Photoacoustics

Gas-bubble-enhanced focused ultrasound (FUSGB) is advancing clinically across a myriad of applications, including targeted drug delivery through transient blood-brain barrier opening (BBBO). Its broader translation requires a better understanding of the bioeffects that enable reproducible enhancement of barrier permeability while avoiding vascular injury, underscoring the need to clarify the relationship between acoustic exposure, bubble activity, and the biological response central to treatment optimization. These interactions are difficult to isolate in vivo, and acoustic reflections, geometry, and field distortion in conventional cultureware hinder quantitative in vitro studies. Herein, we present a theoretically and experimentally characterized ultrasound-transparent organ-on-chip device with >99.9% US transparency that enables quantitative in vitro modeling of the FUSGB procedure and study of BBBO. Furthermore, submicron bubbles (sMB, ~800nm) were fabricated and characterized. Acoustic dose-dependent subharmonic and broadband cavitation dose curves (SCD and BCD), as well as temporal responses, were studied. A 3-day accelerated Caco--2 surrogate barrier with a high TEER (~1200xcm2) was used to demonstrate US dose-dependent targeted barrier opening, visualized using an in vitro Evans Blue assay. Together, this study and its findings establish a foundation for quantitative in vitro modeling of FUS BBBO, as well as other US interactions in microphysiological systems, supporting systematic evaluation of procedural regimens, large-parameter-space optimization, acoustic safety window, and exposure conditions for brain drug delivery, and additionally enabling mechanistic insight into FUSGB BBBO.

The Transport-of-Intensity Equation (TIE) and polarization differential phase contrast (pDPC) exhibit transfer characteristics that limit their use as standalone quantitative phase imaging modalities. TIE provides stable recovery of low spatial frequencies but attenuates fine structure, while pDPC enhances phase gradients but is intrinsically insensitive near zero frequency. We present a hybrid reconstruction framework that combines a single-shot pDPC measurement with an in-focus-defocused TIE image pair and fuses the resulting phase estimates in the Fourier domain using frequency-selective weighting. The illumination geometry is co-designed to enforce angular separation between modalities, enabling high-frequency phase-gradient acquisition in a single exposure while preserving low-frequency sensitivity through low-numerical-aperture symmetric illumination. The proposed method requires three intensity measurements without requiring interferometric stability or sequential asymmetric illumination. Experimental results demonstrate improved phase fidelity across spatial frequencies, reduced halo artifacts, and enhanced recovery of both smooth and fine structural features. This approach provides a compact and robust method that moves towards extended spatial-frequency phase imaging for dynamic and resource-constrained applications.

The lack of non-invasive, quantitative methods to track the location and proliferation of transplanted cells hampers the clinical translation of cardiac regenerative therapies. Here, we establish a PET reporter for tracking human induced pluripotent stem cell (hiPSC)-derived cardiac cells across complementary two-dimensional (2D) and three-dimensional (3D) cardiac tissue models. Methods hiPSCs were genetically engineered using CRISPR-Cas9 to express a human-derived, anticalin-based, bioorthogonal PET reporter (DTPA-R) that specifically binds radiolabeled DTPA-metal complexes. The engineered hiPSCs were differentiated into ventricular progenitors (HVPs) and cardiomyocytes (CMs). Stability of reporter gene expression and its effects on cellular differentiation and function were evaluated. Uptake of [18F]F-DTPA*Tb by DTPA-R-positive and -negative HVPs was analyzed in vitro, and DTPA-R HVPs were evaluated in ex vivo cultured porcine myocardial slices with and without radiofrequency ablation (RFA) injury. Phantom studies were performed to evaluate PET detectability under clinically relevant large-animal and human imaging conditions and to assess how activity distribution affects signal detection in relation to DTPA-R cardiac cell-equivalent numbers. Results DTPA-R expression was maintained during the differentiation stages of hiPSC-derived cardiac cells and had no measurable impact on migration, differentiation, and functional integration. PET imaging enabled the specific detection of DTPA-R HVPs in intact and injured myocardial slices. At the initial phantom scan, the measured activity corresponded to cell-equivalent numbers of approximately 3.2 x 10^6 HVPs or 5.5 x 10^6 CMs. The compact 200 uL source remained clearly detectable at 6 h, whereas the more dispersed 1,000 uL source progressively approached background activity, indicating that PET detectability depended strongly on local activity concentration and distribution. Conclusion This study establishes a bioorthogonal PET reporter gene platform for quantitative imaging of hiPSC-derived cardiac cells, with no measurable adverse effects on the biological parameters examined, and provides a methodological foundation for future translational and in vivo imaging studies. The approach may also be applicable to other hiPSC-derived cell products beyond cardiac cell lineages.

Purpose: To evaluate, within a synthetic pulmonary-artery benchmark, whether biomarker-aware super-resolution improves hemodynamic endpoint fidelity in 4D flow MRI. Methods: A 3D residual channel attention network (RCAN) was trained on 22 CFD simulations from 11 pulmonary-artery geometries with 2x spatial degradation. The original primary comparison evaluated full biomarker-aware training against a combined divergence-temporal-regularization control across 14 matched seeds on a fixed six-case test set. Additional matched analyses subsequently separated divergence and adjacent-frame temporal-difference regularization and evaluated biomarker supervision in a controlled 2x2 design with a common reconstruction schedule. Results: In the original primary comparison, biomarker-aware training reduced PI error relative to the matched combined-regularization control (12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015). In the matched factorial analysis, biomarker supervision reduced PI error without (-2.235 percentage points [95% CI -3.514, -0.849]) and with (-4.125 percentage points [95% CI -5.392, -2.732]) combined regularization. PI improvement was not mirrored by uniformly improved reconstruction metrics: PSNR changed by -0.058 (p = 0.50) and +0.144 dB (p = 0.28), while SSIM decreased by 0.009 (p = 0.004) and 0.007 (p = 0.009), respectively. Divergence alone changed PI by +0.034 percentage points [95% CI -0.705, 0.809] while reducing DivRMS by 1.869 percentage points; temporal-difference regularization increased PI error by 1.562 percentage points [95% CI 0.594, 2.439]. Conclusion: Within this synthetic, fixed-case RCAN benchmark, biomarker supervision improved PI under both regularization conditions. PI improvement was not mirrored by uniformly improved reconstruction metrics. Temporal-difference - not divergence - regularization was the only isolated component associated with reproducible PI deterioration. These findings are configuration-specific and require geometry-independent and in vivo validation.

Objectives: Intraoperative ultrasound (IOUS) is an increasingly valuable real-time imaging modality in neurosurgery, enabling visualization of brain parenchyma, tumor margins, ventricular anatomy, and vascular structures during cranial procedures. Despite its clinical relevance, the development of artificial intelligence (AI)-assisted interpretation tools for neurosurgical IOUS has been limited by the scarcity of large, annotated datasets. Manual pixel-level annotation of ultrasound video frames is labor-intensive, requires specialized neuroanatomical expertise, and scales poorly. To address this bottleneck, we developed a proof-of-concept interactive semi-automated annotation pipeline designed to explore the feasibility of scalable, expert-guided labeling for intraoperative neurosurgical IOUS. Methods: We implemented the system as a napari-based graphical interface coupled with a scikit-learn Random Forest (RF) pixel classifier. Real intraoperative neurosurgical ultrasound videos from five craniotomy cases were used as training material. A neurosurgical expert annotated a minimal set of representative frames using brush-based labeling to define anatomical classes of interest. For each labeled pixel, a feature vector was computed comprising raw intensity, multi-scale Gaussian filtering ({sigma} = 1, 2, 3), Laplacian of Gaussian, and Sobel edge magnitude. The RF classifier was trained on accumulated annotations using class-balanced weighting and out-of-bag (OOB) validation, then applied across full video sequences. An incremental accumulator module enabled multi-case training. Segmentation outputs were exported as TIFF overlays for expert quality review. Results: Across five intraoperative cases, sparse expert annotation of a small number of representative frames per case was sufficient to train case-specific Random Forest classifiers that propagated labels across the full video sequences. Out-of-bag estimation served as an internal coherence check during training, and the classifier additionally produced per-class probability maps that exposed regions of uncertainty at acoustic boundaries. On expert qualitative review, the auto-segmented frames were anatomically coherent, with the classifier reliably distinguishing anechoic fluid compartments from echogenic tissue interfaces; residual misclassification was concentrated in low-contrast boundary regions. Conclusions: This proof-of-concept study demonstrates that semi-automated, expert-guided annotation of intraoperative neurosurgical ultrasound is technically feasible using classical machine learning. By combining neurosurgical domain expertise with an interactive, incremental annotation workflow applied to real intraoperative acquisitions, this framework represents a foundational step toward scalable generation of labeled datasets for neurosurgical IOUS. Future directions include formal multi-rater validation, expanded multi-class anatomical labeling, and eventual migration to deep learning architectures as annotated datasets grow.

Optical coherence tomography angiography (OCTA) has been extensively explored for the detection of pathological retinal changes arising in various ophthalmological diseases. Due to phase instability of conventional systems, most OCTA signal processing methods to date have relied on intensity-based averaging, despite the resulting lower contrast and dynamic range compared to coherent averaging approaches. In this work, we (1) present a novel computation pipeline that enables fully-coherent processing in any phase-stable or phase-unstable system, and (2) demonstrate the first angiography metric with integrated signal-to-noise ratio (SNR) correction and coherent spatiotemporal ensemble averaging effective in any OCT system. Our method does not rely on 2D en face projections or binarization to produce high-contrast en face views and it significantly enhances the contrast-to-noise ratio of computed OCTA volumes, particularly in regions with reduced SNR levels. Our OCTA volumes also have high cross-sectional quality, enabling the use of single-depth en face views and the localization of capillary paths in the axial dimension. We can now identify capillaries below the retinal pigment epithelium, near the lamina cribrosa in the optic cup, which was not previously possible due to low SNR. We also conducted an analysis of strategies for bias reduction in OCTA and developed the optimal method utilizing coherent spatiotemporal ensemble averaging. Because our method leverages coherent averaging across all dimensions, it provides flexibility in shaping the averaging kernel to target specific workflows. The value of this additional degree of freedom is illustrated in our vessel tracing investigations, in which the depth resolution was maximized by limiting coherent ensemble averaging to the two lateral dimensions even in presence of strong phase noise, enabling the identification of 3D features such as overlapping capillary paths.

Medical ultrasound is a portable, non-invasive, and cost effective imaging modality that is particularly well suited for resource-limited settings. Optic nerve sheath diameter (ONSD) measurement from ultrasound is used as a point-of-care assessment tool for intracranial pressure (ICP), which is associated with several neurological conditions. However, valid measurement depends on selecting a frame in which the optic nerve sheath is clearly visible. Manual selection requires a high level of expertise. To enable the use of ONSD in low resource settings by medical personnel of all levels, we present a sparsely supervised AI framework that scores each frame of an ONSD ultrasound video and selects the optimal frame for ONSD assessment. Per-frame embeddings from an ultrasound foundation model (USFM) are passed to a lightweight temporal head and trained using Gaussian soft labels, which assign graded targets around labeled key frames, rather than hard binary (0/1) per-frame targets. We evaluate the model performance using subject-level cross-validation on 18 subjects and 323 ultrasound videos spanning nine acquisition sweep types. Top-k frame selection is used as a metric for direct comparison against the baselines. While only requiring sparse labels, our method can identify an optimal frame in 82.2% of the evaluated sweeps, exceeding the strongest training-free baseline (49.2%) and hard-label classification (71.9%).

To evaluate whether eight-bin deep-silicon photon-counting CT can quantify iodine and gadolinium separately and distinguish radiopaque embolic beads from vascular contrast in tumor phantoms. In this phantom study, two-input decomposition of scanner-generated iodine and water images was compared with three- and five-material decomposition of five reconstructed energy-bin images in 56 common test vials, including nine simulated-blood preparations. One tantalum and one bismuth vial were also tested with five-material decomposition. A separately calibrated two-input method using scanner-generated 70-keV and iodine images was applied to four static 3D-printed tumor vascular models: iodine-only and gadolinium-only controls and two models with iodine-containing beads and gadolinium-filled channels. Concentration errors were summarized by bias and root mean square error (RMSE). For iodine, RMSE was 0.40, 0.73, and 1.12 mg I/mL with two-input, three-material, and five-material decomposition, respectively; corresponding gadolinium RMSEs were 0.57, 0.55, and 0.46 mg Gd/mL. Two-input biases were +0.01 mg I/mL and +0.29 mg Gd/mL. Five-material decomposition distinguished tantalum and bismuth in the separate test vials. Beads and vascular gadolinium were spatially separated in the tumor models. In the two bead-containing models, gadolinium measured 32.56 {+/-} 8.02 and 37.02 {+/-} 7.93 mg Gd/mL (mean {+/-} spatial SD), compared with the prepared 35.00 mg Gd/mL. Deep-silicon photon-counting CT separately visualized radiopaque embolic beads and quantified vascular gadolinium from a single acquisition. Recalibrated scanner-generated images supported quantitative dual-contrast imaging, while research energy-bin decomposition distinguished additional materials.

Abstract: Purpose: Superparamagnetic iron oxide nanoparticles (SPION) are widely used as MRI contrast agents for cell tracking and molecular imaging. However, their detection using conventional T2/T2* weighted and susceptibility-weighted MRI relies on negative contrast (signal voids), which lacks specificity because similar hypointense signals can arise from other sources of magnetic susceptibility. This study introduces Frequency Shift Imaging (FSI), a positive-contrast technique based on an adiabatic Shinnar-Le Roux (SLR) self-refocused pulse that addresses the limited specificity of conventional SPION imaging. Methods: An adiabatic self-refocused RF pulse was designed using the SLR algorithm for frequency-selective positive-contrast imaging. The optimized pulse had a spectral bandwidth of 370 Hz, a peak RF amplitude of 14 T, and a duration of 15 ms. FSI data were acquired on a 7T MRI scanner. Phantom experiments used agarose-embedded 30 nm cell-labeled SPION at 0%, 25%, 75%, and 100% concentrations (pure). In vivo validation was performed following intramuscular SPION injection into the hind limb of a mouse. FSI images were compared with conventional acquisitions. Results: Quantitative phantom analysis showed normalized FSI signal intensities of 25.02% and 75.16% for samples containing 25% and 75% labeled cells, respectively, demonstrating an approximately proportional relationship between FSI signal and labeled-cell concentration. In vivo, FSI generated localized hyperintense positive contrast at the SPIO injection site, providing improved conspicuity and localization compared with the corresponding negative-contrast signal void. Discussion and Conclusion: FSI enables robust, B1-insensitive, positive-contrast SPION imaging at 7T with reduced echo time and SAR compared to paired adiabatic approaches, supporting its potential for quantitative cell tracking and molecular imaging applications.

Functional ultrasound (fUS) imaging provides deep, wide-field access to brain function, but instrument cost and complex acquisition and analysis workflows restrict its wider adoption. Here we introduce @fUS, an agentic transcranial imaging platform with an open architecture that integrates purpose-built hardware with executable experimental skills and persistent structured memory. The system combines 128-channel, 16-bit acquisition at 125 MHz with GPU-based processing and enables functional imaging through the intact scalp and skull of mice, with approximately 100-m spatial resolution and 10-Hz temporal sampling, providing a basis for longitudinal studies without cranial-window surgery. Its agent connects scientific objectives to experimental design, direct instrument control and data analysis, retaining context across interactions and supporting inspectable workflows through text and hands-free voice control. Neuroscience trainees with limited engineering experience independently configured the system within 30 min. Agent-assisted exploration of whisker-stimulation datasets revealed low-frequency vascular changes beyond conventional response mapping, supported by complementary two-photon measurements of single-vessel diameter. By combining transcranial imaging with accessible instrument control and analysis, @fUS provides a foundation for wider adoption and larger, more diverse neuroscience datasets. More broadly, it offers a framework for scientific instruments in which measurement, computation and experimental reasoning are developed as parts of the same system.

Abstract Background Advanced in vitro models of the human heart have become promising tools for studying cardiac diseases and drug discovery. Three-dimensional (3D) engineered heart tissues (EHTs) provide more adult cardiac tissue-like structural organization compared to traditional two-dimensional (2D) in vitro models with straightforward contractile readouts. However, the assessment of the architecture of EHTs, especially the quantification of cellular and tissue structural components, has remained poorly studied. Here we demonstrate the use of light-sheet microscopy for structural imaging of intact EHTs and provide quantification of the EHT cellular and subcellular structures. Methods We developed an in-house inverted selective plane illumination microscopy (iSPIM) platform and combined it with iterative indirect immunofluorescence (IT-IF) staining for better signal acquisition. We developed an image-processing and analysis pipeline dedicated to volumetric EHT analysis. Results With this integrated platform, we achieved volumetric imaging of intact EHTs and quantification of 3D tissue architecture, revealing aligned cardiomyocytes within the imaged volume and increasing cardiac nuclei content towards the core of the tissue. The developed methods allowed the detection of heterogeneities between tissue sections, demonstrating the sensitivity required for future disease modeling or maturation studies. Conclusions The developed integrated platform opens new possibilities for volumetric analysis and characterization of 3D tissue structure using iSPIM, providing new insights into EHT architecture and the use of EHTs as disease modeling tools.

Accurate breast lesion segmentation in ultrasound is difficult because speckle noise, heterogeneous lesion appearance, and poorly defined margins can obscure clinically relevant boundaries. This work presents UGB-SegNet, an uncertainty-guided breast ultrasound segmentation network that combines four components: multi-scale convolutional block attention (CBAM) at three EfficientNet-B0 encoder stages, a learnable feature pyramid network for adaptive multi-scale fusion, an entropy-guided decoder that routes fine-grained encoder information toward uncertain regions, and a boundary-aware composite loss with deep supervision. The model was evaluated on the public BUSI dataset (647 annotated lesion images) using a stratified 454/64/129 train/validation/test split. On the held-out test set, UGB-SegNet achieved 81.30% Dice, 68.49% IoU, 81.71% sensitivity, 98.14% specificity, and 15.63-pixel HD95. Under identical training conditions it outperformed five directly trained baselines: U-Net, Attention U-Net, SegNet, DeepLabV3+, and TransUNet. A five-fold resampling experiment over the development set with the held-out test set fixed yielded a mean Dice of 76.36% +/- 1.66% (95% CI 74.90-77.81). Reported ablations attribute gains of +2.06 Dice points to multi-scale CBAM, +1.52 to learnable FPN fusion, +1.22 to the uncertainty-guided decoder, and +1.24 to boundary-aware training. UGB-SegNet contains 14.00M parameters and runs at 62.4 FPS at 224 x 224 resolution on the tested NVIDIA GPU, providing a compact accuracy-efficiency trade-off for future prospective clinical validation.

Objective: To develop and validate a dual-probe Diffuse Correlation Spectroscopy (DCS) system for non-invasive and simultaneous, monitoring of cerebral blood flow (CBF) in the bilateral Middle Cerebral Artery (MCA) territories, and expanding the utility of conventional DCS limited to cortical-volume-based CBF measurements to vessel-specific cerebral perfusion monitoring. Methods: A dual-probe DCS system was designed for non-invasive monitoring of MCA-specific perfusion. Probe placement and protocol optimization study has been performed using anatomical landmarks, motor and speech activation tasks in healthy volunteers. System stability and repeatability were further evaluated in a pilot cohort of 30 healthy (age, 25{+/-}7 years) participants using optimized probe position and protocol. A bilateral MCA ischemic Lacunar Infract stroke case report also validated the feasibility of the system in clinical settings. Results: Measurements demonstrated superior sensitivity towards MCA-territory perfusion at targeted probe locations compared to off-MCA positions. In pilot cohort, significant increase of 30.34 {+/-} 21.56% and 36.48 {+/-} 21.22% in rCBF corresponding to hand squeeze and speech task respectively showed reproducible physiological responsiveness of the system (p<0.001). Measurement done on a patient with bilateral MCA ischemic Lacunar Infract stroke showed a significant change of 30% during speech for both the MCAs but no significant change is observed for hand squeeze tasks. Conclusion: The custom built dual-probe DCS system enables non-invasive, operator-independent, targeted and continuous monitoring of rCBF within bilateral MCA territories. Significance: This approach enables the potential use of DCS system for bilateral and vessel-specific monitoring of cerebral perfusion in the MCA territories.

Background and Objective: High-speed and high-resolution retinal imaging using adaptive optics line confocal ophthalmoscopy (AOLCO) resolves individual red blood cells (erythrocytes) flowing through the smallest retinal capillaries, but quantifying their velocity first requires removing linear and nonlinear distortions caused by eye motion that may result in a displacement of tens to hundreds of pixels between frames. We present a computational pipeline that stabilizes AOLCO capillary video and measures erythrocyte velocity in true physical units, delivered as an interactive 3D Slicer extension and a batch command-line tool sharing one analysis core. Methods: Our video analysis pipeline has four stages: preprocessing, registration, postprocessing, and velocity measurement. During preprocessing, illumination is corrected, and closed-eye frames are identified. During registration, each frame is aligned to a reference frame using affine and, when needed, B-Spline registration, initialized by our robust and fast (GPU-based) translation-registration algorithm to accommodate large eye movements (e.g., microsaccades). During postprocessing, adjacent registered frames are differenced to suppress stationary structures, and a 2D projection of the frame-difference video reveals the capillary network. During velocity measurement, an operator manually identifies capillary segments on the 2D projection image. Spatiotemporal images are then generated along these segments from the frame-difference video, and blood-flow velocity curves are obtained from these images using the Radon transform. Results: We validate the velocimetry of the pipeline against synthetic ground-truth flow data, and the experiments show that the velocimetry recovers the speed within an error of 4% The GPU-based translation registration algorithm aligns the large eye motions (~90 um per frame on average) and runs about 3.6 x (100 Hz) to 38 x (400 Hz) faster than the baseline method. Across the full cohort (2344 videos, 57 subjects, 30 Hz to 400 Hz), our registration approach improves structural similarity (SSIM) of the video frames on every acquisition from 0.73 to 0.86. The recovered blood flow velocities are physiologically plausible, of the same order as published AO measurements. Conclusions: Our computational pipeline enables AOLCO velocimetry on the capillary-level.

Significance: Fluorescence imaging remains largely qualitative and device-specific, limiting reproducibility and intersystem comparisons. SI-traceable radiometric characterization requires calibrated sources suitable for transferring known radiance to imaging systems. Aim: Establish and evaluate a compact, calibrated solid-state radiometric emitter target (RET) for SI-traceable radiance transfer, and assess methods for evaluating radiance invariance, imager responsivity, and extension of radiance transfer to digitally defined regions of interest (ROIs). Approach: The radiance invariance of an LED-based RET was evaluated by measuring its radiance across varying source-to-aperture distances and collection apertures. A previously described radiance-transfer formalism based on calibrated source radiance and collection geometry was implemented to determine imager responsivity (R{lambda}), which was evaluated across varying working distances and lens apertures. Grubbs and MAD outlier tests and one-way ANOVA were used to assess radiance and responsivity invariance. Finally, responsivity obtained using digitally defined ROIs was compared with matched physical apertures to assess ROI-based radiance transfer. Results: The measured RET radiance remained invariant across the tested source-to-aperture distances and collection apertures, with no significant differences observed across distance (p = 0.88) or aperture (p = 0.15). Imager responsivity remained stable across working distances for both tested lens configurations (p = 0.48 and p > 0.99). Responsivity was also invariant across lens apertures, except for deviations at f/11 that were consistent with practical aperture tolerances. Digitally defined ROIs reproduced responsivity values obtained using matched physical apertures, demonstrating that the radiance-transfer approach can be extended to image-defined regions. Conclusions: The presented methods provide an approach for evaluating compact LED-based sources for invariant-radiance emission and suitability for SI-traceable radiance transfer. The characterized RET supported stable imager responsivity across changes in imaging geometry and extension to digitally defined ROIs. These methods can be adapted to other LED-based sources, with further angular characterization and uncertainty analysis needed to establish broader applicability.

Purpose: Magnetic Resonance Acoustic Radiation Force Imaging (MR-ARFI) encodes focused ultrasound (FUS)-induced tissue displacements to MR phase shifts. Because these phase shifts are small, optimizing displacement sensitivity and minimizing motion artifacts requires characterization of the temporal dynamics of acoustic radiation force (ARF). Beyond sequence design, such characterization enables MR-ARFI to serve as a probe of brain viscoelastic behavior and can inform the design of therapeutic ultrasound pulsing schemes by quantifying how rapidly tissue deforms and relaxes. Methods: We developed a dynamic model assuming a gamma-variate impulse response for ARF(t), with time constant ({tau}) to be estimated. A single-shot spin echo ARFI acquisition with a bipolar motion encoding gradient (MEG) was implemented, incorporating retrospective corrections for head motion and physiological noise. A 500 kHz FUS pulse was applied in on/off blocks while systematically varying the timing between the MEG and sonication. The resulting phase time series were fit to the model to estimate time constant in five human participants. To assess sensitivity to tissue stiffness, {tau} was additionally measured in two customized gelatin phantoms of differing stiffness, characterized independently by MR elastography (MRE). Results: The average measured time constant in human participants was {tau} 3.7 {+/-} 0.27 ms. Comparing the two phantoms, the stiffer phantom exhibited a shorter {tau} (3.0-3.5 ms) than the softer phantom (4.0-4.5 ms), which tracked the MRE-derived Kelvin-Voigt viscoelastic time constants. In addition, the peak ARFI displacement was lower in the stiffer phantom (0.17 vs 0.74 m for the less stiff phantom), and a broader displacement focal spot size (FWHM 22 mm vs 15 mm). Conclusion: This time series approach with motion and physiological noise corrections validates an MR-based technique capable of characterizing the brain's temporal response to sonication-induced mechanical forces, providing a foundation for optimizing the design of ultrasound pulsing schemes for ARFI, as well as for future studies of brain biomechanics.

Purpose: To quantify, in a computational fluid dynamics (CFD) benchmark, how spatial degradation, temporal acquisition and noise change the error of plane-based pulmonary-artery flow endpoints. Methods: Twenty-two historical, non-converged CFD simulations (11 geometry families) were voxelised at 2 mm with 80 frames per cycle. Simulations with numerical divergence were excluded, leaving 13 (7 families). Fields were degraded to 4 and 8 mm and with noise, plane waveforms were sampled at 20 phases with one-interval averaging, and errors were computed against the noise-free reference waveform on 66 geometric planes. Family-mean paired differences are reported with t-based 95% confidence intervals under an analysis plan fixed in advance. Results: Spatial degradation from 2 to 4 mm increased full-waveform nRMSE by 0.0032 [0.0021, 0.0043], in 7 of 7 families; cycle-mean flow bias accounted for 23% of the squared error, against 60% at 8 mm. With SNR scaled to voxel volume, the increase was not resolved (0.0002 [-0.0012, 0.0015]). Twenty-phase acquisition increased PI error (0.0223 [0.0214, 0.0233]) and diastolic-trough timing error (0.63 [0.52, 0.74] frames) more than spatial degradation, and its point estimates for these endpoints were larger with every recovery method; the nRMSE ordering and peak timing depended on the recovery method. The implementation of the downsampling operator mattered: an alternative, corner-aligned implementation of the same 4 mm degradation gave a larger spatial effect (nRMSE difference 0.0010 [0.0002, 0.0017]). Conclusion: In this benchmark, a 2 to 4 mm degradation produced a small, consistent, mostly shape-related nRMSE increase that depended on the noise model, whereas 20-phase acquisition changed PI error and trough timing more. The results describe benchmark behaviour, not clinical acquisition requirements.

Counterfactual medical images are useful only when a requested clinical change is introduced without silently altering patient characteristics that should remain fixed. Existing diffusion editors can produce convincing images, but realism or target-classifier flips do not establish that an edit is localized, diagnosis-specific, anatomically faithful, or useful on real clinical data. We introduce PATHEDIT, a state-factorized counterfactual editing framework that decomposes an abdominal CT representation into pathology, anatomy, acquisition, and residual context before performing a clinically specified latent intervention. A pathology editor modifies only the target factor, while non-target factors are explicitly copied and preservation losses penalize collateral drift. The edited state is decoded jointly into an image, a report level description, and diagnostic readouts. Weak supervision from lesion masks, organ segmentations, and naturally occurring longitudinal lesion changes anchors edit direction and spatial support without requiring pixel-aligned counterfactual ground truth. We evaluate four properties that are often conflated in medical image editing: target edit success, non-target preservation, anatomical identity, and real-data utility. In our evaluation protocol, PATHEDIT achieves 94.2% target success while reducing mean off-target diagnostic change to 3.0%, yields substantially stronger alignment with observed lesion changes, and improves rare-lesion recognition and shortcut robustness on held-out real CT. These results motivate astricter view of counterfactual medical imaging: a useful edit must prove not only what changed, but also what did not.

The chick embryo is a widely used vertebrate model for developmental biology, cardiovascular, and bioengineering research. We present an automated laser speckle contrast imaging (LSCI) platform for non-invasive, longitudinal, label-free imaging of the developing heart and extraembryonic vasculature of the chick embryo through the intact eggshell. The platform can detect blood vessels as small as 70 +/- 17 um in diameter and enable repeated imaging from day 0 to day 8 of incubation without disturbing normal embryonic development. It provides both structural (a map of growing extraembryonic blood vessels) and functional (blood flow dynamics and heart rate) information. Using this system, we generated the first continuous, non-invasive, time-lapse recordings of the early functioning chick heart and its extraembryonic vessels. Longitudinal imaging was performed on 15 embryos from different chicken breeds with eggshell colors white, brown and green. Analysis revealed slight variability in early cardiac and vascular morphogenesis across breeds and shell types and shows its potential for automated developmental staging. Finally, for the first time, the growing vasculature of the yolk sac and the chorioallantoic (CAM) membrane was imaged from day 4 to 8. The heart rate and growth of the extraembryonic blood vessels were quantified automatically during imaging.

To compare virtual noncontrast (VNC) error between deep-silicon photon-counting CT (PCCT) and dual-energy energy-integrating detector CT (EID-CT) in a matched phantom and characterize tissue-specific error in humans. This prospective exploratory study included a matched phantom experiment and pilot human evaluation. Prototype projection-domain water-density maps served as the VNC surrogate. Twenty-four vials containing 0.1--20 mg I/mL in deionized water or simulated blood were scanned with PCCT and EID-CT. Three men underwent true noncontrast and contrast-enhanced PCCT; 80 matched measurements were analyzed. Paired t tests and mixed-effects models assessed system differences and iodine associations. Three participants (mean age, 67 {+/-} 10 years; all men) were included. Mean VNC error was 29.1 HU lower with PCCT than EID-CT (95% CI, 15.5--42.7 HU; {rho} < 0.001). Error was not associated with iodine concentration for PCCT (slope, 0.03 HU per mg I/mL; 95% CI, -0.92 to 0.98; {rho} = 0.95) but increased for EID-CT (slope, 3.51 HU per mg I/mL; 95% CI, 2.56--4.46 HU; {rho} < 0.001). The slopes differed by 3.48 HU per mg I/mL ({rho} < 0.001). In humans, errors in five non-blood-pool tissues were within 5 HU. Error was not associated with iodine concentration among 61 non-blood-pool measurements (adjusted slope, -0.17 HU per mg I/mL; 95% CI, -1.22 to 0.88; {rho} = 0.75). PCCT provided VNC measurements with errors closer to zero and less dependent on iodine than EID-CT in the phantom. In humans, error was generally small in non-blood-pool tissues and was not associated with iodine concentration.