malignancies from benign liver lesions. THE may thus provide a rapid, cost-effective approach for viscoelastic profiling of liver tumors in clinical diagnostic imaging.

Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg-Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for continuous-wave excitation but degrade significantly under single-cycle conditions. In diagnostic imaging, high frame rate is critical for applications demanding rapid scanning. In this context, multi-line transmission (MLT) leverages beam shaping to synthesize multiple simultaneous foci, thereby increasing frame rate. In parallel, structured illumination methods for super-resolution and acoustical holography likewise depend on actively shaping single-cycle pulses to produce controlled patterns, highlighting the need for precise short-pulse beam shaping. To address this challenge, we introduce the spatio-temporal adaptive reconstruction (STAR) algorithm, which performs active beam shaping directly in the time domain by integrating the generalized angular spectrum method (GASM) into an iterative optimization scheme. STAR enforces constraints on both the transducer and focal planes, enabling accurate control of single-cycle excitations. Simulations showed that STAR consistently outperformed GS for multi-focus patterns. For example, in a four-foci configuration, STAR achieved a correlation of 0.80 compared to 0.64 for GS, with significantly improved uniformity across focal peaks. Resolution analysis demonstrated that STAR reduced the minimum distinguishable foci spacing from 1.09 mm with GS to 0.87 mm. Experimental hydrophone measurements confirmed these improvements. Across multi-foci patterns, STAR produced more distinct and balanced foci compared to those observed with GS. These results demonstrate that STAR provides robust and efficient active beam shaping of single-cycle pulses, maintaining accuracy across different depths and frequencies for diagnostic applications.

Sodium magnetic resonance imaging (23Na MRI) provides a unique opportunity to probe ionic microenvironments in neural tissue because sodium ions play central roles in membrane electrophysiology, ion transport, and cellular homeostasis. Unlike conventional proton ({superscript 1}H) MRI, which primarily reflects water distribution and tissue structure, {superscript 2}3Na MRI is sensitive to ionic compartmentation and quadrupolar interactions arising from the spin-3/2 nature of the sodium nucleus. However, sodium MRI remains technically challenging due to intrinsically low signal sensitivity and rapid biexponential relaxation, particularly when imaging small biological systems. Here, we establish a high-field multinuclear MRI platform for imaging human cerebral organoids at 14 Tesla. Cerebral organoids derived from human induced pluripotent stem cells provide a simplified three-dimensional neural tissue model that enables investigation of ionic microenvironments without vascular or systemic confounds. Using a dual-tuned {superscript 1}H/{superscript 2}3Na radiofrequency coil, we performed co-registered structural, diffusion, and sodium imaging of individual fixed organoids. High-resolution {superscript 1}H MRI (33-100 m) revealed pronounced microstructural heterogeneity, while multi-echo {superscript 2}3Na MRI (300-400 m) enabled voxel-wise characterization of quadrupolar relaxation behavior. Bi-exponential analysis of the sodium signal decay identified distinct relaxation components (T2*short {approx} 1 ms and T2*long {approx} 12 ms) and revealed spatial heterogeneity in sodium microenvironments across the organoid tissue. These results demonstrate the feasibility of quantitative sodium relaxometry in cortical organoids and establish a multinuclear imaging platform for investigating ionic microenvironment dynamics in three-dimensional neural tissue models.

SignificanceQuantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying cerebral blood flow in general and neural-vascular coupling and stroke in particular. Conventional multi-exposure fitting is slow and difficult to scale. Efficient, physically grounded methods are needed to extract both vascular and tissue dynamic biomarkers from LSCI data. AimTo develop and validate a physics-informed neural network (PINN) that quantitatively estimates fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from LSCI measurements without requiring ground-truth labels. ApproachWe developed a physics-informed neural network (PINN) to estimate fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from multi-exposure LSCI data without requiring ground-truth labels. The analytical LSCI model is embedded in the network loss function, enforcing consistency with speckle physics during training. The model operates in a self-supervised manner and performs pixel-wise inference across full-field images. The framework was evaluated using in vivo mouse stroke LSCI datasets. ResultsThe PINN accurately recovered fast decorrelation rates associated with cerebral blood flow and slower dynamics linked to tissue and cellular motion. The parameter maps closely match those from traditional nonlinear fitting, but at orders-of-magnitude higher speed, reducing analysis from several hours to two seconds per image. It also generalizes to unseen subjects and remains robust under noise. ConclusionsOur approach establishes physics-informed learning as a practical framework for near real-time extraction of vascular and cellular biomarkers from LSCI, enabling longitudinal monitoring of stroke progression and potentially facilitating clinical translation.

Myocaridum biomechanics are a biomarker for multiple cardiac pathologies. However the rapid and complex heart motion hampers accurate measurements of the tissue stiffness. Current in vivo methods for the evaluation of myocardium mechanical health are either highly invasive or can only provide with a global surrogate of heart function as they suffer from poor spatiotemporal resolution. We propose a new in vivo technique, transient magnetic resonance elastography (tMRE), to assess the dynamic cardiac biomechanics. tMRE is able to quantify local shear wave speed as a proxy for myocardial stiffness at user-defined times within the cardiac cycle. We report proof-of-concept results where we probe the septum of 4 different healthy rat specimens at 3 physiologically distinct cardiac phases. We provide with apparent speed measurements for early systole, mid-late systole and early diastole that match the expected values from the cardiac cycle physiological mechanics. We correct for non-negligible geometrical biases using literature results and report true stiffness values where possible. Finally, we validate tMRE in phantom experiments.

Three-dimensional (3D) handheld photoacoustic tomography typically relies on bulky and expensive external positioning trackers to correct motion artifacts, which severely limits its clinical flexibility and accessibility. To address this challenge, we present PA-SfM, a tracker-free framework that leverages exclusively single-modality photoacoustic data for both sensor pose recovery and high-fidelity 3D reconstruction via differentiable acoustic radiation modeling. Unlike traditional Structure-from-Motion (SfM) methods that formulate pose estimation as a geometry-driven optimization over visual features, PA-SfM integrates the acoustic wave equation into a differentiable programming pipeline. By leveraging a high-performance, GPU-accelerated acoustic radiation kernel, the framework simultaneously optimizes the 3D photoacoustic source distribution and the sensor array pose via gradient descent. To ensure robust convergence in freehand scenarios, we introduce a coarse-to-fine optimization strategy that incorporates geometric consistency checks and rigid-body constraints to eliminate motion outliers. We validated the proposed method through both numerical simulations and in-vivo rat experiments. The results demonstrate that PA-SfM achieves sub-millimeter positioning accuracy and restores high-resolution 3D vascular structures comparable to ground-truth benchmarks, offering a low-cost, softwaredefined solution for clinical freehand photoacoustic imaging. The source code is publicly available at https://github.com/JaegerCQ/PA-SfM.

Peritoneal micrometastases (micromets) remain a major barrier to durable cytoreduction in ovarian and other intra-abdominal cancers, because lesions can be difficult to visualize and are often resistant to systemic therapy. Liposomal doxorubicin (Dox) improves pharmacokinetics but can be limited by slow intratumoral release. Porphyrin-phospholipid (PoP) liposomes enable near-infrared light-triggered release of Dox (chemophototherapy (CPT)), creating an opportunity for intraoperative, fluorescence-guided treatment planning and monitoring. Here, we evaluate a laparoscopic fluorescence imaging platform for quantifying light-triggered drug delivery in 2D monolayers and 3D spheroid cluster models. Dox fluorescence increased linearly with administered LC-Dox-PoP concentration in both SCC2095sc and SKOV-3 cultures (R2 = 0.97-0.98 in 2D; R2 = 0.98 in spheroid clusters over 1-9 {micro}g/mL). Laparoscope-derived fluorescence measurements agreed with standard well-plate reader measurements (R2 = 0.89-0.96). Porphyrin fluorescence provided stronger, complementary contrast for localizing spheroid constructs and decreased after activation light exposure, consistent with photobleaching during triggered release. Together, these results support a quantitative imaging framework for fluorescence-guided monitoring of light-triggered liposomal drug release, with potential to inform individualized CPT dosimetry for peritoneal micrometastases. These findings in SCC2095sc (oral squamous cell carcinoma) additionally suggest relevance of fluorescence-guided CPT for head and neck/oral cancer, where localized post-resection adjuvant treatment may improve control of residual disease.

SignificanceMastectomy skin flap necrosis remains a major complication in implant-based breast reconstruction due to inadequate tissue blood flow. Existing diagnostic technologies are limited by shallow depth sensitivity, dye-related risks, contact requirements, and an inability to continuously assess blood flow. AimThis study aimed to translate a noncontact, dye-free, depth-sensitive speckle contrast diffuse correlation tomography (scDCT) technique to a clinically relevant porcine skin flap model for assessing flap blood flow and viability. ApproachThe scDCT system was optimized to image blood flow over seven days in four porcine skin flaps including Sham (SH), Implant (IM), Half Necrosis (HN), and Full Necrosis (FN). Measurements were compared with indocyanine green angiography (ICG-A) as a reference standard. ResultsscDCT enabled longitudinal monitoring of flap blood flow, revealing significant flow differences among flap types and over time. FN flaps consistently exhibited the most severe flow impairment, while other flap types showed partial or complete recovery over time, distinguishing nonviable from viable tissue. scDCT measurements demonstrated moderate to strong correlations with ICG-A across time points. ConclusionsThe findings support scDCT as a promising perioperative imaging modality for improving flap necrosis risk stratification and surgical decision-making, with future work focused on large-scale validation and clinical translation.

Focused ultrasound can be delivered through the temporal window to modulate heterogeneously located brain areas. Acoustic simulations allow for safety assessments when dynamically targeting brain structures, but the mismatch between simulation and measured focal pressure can vary across the steerable range due to mechanically inaccurate assumptions made about the skull and transducer. Here, we describe efficient methods for simulation-measurement calibration using axisymmetric projections and sparse sampling across a 3D steerable subspace encompassing deep brain targets across 157 subjects. To address the simulation-reality mismatch in skull transmission, we used the measured and predicted pressure values through eight human temporal window fragments to derive an optimized bone attenuation coefficient. Collectively, the calibration framework and optimized temporal window coefficients can be used broadly across studies to improve the accuracy of reporting and dependent safety assessment for personalized neuromodulation treatments.