Photoacoustics

This study presents the use of sub-micron nanobubbles (NBs) as contrast agents for ultrasound localization microscopy (ULM), a super-resolution imaging technique that visualizes microvascular structure and flow beyond the acoustic diffraction limit. While ULM has traditionally relied on micron-sized microbubbles (MBs), the reduced dimensions and prolonged circulation times of NBs make them attractive candidates for localization-based imaging. However, their weaker acoustic responses present significant challenges for reliable detection and tracking. To address this challenge, we developed the ULM Master GUI, an interactive framework for optimization of the complete ULM processing pipeline. Using custom ultrasound-compatible wall-less gelatin flow phantoms containing vessel-mimicking channels and bifurcations ranging from 100 to 500 m, we demonstrate that NB-based ULM achieves velocity reconstruction and flow partitioning measurements comparable to conventional MB-based ULM. Across all investigated geometries, NBs faithfully reproduced the underlying flow patterns and hemodynamic behavior despite their substantially reduced acoustic scattering. These findings establish the feasibility of NB-based ULM, expand the range of contrast agents available for localization microscopy, and provide a foundation for future super-resolution ultrasound imaging using nanoscale acoustic contrast agents. The ULM processing GUI is publicly available at https://github.com/grisha1998/ulm-super-resolution-toolbox.

Fluorescence imaging in the second near-infrared window (NIR-II, 950-1700 nm) offers reduced scattering, lower autofluorescence, and deeper tissue penetration than NIR-I imaging, but its adoption is limited by the need for custom-synthesized fluorophores. Here, we identify commercially available dyes that exhibit usable NIR-II emission. Eleven visible, far-red, and NIR-I fluorophores were screened under twelve acquisition configurations combining 670, 760, and 808 nm excitation with band-pass (950 nm, 1400 nm) or long-pass (1000 nm, 1250 nm) emission filters. Output varied markedly with fluorophore identity and excitation/emission configuration. Among hydrophobic dyes, DiR exhibited strong emission across almost all excitation and emission filters. Among hydrophilic dyes, strong NIR-II fluorescence was observed for IRDye 680RD (excitation at 670 nm), sulfo-cyanine 7 (excitation at 670 nm and 760 nm), and indocyanine green (excitation at 808 nm). DiR showed a linear concentration-response under 760 nm excitation with BP1400 detection. Upon encapsulation in PEGylated liposomes, strong NIR-II fluorescence was retained. In an in vivo study in mice, NIR-II resolved vasculature that NIR-I could not consistently delineate, and enabled pharmacokinetic analysis. Both windows returned similar ex vivo organ distributions. NIR-II imaging is therefore accessible using commercial off-the-shelf fluorophores, provided the dye is matched to the intended excitation/emission configuration.

Objective: This study aims to develop and validate a quantitative, depth-resolved anisotropy imaging framework that extends ARFI-based focal degree-of-anisotropy (DoA) estimation into two-dimensional mapping by modeling the depth-dependent relationship between shear modulus ratio (SMR) and peak displacement ratio (PDR). Methods: We propose APRIL (Adaptive Polynomial Regression for anisotropy Imaging via ARFI-induced DispLacements), a framework for quantitative, depth-resolved DoA imaging that adaptively selects polynomial regression or shape-preserving spline interpolation based on excitation PSF asymmetry. Training data were generated using an LS-DYNA3D + Field II simulation pipeline in homogeneous transversely isotropic media (SMR 0.9-4.9). Testing included shifted SMRs under varied acoustic conditions and three heterogeneous inclusion configurations (anisotropic inclusion in isotropic background and vice versa). Experimental validation was performed in an in-vivo murine tumor model over the time, ex-vivo chicken breast, and tissue-mimicking gelatin phantoms, using a Verasonics system with an L11-5v transducer. Results: APRIL achieved depth-resolved SMR prediction errors below 9% over 10-30 mm, with highest accuracy in the focal region (MAE 2.3%, RMSE < 0.1) and stable performance across PSF transition zones. In heterogeneous phantoms, it reconstructed anisotropy maps with SSIM up to 86% and MPE below 7%, accurately delineating inclusion boundaries. Under acoustic parameter variations, mean absolute errors remained below 10%, demonstrating robustness to system and tissue heterogeneity. Conclusion: APRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates. Significance: The method provides a physically grounded and generalizable framework for clinically viable anisotropy biomarkers in muscle, tendon, kidney, tumor and breast tissues.

Photoacoustic (PA) and ultrasound (US) imaging provide complementary molecular, functional, and anatomical contrasts. Here, we present a panoramic PA-US imaging platform that integrates multispectral PA computed tomography (PACT) along with reflection-mode and transmission-mode US imaging through a single shared full-ring ultrasound array. We employ an ultrafast planewave transmission scheme in reflection-mode US for power Doppler (PWD) imaging and ultrasound localization microscopy (ULM). Additionally, we use the transmission-mode US to reconstruct a spatially resolved speed of sound (SoS) map that corrects both PA and US reconstruction. Such correction sharpens the resolution of PACT, suppresses the artifacts of PWD, and improves microbubble localization of ULM. Elevational scanning further enables whole-body volumetric imaging with co-registered PA and US contrasts. The integrated system maps photoswitchable DrBphP1-expressing tumors alongside their blood perfusion and oxygenation environment. Applying the platform to monitor unilateral renal ischemia-reperfusion injury, we report that microvascular perfusion and renal oxygenation recover at different rates. Collectively, we demonstrate that the integrated PA-US imaging platform provides a unified framework for multiparametric study of anatomy, perfusion, microvascular flow, oxygenation, and molecular activities.

Urinary stone disease is a common urological condition with increasing incidence, particularly in developed countries. Laser lithotripsy (LL) has become a preferred minimally invasive treatment due to its high precision and low tissue damage. Recent studies suggest that cavitation plays a critical role in stone damage during LL, and three-dimensional passive cavitation mapping (3D-PCM) has emerged as a promising tool for detecting these events. However, clinical translation of 3D-PCM remains challenging due to limitations in imaging depth, field of view (FOV), and procedural compatibility. Here, we present a large-FOV dual-modality imaging system (3D-PCM and B-mode ultrasound) based on a large-aperture planar ultrasound array. Through array optimization and model-based reconstruction, our system achieves an expanded FOV of ~40*40mm^2 at a clinically relevant imaging depth of ~110mm, while maintaining high spatial resolution of ~0.6 mm laterally and ~0.4 mm axially. In vivo experiments in a porcine model demonstrate that the reconstructed cavitation distribution correlates well with stone damage. Our technology has the potential to provide real-time treatment feedback during LL without disrupting the standard workflow.

SignificanceDiffuse Correlation Spectroscopy (DCS) is an established technique for non-invasive monitoring of Cerebral Blood Flow (CBF), but existing applications primarily measure CBF changes averaged over cortical tissue volumes. A method capable of targeting blood flow within a particular intracranial artery would expand the utility of DCS for vessel-specific cerebral perfusion monitoring and continuous bedside assessment. AimWe aim to investigate the feasibility of targeted, non-invasive monitoring of blood flow in the Anterior Cerebral Artery (ACA) using DCS through optimization of probe geometry and placement. ApproachA custom-built DCS system operating at 785 nm was used to probe ACA from the glabellar region. Source-detector (SD) separation, probe orientation, and probe location were systematically optimized using lower-limb motor tasks and a mental arithmetic task. The optimized configuration was evaluated using an ACA-mimicking multilayer phantom and validated in forty healthy volunteers during lower-limb activation tasks and postural changes. ResultsAn SD separation of 17 mm, vertical probe orientation, and glabellar placement provided the highest sensitivity to ACA-related blood flow changes. Phantom experiments demonstrated sensitivity to flow changes in a vessel located 45 mm beneath the scalp and showed that the frontal sinus, cerebrospinal fluid, and the absence of cortical tissue beneath the glabella along the longitudinal fissure together provide the best optical window for probing deep ACA flow. Using the optimized probe configuration, significant increases in relative CBF were observed during standing leg marching (66.4 {+/-} 38.6%) and supine leg crunches (39.4 {+/-} 32.2%) (p < 0.01), with consistent responses during supine-to-stand postural transitions. ConclusionThe proposed DCS approach enables targeted measurement of blood flow within the ACA territory. This technique provides a framework for continuous, non-invasive monitoring of ACA perfusion and has potential applications in cerebrovascular monitoring and stroke care.

Viscoelastic characterization of tissue has significant diagnostic value in oncology, as tumor progression alters both elasticity and viscosity in ways that neither property alone can fully capture. Existing acoustic radiation force (ARF)-based methods such as Viscoelastic Response (VisR) ultrasound estimate relative elasticity and viscosity through per-A-line nonlinear model fitting, which is computationally intensive and requires auxiliary simulations to correct elasticity-dependent bias. This work presents VESTA (Machine Learning-Enabled Estimation of ViscoElastic Ratios from On-Axis Spatio-Temporal ARFI Features), a two-stage data-driven pipeline that predicts elasticity ratio (ER) and viscosity ratio (VR) directly from seven normalized ARFI displacement features at the A-line level, without model fitting or compensation. Stage~1 is an MLP classifier that detects inclusion boundaries from normalized peak displacement and negative peak velocity ratios; Stage~2 is a dilated Conv1D regression model that estimates ER and VR along the full axial sequence using the predicted mask alongside displacement features. The pipeline was trained on 500 simulated inclusion scenarios spanning three geometries, five focal depths, two F-numbers, and a broad range of material contrasts. In silico, mean predicted ER and VR were within 12\% of ground truth across all geometries, with performance best when ER and VR were moderate or decoupled. Experimental validation on a chicken breast phantom demonstrated plausible generalization to real tissue heterogeneity. Applied to an in vivo murine 4T1 breast cancer model, the pipeline tracked treatment-related attenuation of mechanical contrast in paclitaxel-treated tumors relative to controls over a 36-day imaging period, supporting its relevance for tumor monitoring.

Blood clot formation and thrombolysis are dynamic biological processes that play central roles in hemostasis, thrombosis, and thrombolytic therapy. Monitoring clot evolution is challenging, as existing approaches often rely on specialized hardware or complex acquisition protocols. This study presents dense speed-of-sound shift imaging (DSI), a noninvasive ultrasound framework for spatiotemporal monitoring of coagulation and lysis from ultrasound image sequences acquired with a single imaging transducer. DSI estimates interframe displacements using dense optical flow and reconstructs slowness-shift maps by solving a regularized inverse problem, from which relative speed-of-sound (SoS) shifts are derived. Using this approach, we quantified spatially localized acoustic signatures of material solidification, clot formation, and enzymatic clot dissolution across systems of increasing biological complexity, including thermally gelling gelatin, fibrin clots, and porcine and human whole blood. DSI detected composition-dependent clot properties, with fibrinogen primarily affecting SoS shift magnitude and thrombin primarily affecting clotting kinetics. In both porcine and human whole blood, DSI tracked the full transition from rapid clot formation to tPA-mediated thrombolysis, revealing markedly slower lysis kinetics and species-dependent differences in clotting amplitude and stabilization time. Together, these results establish DSI as a simple, ultrasound-based platform for quantitative monitoring of coagulation, thrombolysis, and related biological material transitions.

PurposeIn this work, we aimed to establish a flow phantom for multi-modal PET and spectral CT imaging to improve blood flow quantification. MethodsA modular flow phantom was built with materials compatible with both PET and spectral CT. A peristaltic pump was used to allow for recirculation. Pores were installed through the aorta to allow for tissue exchange between the blood and tissue compartments, and valves were placed in line with the aorta to control the pressure gradient between compartments. We characterized the system using saline bolus experiments, dynamic PET imaging, and iodine-based spectral CT acquisitions. A blood flow (K1) of 1.0 mL/min/mL with a pressure range of approximately 1.0-3.0 psi was targeted. Using compartmental modeling, we estimate K1 across phantom configurations and evaluate the consistency of perfusion-related parameters derived from saline, PET, and spectral CT measurements. ResultsWith the four pore, two valve configuration, target K1 of 1.0 mL/min/mL was achieved with a physiologic pressure range (2.2-3.5 psi) and a pump speed of 150 rpm. Further, the flow phantom was also able to recapitulate K1 across a range of values through adjustable modifications to the phantom configuration. ConclusionsWe present a modular multimodal flow phantom with a tissue-mimicking compartment, vascular tubing with an adjustable number of pores and valves, and 3D-printed components to support tunable exchange between blood-pool and tissue compartments and controlled dynamic perfusion imaging with same-session PET and spectral CT. Such a setup will enable the development of multi-modal approaches for evaluating tissue perfusion.

Quantifying cells within intact three-dimensional biological specimens remains a major challenge, as standard optical and histological techniques are inherently two-dimensional, destructive, or constrained by light scattering. Optical clearing can extend imaging depth but is time-consuming, disruptive to tissue integrity, and often incompatible with downstream analyses, limiting its practical use for routine three-dimensional quantification. X-ray computed tomography can overcome these limitations, yet conventional micro-CT lacks the soft-tissue contrast required for cellular-scale analysis. Here, we introduce an integrated imaging framework in which propagation-based phase-contrast X-ray CT is combined with volumetric nuclear segmentation to enable three-dimensional cell quantification in unstained volumetric tissue. We imaged ex vivo human liver tissue and segmented nuclei throughout the reconstructed volume, extracting quantitative nuclear metrics and spatial organisation metrics, including equivalent diameter, minor-to-major axis ratio and nearest-neighbour distance. We assessed measurement consistency across two non-overlapping volumes of interest and benchmark slice-resolved nuclear metrics against haematoxylin and eosin histology. The resulting high-contrast volumetric datasets preserve tissue context, allowing quantitative measurements to be interpreted alongside surrounding architecture and microstructure. Together, these results show that laboratory phase-contrast X-ray CT supports nucleibased volumetric cell quantification in intact unstained tissue and provides a framework for context-preserving quantitative analysis in three dimensions.

PurposeTo enhance the SNR in MRI within a localized region of interest using a novel interventional wireless RF resonator probe combined with a dual-drive pTx system. MethodsA dual-drive body birdcage coil was operated in a linearly-polarized mode to decouple a passive RF resonator probe from the transmit field while maintaining the resonator in a receive-only coupled mode. The resonator was fabricated using standard microfabrication techniques and tuned to the Larmor frequency of a 3T MRI system. The 10-g specific absorption rate (SAR) distribution was simulated to identify potential hot spots around the resonator prior to heating experiments. To evaluate the interaction between the resonator probe and the linearly-polarized transmit field, SNR and flip-angle distributions were measured in a phantom. In vivo imaging studies were subsequently performed using the resonator probe in conjunction with the linearly-polarized dual-drive birdcage coil. ResultsTemperature measurements demonstrated a normalized temperature increase of less than 0.10{degrees}C, corresponding to a SAR value below 1.21 W/kg. Experimental flip-angle mapping confirmed effective magnetic decoupling of the resonator probe using linearly-polarized dual-drive transmission. An SNR enhancement factor of 1.6 was achieved within the region of interest in phantom experiments. In vivo imaging demonstrated a 2.0 {+/-} 0.2-fold SNR enhancement in the vicinity of the resonator probe. ConclusionA novel interventional approach for localized SNR enhancement in MRI was demonstrated using a wireless RF resonator probe and a dual-drive pTx system. The proposed technique enables local signal enhancement while minimizing transmit-field interactions, thereby facilitating safe interventional MRI and potentially improving image quality and diagnostic performance.

Spectral computed tomography (CT) is a burgeoning quantitative imaging technique with applications in oncologic diagnostics, prognostic prediction, tissue perfusion studies, and treatment follow-up. While normalized iodine concentration values have been correlated with microenvironmental biophysical changes, obtaining accurate iodine concentrations, particularly at low concentrations remains difficult due to varying spectral CT instrumentation performance. Hybrid spectral CT systems, combining multiple spectral CT instrumentation techniques, address these quantitation insufficiencies by increasing spectral separation but have not been evaluated on a clinically analogous platform. We validate a hybrid spectral CT system, comprised of clinical-grade components, acquiring four distinct effective spectra and applying efficient noise-reducing weighting schemes to compare iodine noise and bias against conventional kVp-Switching (kVp-S). Two tube current levels (50, 350 mA) and three duty cycle ratios (33/67, 50/50, 75/25) were implemented to elucidate radiation dose exposure and kVp-S parameterization impact. A standard quality assurance (QA) and patient-derived, abdominal IodinePrint phantom were scanned on the system. The average absolute bias in iodine density images of the QA phantom was comparable across acquisition techniques, below 0.5 mg/mL, while quantitative noise improved by 22% using noise-optimized weighting schemes. In the IodinePrint phantom aorta and pancreas structures, the noise-optimized weighting scheme increased signal-to-noise ratio (SNR) by 1.3x compared to kVp-S alone. These results highlight the increased precision of hybrid, multi-channel spectral CT systems and motivate CT designs that enable robust CT biomarker development.

Ultrathin endoscopy is highly attractive for real-time tissue imaging in narrow and hard-to-reach regions of the body. A single multimode fibre (MMF) is an attractive probe because of its small diameter, flexibility, and diffraction-limited spatial resolution enabled by the large number of transverse modes guided within a single core. Because the distal fibre tip is inaccessible during endoscopy, reflection-mode imaging, in which the same fibre delivers illumination and collects backscattered light, is more practical than transmission-mode imaging. However, image recovery from the resulting speckle pattern is challenging because light undergoes double-pass propagation through the MMF, with mode coupling and dispersion; the backscattered signal is weak, and the camera records intensity only, without phase information. Here, we propose a single-shot reflection-mode MMF imaging framework that combines a reflected real-valued intensity transmission matrix (reflected-RVITM) with an image restoration network. The reflected-RVITM is calibrated using intensity-only measurements, without interferometry or phase retrieval, and provides a physics-guided initial reconstruction from a single backscattered speckle frame. A restoration network then refines this initial reconstruction instead of inverting the raw speckle. Four restoration backbones are evaluated: HPM-Attention-UNet, GAM, MambaIRv2, and CICPNet. On matched datasets, hybrid models outperformed corresponding networks trained to map raw speckle directly to images. For example, HPM-Attention-UNet on MNIST improved mean PCC from 0.572 to 0.944 (+65.1%). Under domain shift, with training only on Fashion-MNIST and tested on unseen CIFAR scenes, hybrid models achieved mean PCC of 0.61-0.65, compared with 0.36-0.50 for direct learning. This framework is further demonstrated using physical objects at the distal fibre tip. These results demonstrate that a reflected-RVITM physics prior combined with a restoration network enables single-shot image recovery after intensity-only calibration, offering a phase-retrieval-free and generalisable route towards minimally invasive reflection-mode MMF endoscopy.

AO_SCPLOWBSTRACTC_SCPLOWEarly tumor detection is critical for improving patient survival and recovery. Clinically, tissue palpation is routinely used to identify regions of abnormal stiffness, a hallmark of many pathological conditions. However, palpation is restricted to anatomically accessible sites and remains highly operator dependent. Here, we introduce a method for real-time quantitative stiffness mapping using an unmodified commercial endoscope, with the goal of enhancing diagnostic capabilities and restoring mechanical feedback during endoscopic procedures. Our approach combines shear wave elastography with speckle imaging and an innovative synchronization strategy that enables the measurement of shear wave propagation using an unmodified commercial endoscope. The resulting wave fields are analyzed with the noise-correlation-inspired (NCI) method[1], providing pixel-wise estimates of shear wave velocity and, consequently, quantitative maps of local tissue stiffness. The method demonstrated robust performance in both benchtop and endoscopic configurations. Validation was achieved on polymer phantoms as well as on ex vivo and in vivo biological tissues, highlighting its potential for minimally invasive biomechanical imaging and real-time tissue characterization.

Minimally invasive surgery is a powerful technique that enables operations deep within the body while minimizing patient trauma and recovery time. Optical endoscopes are key to providing intraoperative vision but still face challenges due to the loss of essential senses, including depth perception and tactile feedback for tissue evaluation. Thus, it is critical to develop endoscopic imaging technologies that can augment operators with critical information. In this work, we explore a prototype multimodal 3D imaging endoscope that integrates volumetric light-field imaging with laser-speckle contrast imaging to simultaneously capture 3D structure and blood-flow information in a clinically relevant form factor.

PurposeTo quantify parasitic losses in ultra-high field (UHF) magnetic resonance imaging (MRI) receive coils and determine how they contribute to the mismatch between numerically predicted and experimentally realized signal-to-noise ratio (SNR), with the goal of guiding receive-array designs toward ultimate intrinsic SNR (uiSNR). MethodsSNR was measured across multiple field strengths (3T, 7T, 10.5T) using commercial and custom-built arrays. To quantify parasitic losses, unloaded-to-loaded quality factor ratio (QR) measurements were performed on representative loop resonators and practical RF coils. Measured losses were combined with single-loop electromagnetic simulations to separate conductor, radiation, and component losses. These bench-derived loss estimates were then incorporated into full-array electromagnetic simulations of a 128-channel receive array to evaluate their impact on predicted intrinsic SNR. ResultsMeasurements across field strengths supported the expected supralinear increase of SNR with B0. QR analysis showed that, at UHF, radiation loss must be excluded from unloaded-Q measurements to avoid overestimating electronic-noise penalties, and that multiple seemingly modest parasitic losses collectively impose substantial SNR degradation. In the 128-channel array, simulations including only conductor and radiation losses predicted 93% of central uiSNR, whereas inclusion of the full measured parasitic-loss budget reduced predicted performance to 78%, in close agreement with the experimentally measured 77%. ConclusionsThe gap between predicted and realized SNR performance of high-channel-count 10.5T loop arrays can be largely explained by parasitic losses that are not captured in conventional simulations. A bench-measurement-informed simulation framework enables more realistic prediction of coil performance and provides practical guidance for optimizing future UHF receive arrays.

Hyperspectral imaging (HSI) has gained increasing attention for bone assessment because it captures rich wavelength dependent information associated with mineralised tissue. HSI provides detailed spectral information related to material composition, while 3D geometric information supports the analysis of surface morphology and structural detail. However, integrating spectral and geometric information remains challenging, particularly when conventional reconstruction pipelines depend on external pose estimation. To address this challenge, we propose BoNeRF-HS, a self-calibrated hyperspectral neural radiance field for 3D reconstruction. BoNeRF-HS jointly optimises camera intrinsics, volume density, and hyperspectral radiance, removing the need for COLMAP based poses. To improve spectral modelling, we incorporate a gated spectral adapter head that learns wavelength dependent radiance features for hyperspectral view synthesis. We evaluate BoNeRF-HS on a multi-view hyperspectral dataset containing mouse bone, trabecular bone analogue, and cortical bone analogue samples. Experimental results demonstrate that our framework achieves improved reconstruction quality, and better preservation of bone surface details compared with existing approaches.

This study reports a brand-new continuous-wave-excited (CW-excited) two-photon fluorescence emission mechanism in indocyanine green (ICG), an organic fluorescent dye widely used in clinical practice. This mechanism is based on the excited state absorption (ESA) process of the first singlet excited state. Intramolecular electrons sequentially absorb two photons to reach a high-energy singlet excited state, followed by direct radiative transition to the ground state to generate fluorescence. The entire process is exclusively mediated by singlet states. We further summarize the essential requirements for organic dyes to realize this luminescence mechanism. First, the dye must possess at least two well-separated singlet excited states with distinct energies, corresponding to two absorption peaks at different wavelengths in the absorption spectrum. The wavelength of the high-energy singlet excited state is about half that of the first singlet excited state. Second, the peak in the absorption spectrum corresponding to the transition from the ground state to the first singlet excited state has a sufficiently large molar extinction coefficient. Third, the first singlet excited state exhibits the capability of ESA. Fourth, electrons at the high-energy singlet excited state can directly transit to the ground state and emit fluorescence. We validated this mechanism in a variety of organic dyes satisfying the above conditions, confirming its universality. Using CW laser as the excitation source, we achieved two-photon fluorescence imaging of mouse cerebral blood vessels at a depth of 400 m, which clearly resolves three-dimensional vascular networks with high resolution. We also performed two-photon fluorescence imaging on human gastric cancer tissue samples at a depth of around 150 m, which provides a low-cost strategy for clinicians to rapidly acquire high-contrast tumor tissue images.

Significance: Continuous, noninvasive, and depth-resolved monitoring of blood-flow-related tissue dynamics remains an important unmet need. Speckle-contrast optical spectroscopy (SCOS), including interferometric implementations such as iSCOS, provides a scalable optical route to blood-flow sensing, but conventional continuous-wave approaches lack intrinsic depth selectivity. Time-of-flight (TOF) gating offers a way to separate superficial and deeper dynamic contributions in layered tissues, such as skin-muscle or scalp-cortex, by resolving photon path lengths. Aim: We introduce a swept-source, single-channel implementation of interferometric speckle-contrast optical spectroscopy (iSCOS) to obtain TOF-resolved temporal speckle contrast, {kappa}^2, from the measured field autocorrelation g_1, and evaluate its feasibility for depth-resolved blood-flow sensing. Approach: A swept-source iNIRS system operating at 780 nm acquired interferometric signals, which were Fourier-transformed along the optical-frequency axis to recover complex TOF-resolved speckle fields. Temporal speckle contrast was then estimated at each TOF gate indirectly from g_1 using the speckle-visibility relation. Diffusion-based numerical simulations were first used to compare the direct variance-based estimator and the indirect g_1-based estimator under varying reduced scattering coefficient, diffusion coefficient, additive noise level, and bi-layer geometry. Because the simulations showed that the g_1-derived {kappa}^2 estimator was substantially less sensitive to additive noise than the direct estimator, this estimator was used for the main phantom and in vivo analyses, while the direct estimator served as a simulation comparator. The g_1-derived estimator was then applied to liquid and bi-layer phantoms, followed by proof-of-concept in vivo measurements on the human forearm during cuff occlusion and on the forehead during a Sudoku task. Results: TOF-resolved kappa2 curves recovered with the g_1-derived estimator matched DWS theory across scattering coefficients, photon path lengths, and exposure times. The estimator preserved theoretical accuracy for additive noise amplitudes up to 50% of the field amplitude, whereas the direct variance estimator showed substantial noise-induced bias and required correction. Bi-layer simulations and phantom experiments reproduced the predicted direction and onset of TOF-dependent decorrelation-rate trends in layered media. In vivo, the recovered blood-flow index tracked the expected TOF-dependent cuff-occlusion and reactive-hyperemia response in the forearm. During the single-subject Sudoku task, the left-forehead recording showed a TOF-dependent relative blood-flow-index increase of +0.8 {+/-} 1.9% at TOF = 400 ps, +9.8 {+/-} 2.2% at TOF = 600 ps, and +15.2 {+/-} 5.6% at TOF = 800 ps. This pattern is consistent with increased sensitivity to deeper tissue at longer photon path lengths, but requires cohort-level validation before quantitative interpretation as cognitive activation. Conclusions: Coupling temporal speckle-contrast analysis with swept-source iNIRS yields a proof-of-concept, depth-resolved platform for blood-flow sensing. By estimating TOF-resolved speckle contrast through the g_1-derived {kappa}^2 route, TOF-iSCOS suppresses additive-noise bias while preserving sensitivity to deeper dynamic tissue layers. The present single-channel results bridge continuous-wave iSCOS, interferometric NIRS and time-domain diffuse correlation spectroscopy (TD-DCS), and motivate future multi-channel and cohort studies for scalable cortical hemodynamic monitoring.

Continuous wave diffuse correlation spectroscopy (cw-DCS) is a noninvasive optical technique to monitor the tissues blood flow changes. This technique measures the tissue blood flow index (BFI) by evaluating the decay rate of the autocorrelation function. The derived BFI is proportional to mean squared displacements of the red blood cells considered as the fast-dynamic scatterer component of tissue in time. However, biological tissue contains static scatterer component and slow-dynamic scatterer component which affect the decay rate of autocorrelation function and as a result the derived BFI. In this study, we assessed the fractional contribution of static, slow-dynamic and fast-dynamic scatterer components of a medium in the flow index derived by cw-DCS. The measurements performed on Agar-based phantom with tube showed that presence of static scatterer component and slow-dynamic scatterer component led to substantial underestimation ({approx} 123%) of the flow index derived by Siegert relation, compared to effective diffusion coefficient of fast-dynamic scatterers components derived by modified Siegert relation and bi-exponential model. The less underestimation was observed for the corresponding parameters obtained from the liquid phantom measurements ({approx} 25%) as well as during the forearm occlusion test and respiratory challenges ({approx} 16% - 26%).