Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy.

Significance: Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes. Aim: We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT. Approach: Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers. Results: Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (p=0.042). In addition, the change in normalized tumor StO2 upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (p=0.038). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI. Conclusions: These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.

Imperfect refractive index matching in scanning laser optical tomography and a method for digital correction.

Significance: Scanning laser optical tomography (SLOT) is a volumetric multi-modal imaging technique that is comparable to optical projection tomography and computer tomography. Image quality is crucially dependent on matching the refractive indexes (RIs) of the sample and surrounding medium, but RI matching often requires some effort and is never perfect. Aim: Reducing the burden of RI matching between the immersion medium and sample in biomedical imaging is a challenging and interesting task. We aim at implementing a post processing strategy for correcting SLOT measurements that have errors caused by RI mismatch. Approach: To better understand the problems with poorly matched Ris, simulated SLOT measurements with imperfect RI matching of the sample and medium are performed and presented here. A method to correct distorted measurements was developed and is presented and evaluated. This method is then applied to a sample containing fluorescent polystyrene beads and a sample made of olydimethylsiloxane with embedded fluorescent nanoparticles. Results: From the simulations, it is evident that measurements with an RI mismatch larger than 0.02 and no correction yield considerably worse results compared to perfectly matched measurements. RI mismatches larger than 0.05 make it almost impossible to resolve finer details and structures. By contrast, the simulations imply that a measurement with an RI mismatch of up to 0.1 can still yield reasonable results if the presented correction method is applied. The experiments validate the simulated results for an RI mismatch of about 0.09. Conclusions: The method significantly improves the SLOT image quality for samples with imperfectly matched Ris. Although the absolutely best imaging quality will be achieved with perfect RI matching, these results pave the way for imaging in SLOT with RI mismatches while maintaining high image quality.

Mapping brain function in adults and young children during naturalistic viewing with high-density diffuse optical tomography.

Human studies of early brain development have been limited by extant neuroimaging methods. MRI scanners present logistical challenges for imaging young children, while alternative modalities like functional near-infrared spectroscopy have traditionally been limited by image quality due to sparse sampling. In addition, conventional tasks for brain mapping elicit low task engagement, high head motion, and considerable participant attrition in pediatric populations. As a result, typical and atypical developmental trajectories of processes such as language acquisition remain understudied during sensitive periods over the first years of life. We evaluate high-density diffuse optical tomography (HD-DOT) imaging combined with movie stimuli for high resolution optical neuroimaging in awake children ranging from 1 to 7 years of age. We built an HD-DOT system with design features geared towards enhancing both image quality and child comfort. Furthermore, we characterized a library of animated movie clips as a stimulus set for brain mapping and we optimized associated data analysis pipelines. Together, these tools could map cortical responses to movies and contained features such as speech in both adults and awake young children. This study lays the groundwork for future research to investigate response variability in larger pediatric samples and atypical trajectories of early brain development in clinical populations.

Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.

Maternal prenatal depressive symptoms and child brain responses to affective touch at two years of age.

BACKGROUND: Touch is an essential form of mother-child interaction, instigating better social bonding and emotional stability. METHODS: We used diffuse optical tomography to explore the relationship between total haemoglobin (HbT) responses to affective touch in the child's brain at two years of age and maternal self-reported prenatal depressive symptoms (EPDS). Affective touch was implemented via slow brushing of the child's right forearm at 3 cm/s and non-affective touch via fast brushing at 30 cm/s and HbT responses were recorded on the left hemisphere. RESULTS: We discovered a cluster in the postcentral gyrus exhibiting a negative correlation (Pearson's r = -0.84, p = 0.015 corrected for multiple comparisons) between child HbT response to affective touch and EPDS at gestational week 34. Based on region of interest (ROI) analysis, we found negative correlations between child responses to affective touch and maternal prenatal EPDS at gestational week 14 in the precentral gyrus, Rolandic operculum and secondary somatosensory cortex. The responses to non-affective touch did not correlate with EPDS in these regions. LIMITATIONS: The number of mother-child dyads was 16. However, by utilising high-density optode arrangements, individualised anatomical models, and video and accelerometry to monitor movement, we were able to minimize methodological sources of variability in the data. CONCLUSIONS: The results show that maternal depressive symptoms during pregnancy may be associated with reduced child responses to affective touch in the temporoparietal cortex. Responses to affective touch may be considered as potential biomarkers for psychosocial development in children. Early identification of and intervention in maternal depression may be important already during early pregnancy.

IR780-based diffuse fluorescence tomography for cancer detection.

IR780 iodide is a commercially available targeted near-infrared contrast agent for in vivo imaging and cancer photodynamic or photothermal therapy, whereas the accumulation, dynamics, and retention of IR780 in biological tissue, especially in tumor is still under-explored. Diffuse fluorescence tomography (DFT) can be used for localization and quantification of the three-dimensional distribution of NIR fluorophores. Herein, a homemade DFT imaging system combined with tumor-targeted IR780 was utilized for cancer imaging and pharmacokinetic evaluation. The aim of this study is to comprehensively assess the biochemical and pharmacokinetic characteristics of IR780 with the aid of DFT imaging. The optimal IR780 concentration (20 µg/mL) was achieved first. Subsequently, the good biocompatibility and cellar uptake of IR780 was demonstrated through the mouse acute toxic test and cell assay. In vivo, DFT imaging effectively identified various subcutaneous tumors and revealed the long-term retention of IR780 in tumors and rapid metabolism in the liver. Ex vivo imaging indicated IR780 was mainly concentrated in tumor and lung with significantly different from the distribution in other organs. DFT imaging allowed sensitive tumor detection and pharmacokinetic rates analysis. Simultaneously, the kinetics of IR780 in tumors and liver provided more valuable information for application and development of IR780.

AI-driven projection tomography with multicore fibre-optic cell rotation.

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.

Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling.

OBJECTIVE: Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design. METHODS: Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution. RESULT: Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography. CONCLUSION: We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice. SIGNIFICANCE: Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

K-CapsNet: K-Nearest Neighbor Based Convolution Capsule Network for Cerenkov Luminescence Tomography Reconstruction.

Cerenkov luminescence tomography (CLT) has received significant attention as a promising imaging modality that can display the three-dimensional (3D) distribution of radioactive probes. However, the reconstruction of CLT suffers from severe ill-posed problem. It is difficult for traditional model-based method to obtain satisfactory result. Recently, deep learning-based method have shown great potential for accurate and efficient CLT reconstruction. In this study, a KNN-based convolution capsule network, named K-CapsNet, is proposed for cerenkov luminescence tomography. In K-CapsNet, the surface photon intensity is encoded in capsule form. The KNN-based convolution and K-means clustering are proposed for efficient encoding. Numerical simulation experiments have been carried out to verify the performance of K-CapsNet, and the results show that it performs superior in source localization and morphological restoration compared with existing methods.

FISTA-NET: Deep Algorithm Unrolling for Cerenkov luminescence tomography.

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging technique that can be used to reconstruct the three-dimensional distribution of radioactive probes in living animals. However, the accuracy of CLT reconstruction is limited by the simplified radiative transfer equation and ill-conditioned inverse problem. To address this issue, we propose a model-based deep learning network that combines the neural network with a model-based approach to enhance the performance of CLT reconstruction. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA), a traditional model-based approach, is expanded into a deep network (known as FISTA-NET). Each layer in the network represents an iteration of the algorithm steps, and connecting these layers can form a deep neural network. In addition, different from the traditional FISTA, the key parameters in FISTA, such as gradient step size and threshold value, can be learned through training data without manual production. To evaluate the performance of FISTA-NET, numerical simulation experiments were conducted, which demonstrate its excellent positioning and shape recovery abilities.Clinical Relevance-This indicates that FISTA-NET strategy can significantly improve the quality of CLT reconstruction, which is further beneficial to the assessment of disease activity and treatment effect based on CLT.

One-dimensional convolutional neural network for Jacobian in Diffuse Optical Tomography.

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint method.Clinical Relevance- These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

Image Reconstruction Using Deep Learning for Near-Infrared Optical Tomography: Generalization Assessment.

Time is one of the most critical factors in preventing brain lesions due to hypoxic ischemia in preterm infants. Since early detection of low oxygenation is vital and the time window for therapy is narrow, near-infrared optical tomography (NIROT) must be able to process the high-dimensional data provided by today's advanced systems in the shortest possible time. Deep learning approaches are attractive because they can exploit such high information density while reducing inference time. The aim of this study was to evaluate the performance of a hybrid convolutional neural network, designed for NIROT image reconstruction and trained on synthetic data. Generalization capability was assessed using measurements on phantoms of a surface topology more divergent than the range of variation in the geometries of the in-silico data, with unseen, non-spherical inclusion shapes, and with source and detector arrangements different from those used for data generation. Substantial gains in speed, localization accuracy, and high image quality were achieved even under the highly varied measurement conditions.

Time Domain Near-Infrared Optical Tomography Utilizing Full Temporal Data: A Simulation Study.

The analysis of full temporal data in time-domain near-infrared optical tomography (TD NIROT) measurements enables valuable information to be obtained about tissue properties with good temporal and spatial resolution. However, the large amount of data obtained is not easy to handle in the image reconstruction. The goal of the project is to employ full-temporal data from a TD NIROT modality. We improved TD data-based 3D image reconstruction and compared the performance with other methods using frequency domain (FD) and temporal moments. The iterative reconstruction algorithm was evaluated in simulations with both noiseless and noisy in-silico data. In the noiseless cases, a superior image quality was achieved by the reconstruction using full temporal data, especially when dealing with inclusions at 20 mm and deeper in the tissue. When noise similar to measured data was present, the quality of the recovered image from full temporal data was no longer superior to the one obtained from the analysis of FD data and temporal moments. This indicates that denoising methods for TD data should be developed. In conclusion, TD data contain richer information and yield better image quality.

Imaging Cerebral Blood Vessels Using Near-Infrared Optical Tomography: A Simulation Study.

Cerebral veins have received increasing attention due to their importance in preoperational planning and the brain oxygenation measurement. There are different modalities to image those vessels, such as magnetic resonance angiography (MRA) and recently, contrast-enhanced (CE) 3D gradient-echo sequences. However, the current techniques have certain disadvantages, i.e., the long examination time, the requirement of contrast agents or inability to measure oxygenation. Near-infrared optical tomography (NIROT) is emerging as a viable new biomedical imaging modality that employs near infrared light (650-950 nm) to image biological tissue. It was proven to easily penetrate the skull and therefore enables the brain vessels to be assessed. NIROT utilizes safe non-ionizing radiation and can be applied in e.g., early detection of neonatal brain injury and ischemic strokes. The aim is to develop non-invasive label-free dynamic time domain (TD) NIROT to image the brain vessels. A simulation study was performed with the software (NIRFAST) which models light propagation in tissue with the finite element method (FEM). Both a simple shape mesh and a real head mesh including all the segmented vessels from MRI images were simulated using both FEM and a hybrid FEM-U-Net network, we were able to visualize the superficial vessels with NIROT with a Root Mean Square Error (RMSE) lower than 0.079.

Myopic Vascular Changes Revealed by Optical Tomography Angiography and Their Association with Myopic Fundus Manifestations.

INTRODUCTION: We aimed to quantify and evaluate fundal vascular changes at different severities of myopia using optical tomography angiography (OCTA) and explore their association with fundus changes captured by ultra-widefield (UWF) fundus cameras. METHODS: Seventy-four participants with myopia were enrolled in the study and underwent basic ophthalmic examination, OCTA, and UWF fundus photography. Multiple parameters were obtained using OCTA (flow area, structure thickness, and vessel density) and UWF fundus cameras (tessellation and parapapillary atrophy [PPA]). RESULTS: The right eye of 30 participants with low and moderate myopia and 44 participants with high myopia (HM) were included. Patients with HM had a larger flow area of the outer retina (FA-OR) and a smaller thickness of choroid (TC). Axial length was significantly correlated with retinal and choroidal flow area and thickness in the different zones. The PPA area was positively correlated with FA-OR and negatively correlated with TC. Tessellation exhibited different levels of correlation with OCTA parameters regarding the flow area, thickness, and vessel density of the fundal layers, mainly in the inner retina. CONCLUSION: FA-OR and TC exhibited sensitive changes in patients with HM and axial elongation; therefore, they could serve as predictive OCTA biomarkers. The PPA and tessellation were connected to the vascular and structural changes revealed by OCTA.

3D autofluorescence imaging of hydronephrosis and renal anatomical structure using cryo-micro-optical sectioning tomography.

Rationale: Mesoscopic visualization of the main anatomical structures of the whole kidney in vivo plays an important role in the pathological diagnosis and exploration of the etiology of hydronephrosis. However, traditional imaging methods cannot achieve whole-kidney imaging with micron resolution under conditions representing in vivo perfusion. Methods: We used in vivo cryofixation (IVCF) to fix acute obstructive hydronephrosis (unilateral ureteral obstruction, UUO), chronic spontaneous hydronephrosis (db/db mice), and their control mouse kidneys for cryo-micro-optical sectioning tomography (cryo-MOST) autofluorescence imaging. We quantitatively assessed the kidney-wide pathological changes in the main anatomical structures, including hydronephrosis, renal subregions, arteries, veins, glomeruli, renal tubules, and peritubular functional capillaries. Results: By comparison with microcomputed tomography imaging, we confirmed that IVCF can maintain the status of the kidney in vivo. Cryo-MOST autofluorescence imaging can display the main renal anatomical structures with a cellular resolution without contrast agents. The hydronephrosis volume reached 26.11 ± 6.00 mm3 and 13.01 ± 3.74 mm3 in 3 days after UUO and in 15-week-old db/db mouse kidneys, respectively. The volume of the cortex and inner stripe of the outer medulla (ISOM) increased while that of the inner medulla (IM) decreased in UUO mouse kidneys. Db/db mice also showed an increase in the volume of the cortex and ISOM volume but no atrophy in the IM. The diameter of the proximal convoluted tubule and proximal straight tubule increased in both UUO and db/db mouse kidneys, indicating that proximal tubules were damaged. However, some renal tubules showed abnormal central bulge highlighting in the UUO mice, but the morphology of renal tubules was normal in the db/db mice, suggesting differences in the pathology and severity of hydronephrosis between the two models. UUO mouse kidneys also showed vascular damage, including segmental artery and vein atrophy and arcuate vein dilation, and the density of peritubular functional capillaries in the cortex and IM was reduced by 37.2% and 49.5%, respectively, suggesting renal hypoxia. In contrast, db/db mouse kidneys showed a normal vascular morphology and peritubular functional capillary density. Finally, we found that the db/db mice displayed vesicoureteral reflux and bladder overactivity, which may be the cause of hydronephrosis formation. Conclusions: We observed and compared main renal structural changes in hydronephrosis under conditions representing in vivo perfusion in UUO, db/db, and control mice through cryo-MOST autofluorescence imaging. The results indicate that cryo-MOST with IVCF can serve as a simple and powerful tool to quantitatively evaluate the in vivo pathological changes in three dimensions, especially the distribution of body fluids in the whole kidney. This method is potentially applicable to the three-dimensional visualization of other tissues, organs, and even the whole body, which may provide new insights into pathological changes in diseases.

Cross-scale tracing of nanoparticles and tumors at the single-cell level using the whole-lung atlas.

Currently, the effectiveness of oncotherapy is limited by tumor heterogeneities, which presents a huge challenge for the development of nanotargeted drug delivery systems (DDSs). Therefore, it is important to resolve the spatiotemporal interactions between tumors and nanoparticles. However, targeting evaluation has been limited by particle visualization due to the gap between whole-organ scale and subcellular precision. Here, a high-precision three-dimensional (3D) visualization of tumor structure based on the micro-optical sectioning tomography (MOST) system and fluorescence MOST (fMOST) system is presented to clarify 3D spatial distribution of nanoparticles within the tumor. We demonstrate that through the MOST/fMOST system, it is possible to reveal multidimensional and cross-scale correlations between the tumor structure and nanoparticle distribution to remodel the tumor microenvironment and explore the structural parameters of vasculature. This visualization methodology provides an accurate assessment of the efficacy, distribution, and targeting efficiency of DDSs for oncotherapy compared to available approaches.

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.

Significance: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired. Aim: We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions. Approach: We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis. Results: The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features. Conclusions: The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

Application of fluorescence micro-optical sectioning tomography in the cerebrovasculature and applicable vascular labeling methods.

Fluorescence micro-optical sectioning tomography (fMOST) is a three-dimensional (3d) imaging method at the mesoscopic level. The whole-brain of mice can be imaged at a high resolution of 0.32 × 0.32 × 1.00 µm3. It is useful for revealing the fine morphology of intact organ tissue, even for positioning the single vessel connected with a complicated vascular network across different brain regions in the whole mouse brain. Featuring its 3d visualization of whole-brain cross-scale connections, fMOST has a vast potential to decipher brain function and diseases. This article begins with the background of fMOST technology including a widespread 3D imaging methods comparison and the basic technical principal illustration, followed by the application of fMOST in cerebrovascular research and relevant vascular labeling techniques applicable to different scenarios.

Single-exposure ultrasound-modulated optical tomography with a quaternary phase encoded mask.

Ultrasound-modulated optical tomography (UOT) is a deep-tissue imaging modality that provides optical contrast with acoustic resolution. Among existing implementations, camera-based UOT improves modulation depth through parallel detection but suffers from a low camera frame rate. The condition prohibits this technique from being applied to in vivo applications where speckles decorrelate on a time scale of 1 ms or less. To overcome this challenge, we developed single-exposure camera-based UOT by employing a quaternary phase encoded mask (QPEM). As a proof of concept, we demonstrated imaging of an absorptive target buried inside a dynamic scattering medium with a speckle correlation time as short as 0.49 ms, typical of living biological tissues. Benefiting from the QPEM-enabled single-exposure wavefront measurement (5.5 ms) and GPU-assisted wavefront reconstruction (0.97 ms), the point scanning and result update speed can reach up to 150 Hz. We envision that the QPEM-enabled single-exposure scheme paves the way for in vivo UOT imaging, which holds promise for a variety of medical and biological applications.