Ultrasound in Medicine & Biology

ObjectivesBycatch-related decompression after forced submersion can result in severe gas embolic disease in sea turtles. This work used qualitative and quantitative ultrasound analyses, including gas grading, brightness analysis, and texture feature extraction, to investigate organ-specific gas burden and temporal evolution in the hearts, kidneys, and livers of bycaught sea turtles. Materials and MethodsUltrasound imaging of the hearts, kidneys, and livers of bycaught turtles was performed as part of veterinary evaluation either onboard fishing vessels immediately after surfacing (boat group, n=47) or after longer periods at shore-based facilities (shore group, n=30). Gas burden in each ultrasound scan was graded on an ordinal scale from 0 (no gas) to 5 (gas completely shadowing organ anatomy). Temporal differences in gas burden were compared between the shore and boat groups. Quantitative brightness and texture features were extracted from all organs, including contrast, correlation, homogeneity, and energy from liver and kidney data. A multivariate logistic regression model with leave-one-out cross-validation was conducted, with shore versus boat as a binary outcome (surrogate of post-surfacing decompression state) and ultrasound texture metrics as independent variables. ResultsMedian grades from the first ultrasound scan were significantly higher in the boat group than in the shore group for the liver, kidney, and heart (3, 3, and 3 vs 1, 1, and 0, respectively). This pattern coincided with a difference in the mean duration until the first scan which was conducted being 54 min for the onboard studies vs 330 minutes in the shore group. Mean pixel brightness within cardiac and liver regions of interest increased with rising bubble grade before decreasing at the highest grades, consistent with acoustic shadowing at severe gas burden. Texture features demonstrated significant organ-specific changes with increasing gas burden, and the regression models achieved areas under the receiver operating characteristic curve of 0.92 for liver texture features and 0.83 for kidney texture features. ConclusionsThese findings demonstrate organ-specific differences in gas evolution over time. Quantitative ultrasound features were associated with gas burden and post-surfacing interval in bycaught sea turtles. These findings support the feasibility of quantitative ultrasound biomarkers for assessment of decompression-related gas burden.

IntroductionTherapeutic ultrasound has been extensively studied in ablative and sonodynamic contexts, leaving the intrinsic bioactivity of continuous non-thermal low-intensity ultrasound (LIU) largely uncharacterized. ObjectivesTo characterize the tumor biological and immunomodulatory effects of non-thermal continuous LIU in complementary in vitro and in vivo breast cancer models, underpinned by a standardized exposure platform characterized through finite element simulations and experimental validation. MethodsAcoustic and thermal fields were characterized and optimized using in silico simulations and validated against hydrophone and temperature measurements to ensure homogeneous, non-thermal exposure (1MHz, 1W/cm2, 100% duty cycle). 4T07 murine mammary carcinoma spheroids received 20min LIU treatment, and metabolic activity, apoptosis, and intracellular stress-associated markers were assessed. In a syngeneic orthotopic 4T07 mammary carcinoma model in BALB/c mice, up to six LIU treatment cycles were administered; tumor growth, survival, histopathology, immunohistochemistry, bulk tumor RNA sequencing, spleen volume and plasma cytokine profiles were assessed. ResultsIn vitro and intratumoral temperatures remained within the physiological range ([≤]39{degrees}C) throughout exposure. In spheroids, LIU reduced ATP content by more than 40% and significantly increased apoptotic, Hsp70 and Hsp90 cell fractions. In vivo, cyclic LIU slowed tumor growth, increased intratumoral necrosis, and significantly prolonged time to humane endpoint compared to untreated controls. LIU promoted early intratumoral myeloid cell infiltration and shifted the tumor transcriptome (2,573 differentially expressed genes), with enrichment in gene sets associated with immunogenic cell death, pattern-recognition, inflammatory, and innate and adaptive immune programs and downregulation of pro-tumorigenic pathways. LIU enriched the transcriptional signatures of M1 macrophage polarization and, notably, B-cell compartment engagement, which has not previously been reported for standalone continuous mechanical ultrasound. LIU significantly attenuated tumor-associated splenomegaly and elevated plasma IL-1, TNF-, and IL-10. ConclusionThese results establish a reproducible preclinical platform and provide a hypothesis-generating mechanistic basis for evaluating LIU as an adjunct to immune checkpoint blockade. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/743931v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@31d366org.highwire.dtl.DTLVardef@12df6aborg.highwire.dtl.DTLVardef@9d91adorg.highwire.dtl.DTLVardef@c72b8a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Objective To evaluate the effect of vendor-integrated AI-assisted abdominal ultrasound software on operational efficiency and sonographer workload compared with manual scanning. Methods In this prospective randomised crossover study (January to February 2026), 32 healthy adults each underwent two upper abdominal examinations, one manual and one using vendor-integrated AI software (AI Abdomen Release 3.5; ACUSON Sequoia), in randomised order by two experienced sonographers, each participant scanned once by each sonographer. Scan time, hand-console interaction (keystrokes, hand travel, hover, jerk) from a custom depth-camera hand-tracking system, and operator modifications to AI outputs were recorded. Workload was assessed after each scan with the weighted NASA Task Load Index (NASA-TLX). Analysis used linear mixed-effects models. Results AI-assisted scanning reduced scan time (52.4 s, approximately 9%; 95% CI 23.7 to 81.2; P = 0.001), keystrokes (55, approximately 28%; P < 0.001) and hand travel (4.57 m, approximately 39%; P < 0.001), although the time saving was concentrated in one sonographer. Weighted NASA-TLX did not differ between conditions (-3.9 points; 95% CI - 9.3 to 1.5; P = 0.17), but subscale analyses showed reductions in mental demand (- 6.3; P = 0.03) and effort (- 7.0; P = 0.04), with no compensating increases. Sonographers modified 48 of 184 AI-generated values. Conclusion AI assistance improved operational efficiency and reduced self-reported mental demand and effort, with no compensating increase on other subscales. Gains arose under a controlled, abbreviated protocol in healthy volunteers and varied between operators, and are better read as a reshaping of operator work than its removal.

The formation of inert gas bubbles during decompression can lead to decompression sickness (DCS), a major operational risk for divers, compressed-gas workers, astronauts, and high-altitude aviators. In diving, DCS risk is typically inferred from post-dive ultrasound detection of venous gas emboli (VGE), precluding modification of decompression schedules based on real-time physiological feedback. Two-dimensional ultrasound imaging could provide additional insight into decompression-related physiological changes; however, its use in hyperbaric environments has been largely precluded by fire risk associated with elevated oxygen partial pressures (ppO2) in enclosed spaces. Here, we developed a workflow for operating a programmable ultrasound system under hyperbaric conditions and acquiring ultrasound data from the subclavian vein and calf muscle during decompression. A total of 42 dives were conducted by 26 individuals using a previously characterized dive profile to 132 feet seawater (FSW) for 20 min with 9 min of decompression. Three exposure conditions were evaluated: non-exercising, exercising, and a brief pause at 20 FSW during compression. Twelve dives included programmable ultrasound imaging during decompression. Post-dive VGE responses were consistent with prior reports while demonstrating substantial inter-individual variability and sensitivity to modest profile modifications. VGE were detected in the subclavian vein during decompression in two participants and subsequently confirmed by post-dive echocardiography. Calf muscle ultrasound brightness typically increased from pre-dive to decompression measurements, before decreasing below baseline in the 120 min post dive measurement period. These findings demonstrate the feasibility of programmable ultrasound imaging during human decompression and establish a practical framework for ultrasound operation under hyperbaric conditions. This approach may support future physiological studies and development of automated decompression monitoring technologies. New and NoteworthyThis study demonstrates the first use of a programmable ultrasound system to acquire and quantitatively analyze ultrasound data during human decompression. The approach enabled direct visualization of venous gas emboli during decompression and revealed calf muscle ultrasound signal changes, providing a new tool for investigating physiological responses during decompression that are not accessible through conventional post-dive monitoring.

Vertebral artery stenosis is a condition amenable to treatment with stenting. To accurately size stents, angiography is the most used imaging technique despite its limitation as a lumenogram with few planes of imagery. The technique's deficiencies are especially apparent at the vertebral artery origin due to image obstruction by surrounding vessels as well as vessel tortuosity. By contrast, intravascular ultrasound (IVUS) can be used to measure the arterial external elastic lamina (EEL), which is closer to the true vessel size and often leads to larger and more appropriate stent sizes being chosen for more effective treatment. A retrospective measurement comparison study was conducted on eight patients with vertebral artery stenosis. 2D digital subtraction angiography (2D DSA) and IVUS images were taken along the vertebral artery for each patient, and the blood vessel diameter was measured using the two techniques along a continuous segment of the artery. Our results indicated a statistically significant discrepancy between the diameter measurements (p<0.05), with EEL measurements by IVUS showing a mean percent increase of 32.96%, or 1.19 mm, compared to 2D DSA lumen measurements. The significant measurement contrast between the two techniques can lead to smaller stent sizes being chosen, increasing the risk of restenosis and stent thrombosis due to malapposition. Sizing stents to the EEL is possible with IVUS imaging and can lead to more reliable treatment due to more secure strut placement on the tunica intima and a more complete expansion of the artery.

Musculoskeletal ultrasound (MSK-US) enables real-time imaging of muscle structure and function, and wearable ultrasound (WUS) has extended this capability to dynamic movement tasks. Accurate tracking of muscle fascia displacement in M-mode WUS images is essential for quantifying muscle function, yet the relative performance of existing fascia-tracking algorithms remains uncharacterized. This study directly compares five fascia-tracking algorithms: Maximum Pixel Intensity (MPI), Muscle Boundary Tracking Algorithm (MBTA), Principal Component Analysis (PCA), Composite-Factorization PCA (CF-PCA), and U-Net segmentation, against expert-annotated ground truth to identify which approach best supports wearable muscle-monitoring applications. A total of 572 M-mode ultrasound images were collected during isometric quadricep activations (QA) and squats (SQ) using a multi-site WUS system with transducers positioned on the vastus lateralis (VL), rectus femoris (RF), and vastus medialis oblique (VMO). Fascia tracking using U-Net segmentation exhibited the lowest mean absolute error (median QA=0.57, median SQ=1.22; p<0.05), functional range not statistically different from expert traces (QA p=0.33; SQ p=1) and the most accurate estimates of functional error (median QA=-0.21; median SQ=-0.65; p<0.05). PCA-based methods demonstrated the highest correlation with the expert traces (PCA median QA=0.88; CF-PCA median QA=0.88; PCA median SQ=0.78; CF-PCA median SQ=0.75; p<0.005), reflecting superior tracking of relative contraction patterns. These results indicate U-Net segmentation is best suited for applications requiring precise fascia-depth estimation when labeled training data are available, while PCA-based methods are preferable for tracking relative contraction patterns without supervised training, informing algorithm selection for wearable neuromuscular monitoring in clinical and performance settings.

Skeletal muscle is a living, perfused soft tissue whose viscoelastic behavior is shaped by both voluntary contraction and hemodynamic state. However, the independent and superimposed contributions of contractile loading and blood flow restriction (BFR) have not been quantified simultaneously in real time. Twenty-six healthy adults underwent multi-frequency ultrasound time-harmonic elastography (THE, 60-80 Hz) of the vastus lateralis under six conditions: rest, 15% and 30% maximal voluntary contraction (MVC) before BFR, passive BFR after 4 min of cuff inflation, and 15% and 30% MVC shortly after cuff release. Shear wave speed (SWS), reflecting elasticity, and penetration rate (PR), reflecting inverse viscous damping, were extracted using the k-MDEV inversion algorithm. BFR significantly elevated SWS at all three contraction levels relative to the corresponding pre-BFR measurements (Holm-corrected p [&le;] 0.011; dz = 0.54-2.13). PR decreased during resting BFR (dz = 1.34, p < 0.001) and at 15% MVC after cuff release (dz = 0.94, p < 0.001), but not at 30% MVC (dz = 0.21, p = 0.294). BFR-related changes reduced the SWS-force slope by 14.5% and the PR-force slope by 40.7%. Men exhibited a greater BFR-induced increase in resting SWS than women. These findings show that THE can distinguish contractile and hemodynamic contributions to skeletal-muscle viscoelasticity and provide complementary information on elastic and dissipative tissue behavior in vivo.

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.

Background: The zone 4/5 boundary has been described as the mid-descending aorta-T6 level, but this correspondence has uncertain anatomic support. Purpose: To determine whether the aortic valve (AV) level approximates the descending aortic midpoint and to compare candidate boundaries relative to the critical segmental artery (CSA) origin. Materials and Methods: This retrospective study included 204 patients who underwent Adamkiewicz artery-specific CT angiography from January 2022 through February 2026. The CSA was defined as the aortic origin of the segmental artery directly or collaterally connected to the Adamkiewicz artery. Along the descending-aortic centerline, the descending aortic midpoint was the point halfway between a site 20 mm distal to the left subclavian artery origin and the celiac artery origin; the AV-level midpoint was the point halfway between the centerline intersections of the left coronary and noncoronary aortic sinus planes. Equivalence between midpoints was tested within a prespecified +/-20-mm margin. Distal CSA classifications were compared using McNemar tests. Results: The study included 204 patients (median age, 72 years [IQR, 59-79 years]; 142 men and 62 women). The mean difference between the AV-level midpoint and descending aortic midpoint was -1.2 mm (90% CI, -3.3 to 0.9 mm; P < .001 for equivalence). The T6 vertebral level was 59.0 mm proximal to the descending aortic midpoint. The CSA origin was distal to the AV-level midpoint and descending aortic midpoint in 96.6% (197/204) and 95.6% (195/204), respectively (P = .68), but distal to the noncoronary aortic sinus plane in 89.7% (183/204; P = .001 versus the AV-level midpoint). Conclusion: The AV-level midpoint approximated the descending aortic midpoint, whereas the T6 vertebral level was more proximal. Fewer critical segmental artery origins were classified as distal to the noncoronary aortic sinus plane than to the AV-level midpoint.

BackgroundQuantitative dynamic contrast-enhanced myocardial perfusion cardiovascular magnetic resonance (CMR) enables estimation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). These measurements require an arterial input function (AIF), which is typically derived from the left ventricular blood pool. However, the contrast agent bolus undergoes dispersion during transport through the coronary vasculature before reaching the myocardial microcirculation. This may introduce systematic and spatially heterogeneous errors in MBF and MPR estimates. PurposeThis work provides an extended segmental analysis of bolus-dispersion-induced errors in quantitative myocardial perfusion MRI using previously established computational fluid dynamics (CFD) simulations in realistic porcine coronary artery models. The focus of the present analysis is the assignment of coronary outlets to myocardial segments and the resulting segmental variability of MBF and MPR errors. MethodsRealistic three-dimensional models of the left and right coronary arteries were extracted from an ex-vivo porcine imaging cryomicrotome dataset. The models extended down to the pre-arteriolar level and included 364 outlets for the left coronary artery and 104 outlets for the right coronary artery, with an average outlet diameter of 383 {+/-} 85 {micro}m. Blood flow was simulated under rest and stress conditions using OpenFOAM. Contrast agent transport was then modeled by solving the advection-diffusion equation using a gamma-variate bolus as input. Outlet concentration-time curves were analyzed using an indicator-dilution model to estimate MBF and MPR errors. Outlets were assigned to standardized myocardial segments, and segmental averages were evaluated with respect to coronary supply territory and travel distance from the model inlet. ResultsThe simulations demonstrated marked segmental heterogeneity of volume blood flow and bolus-dispersion-induced MBF and MPR errors. Errors increased with travel distance from the coronary artery inlet and were more pronounced in regions supplied by the right coronary artery, consistent with lower flow velocities and stronger bolus dispersion. The resulting systematic errors led to underestimation of MBF and overestimation of MPR, with segmental deviations reaching up to approximately 60%. ConclusionBolus dispersion in the coronary vasculature may lead to substantial segmental and location-dependent errors in quantitative myocardial perfusion MRI. This extended analysis indicates that dispersion-related bias is not spatially uniform, but depends on coronary supply territory, travel distance, and flow conditions. These effects should be considered when interpreting regional MBF and MPR estimates, particularly as automated quantitative myocardial perfusion CMR becomes more widely used.

Breast cancer is the leading cause of cancer-related deaths among women in the Philippines. Over 65% of these cases are diagnosed when they are advanced (Montemayor, 2023). This highlights the need for improved early screening devices. E-HAPLOS, or Electrical Impedance Human-guided Assessment with Pressure for Lump Observation System, is a low-cost glove with sensors designed to improve early detection of suspicious breast lump through touch. It integrates force-sensitive resistors (FSRs) to measure tissue stiffness and Electrical Impedance Spectroscopy (EIS) to analyze conductivity across different frequencies--properties that are closely linked to breast cancer. The prototype uses an ESP32 microcontroller that transmits real-time pressure and impedance data to the website. Tested on gelatin breast models with simulated lump, the FSRs effectively identified lump locations by recording higher mean force values (45.81 kPa vs. 33.57 kPa). This guided approach allowed the combined FSR-EIS system to reach a diagnostic performance with an Area Under the Curve (AUC) above 0.94, a significant improvement over unguided measurement (AUC {approx} 0.78). A two-way ANOVA confirmed a significant difference in diagnostic performance based on the system modality (p < 0.001). Tukeys Honesty Significant Difference (HSD) test showed that the FSR-EIS system was statistically superior to both the unguided EIS (p < 0.001) and FSR-only system (p = 0.041). Results demonstrate the synergistic effect of the integrated system, enabling accurate differentiation of suspicious lumps from normal tissue. The FSR-EIS system of the E-HAPLOS glove shows a great potential for detection of lumps in simulated breasts as a screening tool.

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings

Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.

Objectives To validate two computational fluid dynamics (CFD) models derived from computed tomography angiography (CTA) for estimating trans-stenotic pressure gradients, using invasive intra-arterial pressure measurements as the reference standard in patients with renal artery stenosis (RAS). Background We assessed whether non-invasive assessment of the pressure gradient using CFD could be a reliable alternative to intra-arterial measurements for identifying hemodynamically significant RAS. Methods We performed intra-arterial measurements at rest and during dopamine-induced hyperemia to assess the trans-stenotic pressure gradient in 28 patients with RAS. A pre-intervention CTA scan was used to simulate the pressure gradient with a CFD model using a strategy based on Murray's law (CFD-Mu) and cortical volume (CFD-C). The agreement between the simulated and measured pressure gradients was assessed using intraclass correlation coefficients (ICC), Bland-Altman analysis and diagnostic agreement on the presence of a hemodynamically significant stenosis. Results In 20 patients, successful measurements and simulations were obtained. The ICC between measured pressure gradient and the CFD pressure gradient was 0.78 and 0.94 during baseline and 0.86 and 0.72 during hyperemia, for CFD-Mu and CFD-C, respectively. The sensitivity of CFD-Mu and CFD-C was 70% for both models at rest and 100% compared to the hyperemic measurements, whereas the specificity was 90% and 70% at rest and 79% and 72% during hyperemia, respectively. Conclusions The results support the use of individualized CFD simulations for hemodynamic assessment of RAS using CTA as input. The CFD models demonstrated high accuracy for the identification of a hemodynamically significant stenosis.

Peripheral artery disease (PAD) spans a continuum from large-vessel obstruction to distal microvascular dysfunction, yet routine non-invasive tests, including the ankle-brachial index (ABI), do not provide structurally resolved assessment of the foot microvascular bed and may be unreliable in the setting of medial arterial calcification or perioperative follow-up. Here we developed a clinic-oriented multispectral compound-scanning photoacoustic tomography system (MCPATS) for compression-free distal toe imaging, and an interpretable photoacoustic tomography distal microcirculation score, termed PACT-DMS, for phenotyping PAD-related distal vascular abnormalities. PACT-DMS was derived from anatomically standardized distal toe sections and integrated seven prespecified vascular features spanning trunk-vessel morphology, microvascular distribution and pulsation-related dynamics through a traceable linear support vector machine. In a prospective single-centre cohort of 45 participants, the bilateral fusion PACT-DMS model distinguished patients with PAD from healthy controls with an area under the receiver operating characteristic curve of 0.964 (95% CI, 0.907-1.000) and an accuracy of 91.1% (95% CI, 82.2%-97.8%) under subject-level leave-one-out cross-validation, supported by complementary robustness analyses. Exploratory analyses further showed that PACT-DMS identified abnormal distal vascular phenotypes in 6 of 9 clinically diagnosed PAD limbs with non-abnormal ABI and visualized distal vascular-bed changes before and after revascularization. These findings support MCPATS-enabled interpretable photoacoustic vascular phenotyping as a candidate adjunctive approach for distal microcirculatory assessment in PAD; larger multicentre studies with external validation and prespecified analysis protocols will be required to define its clinical role.

Background: Epicardial adipose tissue (EAT) is a metabolically active fat depot adjacent to the myocardium and the coronary arteries that can be non-invasively assessed by cardiovascular magnetic resonance (CMR). Increased EAT volume quantified by CMR has been linked to adverse cardiac remodeling, atrial fibrillation, coronary artery disease, and heart failure. Among CMR techniques, isotropic three-dimensional (3D) Dixon imaging at 1.3 x 1.3 x 1.3 mm3 resolution was developed to improve tissue characterization, providing fat-water signal separation for precise volumetric EAT assessment. However, manual segmentation of 3D datasets is highly time-consuming. For integration into clinical and research CMR workflows, reliable and fast automated segmentation is needed. Purpose: To develop and evaluate an automated deep-learning-based pipeline for ventricular EAT quantification based on isotropic 3D Dixon CMR acquisitions. Methods: An nnU-Net model was trained on 165 3D Dixon CMR cases encompassing healthy individuals and patients with underlying cardiovascular disease. The model was trained using all four Dixon phase images (opposed-phase, in-phase, fat-phase, water-phase). Manual 3D ventricular EAT segmentations served as the ground truth for training and evaluation. Performance was evaluated in 30 independent cases using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), volumetric agreement, Pearson correlation, intraclass correlation (ICC), and Bland-Altman analysis. Model performance was benchmarked against interobserver and intraobserver variability. Results: Automated segmentation achieved a mean DSC of 0.896 {+/-} 0.039 and HD95 of 1.84 {+/-} 0.93 mm versus ground truth. Volumetric agreement with ground truth was high (r = 0.984, ICC = 0.988, p < 0.001; mean bias -0.70 mL, limits of agreement (LoA) [-10.31, 8.90] mL), exceeding interobserver agreement (bias -25.24 mL, LoA [-42.81, -7.66] mL) and comparable to intraobserver reproducibility (bias 2.72 mL, LoA [-8.73, 14.17] mL). Automated segmentation required less than one minute per case compared to 58.4 {+/-} 7.9 minutes for manual segmentation. Two of 30 cases (6.7%) required minor manual correction, both less than five minutes. Conclusion: Fully automated nnU-Net-based ventricular EAT segmentation from isotropic 3D Dixon CMR achieves accuracy comparable to intraobserver reproducibility while significantly reducing post-processing time. The approach may facilitate large-scale and longitudinal EAT quantification in CMR-based research workflows.

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.

Background: Cerebrospinal fluid (CSF) circulation is important for maintaining homeostasis of the central nervous system. Previous studies have largely focused on the ventricular system, the craniocervical junction, or local spinal segments, leaving the overall and spatially heterogeneous characteristics of CSF flow across the craniospinal axis insufficiently characterized. The spinal subarachnoid space (SAS) is often treated as a homogeneous annular compartment surrounding the spinal cord, an approach that may obscure directional differences among its internal regions. Methods: This single-center, exploratory, prospective imaging study enrolled 15 healthy volunteers. All participants underwent 3.0-T electrocardiography-gated two-dimensional cine phase-contrast magnetic resonance imaging (Cine PC-MRI) and high-resolution T2-weighted imaging. CSF was evaluated at the level of the cerebral aqueduct outlet/fourth-ventricle inlet, C1-C2, C5-C6, T5-T6, L1-L2, and the lumbar cistern. Region-of-interest (ROI)-based quantitative analysis using Q-Flow software recorded mean velocity, absolute peak velocity, and directional peak velocity. Results: Multiplanar Cine PC-MRI showed that CSF phase signals within the spinal SAS were not uniformly distributed but formed two principal flow regions, ventral and dorsal. Mean velocity and absolute peak velocity were similar between the ventral and dorsal regions, whereas directional peak velocity differed (1.50 +/- 2.98 cm/s vs. -0.38 +/- 3.23 cm/s, P = 0.036). High-resolution T2-weighted imaging showed denticulate ligaments, nerve roots, and associated fibrous connective tissue in the lateral transition zones between the two regions. Conclusions: In healthy adults, CSF flow in the spinal SAS was not synchronous motion within a single homogeneous compartment; rather, it showed longitudinal oscillatory flow in ventral and dorsal regions coupled to the cardiac cycle. These findings provide preliminary in vivo evidence for studies of CSF hydrodynamics across the craniospinal axis and an imaging basis for investigating CSF circulation disturbances in conditions such as hydrocephalus, Chiari malformation, syringomyelia, and arachnoid adhesions.

Purpose: To build a 3D U-Net model, BioDeformUNet, to predict the deformation vector field (DVF) of the liver in near real-time, for efficient intra-procedural evaluation of the minimal ablative margin (MAM). Materials and Methods: This retrospective study included 170 contrast-enhanced computed tomography (CECT) image pairs from 157 patients who underwent liver ablation treatment between 2020-2024. Each data instance included one pre-ablation CECT (pre-CECT) and one post-ablation CECT (post-CECT). BioDeformUNet was trained under the guidance of DVFs generated by a biomechanical model-based deformable image registration (DIR) algorithm using a loss function that focused on large liver deformations. Data were split patient-wise into training (92-93 patients), validation (23-24 patients), and testing sets (42 patients). We compared our performance with two deep learning-based DIR methods: VoxelMorph and VFA. Evaluation metrics included: target registration error (TRE), Dice similarity coefficient (DSC), Minimum Ablation Margin (MAM), and inference time. For BioDeformUNet, we additionally evaluated the accuracy of the deformed tumor center-of-mass mapping by comparing the predicted tumor center location with that generated by Morfeus. A mapping error less than 3.0 mm (corresponding to the voxel size) was considered accurate. We used the Wilcoxon signed-rank test to assess the significancy of each test result. Our code is available at https://github.com/XinyueZhang831/BioDeformUNET. Results: The TRE of BioDeformUNet was not significantly different from Morfeus (3.31 BioDeformUNet; 3.23 Morfeus; p-value=0.41). The BioDeformUNet DVF magnitude was within 3.0 mm of Morfeus DVF for an average of 91.9% of the voxels. Tumor mapping errors greater than 3.0 mm occurred in only 8 cases. The inference time of BioDeformUNet was 0.6s per image pair, 0.2s for VoxelMorph, 0.3s for VFA, and 20.2s for Morfeus. Conclusion: BioDeformUNet achieved a similar performance to the biomechanical model-based algorithm but required fewer computational operations, resulting in a 34 times speedup in DVF computation.

Purpose: Quantifying aortic morphology is central to surgical planning for thoracic endovascular aortic repair (TEVAR), yet no consensus exists on how best to represent three-dimensional aortic shape for outcome prediction. Two broad strategies have emerged: statistical shape analysis (SSA), which relies on statistical methods and dimensionality reduction to capture the most significant shape modes, and geometrically-informed approaches that extract descriptors grounded in differential geometry. Here, we directly compare these paradigms on a cohort of 290 CTA scans classified by surgical outcome (non-pathological, successful TEVAR, failed TEVAR). Methods: For the geometrically-informed approach, we use a two-dimensional feature space using normalized fluctuation in integrated Gaussian curvature $\widetilde{\delta K}$ and mean aortic radius $R$. For SSA, we construct a point-cloud shape model with dimensionality reduction using Principal Component Analysis (PCA) and evaluate classification performance as a function of the number of retained principal components. Results: SSA's leading principal components encode variations in global aortic size and are statistically redundant with ($R$, $\widetilde{\delta K}$), yet they lack a one-to-one correspondence with interpretable anatomical quantities. Testing on an unseen, independent dataset reveals that the geometrically-informed approach provided better generalizability than SSA. Using Gaussian process classification with 10-fold cross-validation, we find that the geometrically-informed approach achieves a higher weighted $F_1$ score than SSA achieves with up to 20 principal components. While SSA's full-dataset accuracy rises above 90\% with increasing dimensionality, this gain is driven by overfitting rather than genuine discriminative power. Conclusion: These results demonstrate that geometrically-informed descriptors offer a more interpretable, robust, and clinically translatable framework for aortic disease classification than data-driven statistical shape representations.