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.

Objective Regional aortic mechanics and the mechanical environments of the true and false lumens are not represented by maximal diameter alone. We evaluated whether magnetic resonance elastography (MRE) could provide paired descending thoracic and abdominal aorta stiffness estimates and separate true- and false-lumen estimates in thoracic aortic dissection. Methods We acquired 60-Hz descending thoracic and abdominal aortic MRE data in 10 healthy subjects and in 2 patients with chronic type B aortic dissections. Three motion-encoding directions and four temporal offsets were reduced to first-harmonic complex wave fields. After directional filtering, we traced a curved centerline through the aortic mask. Local spatial frequency was estimated along arc length and converted to effective shear stiffness. The primary regional outcome was the mean effective stiffness over the central 20%-90% of the path from the quality-ranked slice in each region. In dissection, the whole-aorta mask and flap were defined on a registered sagittal scout; a vascular surgeon assigned the true and false lumens, and separate, manually centered lumen paths followed one union-mask phase correction and unwrapping step. We compared regional values with paired tests. Results All 10 regional datasets and all 56 available slices were processed without failure. The 10 healthy volunteers (mean age, 26.6 +/- 4.6 years; 6 men) had descending thoracic stiffness of 15.43 +/- 3.30 kPa and abdominal stiffness of 19.20 +/- 3.14 kPa. The mean thoracic-minus-abdominal difference was -3.77 +/- 3.40 kPa (95% confidence interval, -6.20 to -1.34 kPa; paired t-test, P=.007; exact Wilcoxon signed-rank test, P=.010). Eight participants had a higher abdominal value, and two had a higher thoracic value. In Subject A with a chronic aortic dissection, slice-averaged true- and false-lumen stiffness was 21.62 and 19.07 kPa, respectively (false-minus-true, -2.54 kPa; absolute percentage difference, 12.5%). In Subject B with a chronic aortic dissection, corresponding values were 12.17 and 17.40 kPa (false-minus-true, +5.22 kPa; 35.3%). Conclusions We demonstrated the feasibility of MRE to provide paired regional analysis of the aorta in healthy people and to separate stiffness profiles of the true and false lumens in Type B thoracic dissection cases. Abdominal effective stiffness exceeded descending thoracic stiffness in the volunteer cohort. Opposite true- and false-lumen contrast in the two subjects with chronic Tye B aortic dissections demonstrates measurement feasibility but does not establish a consistent compartment pattern, disease threshold, or intrinsic wall material property.

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.

Purpose: Ultrasound time-harmonic elastography (THE) uses continuous multifrequency vibration and standard-frame-rate ultrasound to generate full-field mechanical maps. We tested whether THE concurrently resolves task-dependent responses in the thin thoracolumbar fascia (TLF) and deeper multifidus muscle (MFM) during back extension. Methods: Eighteen healthy adults (9 female and 9 male; age 19.4 +/- 1.9 years) underwent multifrequency THE (60-80 Hz; 100 frames/s) at rest and during active back extension. Shear wave speed (SWS) and penetration rate (PR), an attenuation-sensitive measure, were quantified in manually defined TLF and MFM regions. Four within-tissue contrasts were tested using paired t tests with Holm adjustment; between-tissue, superficial-tissue-thickness, and sex analyses were exploratory. Results: Back extension increased SWS in the TLF (0.43 +/- 0.43 m/s; dz = 0.99) and MFM (0.62 +/- 0.23 m/s; dz = 2.73) and decreased PR in the TLF (-1.01 +/- 0.54 m/s; dz = -1.88) and MFM (-1.20 +/- 0.51 m/s; dz = -2.34); all Holm-adjusted p < 0.001. The SWS increase was 0.19 m/s greater in MFM than TLF (95% confidence interval, 0.01-0.38 m/s; unadjusted p = 0.042), whereas PR changes did not differ between tissues (p = 0.233). Conclusion: THE simultaneously captured spatially resolved, task-dependent responses in adjacent fascial and muscle tissues using standard-frame-rate acquisition. The findings establish biomechanical sensitivity in healthy adults and support further validation with standardized loading, repeatability testing, and comparison with conventional shear wave elastography.

Transcranial low-intensity focused ultrasound (LIFU) offers a rare ability to noninvasively modulate human brain activity in superficial and deep neural structures. Yet the field still cannot reliably predict how a given acoustic protocol will affect a specific brain target. We synthesize 230 LIFU experiments targeting the human cerebrum from 136 articles reporting sufficient acoustic information and neuromodulatory outcomes. We compare associations between acoustic parameters and reported outcomes across cortical and subcortical targets. This reveals divergent parameter-effect landscapes across cortical and subcortical targets. Cortical outcomes follow a nonmonotonic organization across pulse repetition frequency and duty cycle, with distinct zones associated with facilitation, suppression, and null or ambiguous effects. Subcortical outcomes instead vary predominantly along duty cycle, shifting from suppression at lower values toward facilitation at higher values. Intensity and dose factors do not provide a consistent target-independent explanation for outcome polarity, and current evidence is insufficient to identify parameters promoting lasting effects. These findings challenge the assumption that acoustic protocols have generalizable effects across the brain and identify anatomical target as a central dimension that should inform LIFU protocol design. We provide SonoMap, an open, interactive web resource that supports a more systematic, evidence-based approach to human LIFU neuromodulation.

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.

Background: A major hallmark of necrotizing enterocolitis (NEC) is intestinal hypoxia, but detection of this feature is difficult. Photoacoustic imaging (PAI) is an emerging imaging method to noninvasively visualize intestinal oxygenation and microvasculature and is promising for improved NEC detection. Prior to clinical translation of PAI for NEC detection, initial studies must be performed in a pediatric population to assess safety and feasibility. Methods: Under IRB approval, eighteen healthy pediatric participants underwent PAI scans before and after eating a lipid-rich snack. PAI was acquired in each abdominal quadrant with intestinal regions-of-interest (ROI) manually drawn on each image series to measure averaged values of deoxygenated hemoglobin (Hb), oxygenated hemoglobin (HbO2), total hemoglobin (HbT), and oxygen saturation (sO2). Results: Minor temporary erythema and pressure from safety goggles in some participants were the only adverse events reported. Pre-/post-prandial changes for HbT, HbO2, Hb, and sO2 were assessed for each abdominal region and age cohort. Results showed significant post-prandial hyperemia in the intestines with no changes in bowel oxygenation. Conclusions: PAI is a safe and feasible imaging modality for evaluating intestinal blood flow in pediatric patients that detects physiological changes in bowel microvasculature and shows strong potential as a quantitative biomarker for assessment and monitoring of intestinal vascular health.

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.

Purpose: The aims of this study were to develop an externally validated deep learning vascular segmentation model for digital enhancement of vertebral artery dissection detection on CTA and to assess clinical utility of the model through a paired crossover reader study. Materials and Methods: This retrospective, IRB-approved study conducted from September 2024 to July 2026 included an internal training cohort of 84 manually segmented CTAs plus 17 CTAs from the RSNA Intracranial Aneurysm Challenge (101 CTAs total) and an external testing cohort of 40 CTAs (22 positive, 18 negative for vertebral artery dissection). A nnU-Net (version 2) model was trained using five-fold cross-validation with the entire training cohort. Technical performance was evaluated using the Dice similarity coefficient (DSC). A two-part crossover reader study included 10 readers who assessed diagnostic accuracy, confidence, and interpretation time with and without segmentation-based augmentation. Statistical analyses included McNemar's exact test (accuracy) and Wilcoxon signed-rank test (confidence, time), with P < .05 considered significant. Results: The model achieved a DSC of 0.96 (SD 0.01). In the reader study, diagnostic accuracy was lower with augmentation than without (0.72 vs 0.84, P < 0.001), while confidence (3.98 vs 3.96, P = .77) and interpretation time (137.3 vs 166.7 seconds, P = .54) did not differ significantly. Subjectively, 7 of 10 readers reported they would use the tool routinely in acute or trauma settings. Conclusion: Although the deep learning model demonstrated accurate segmentation of vascular structures, further validation is needed to establish its utility for enabling accurate diagnosis of VAD in fast-paced clinical settings.

Objective: Preclinical studies suggest that irradiation combined with anti-PD-1 antibody (iRT) exacerbates cardiac injury in mice, partly through CD8 T-cell-mediated mechanisms. We investigated radiation-induced PD-L1 expression in cardiomyocytes and explored its potential role in iRT-associated cardiac injury. Methods: AC16 cells were exposed to different doses of irradiation. PD-L1 expression was assessed at different time points by flow cytometry, qRT-PCR, western blotting, and immunofluorescence. Cell viability, apoptosis, and cytokine levels were evaluated. PD-L1 was knocked down using siRNA to assess its function. PBMCs or CD8 T cells were co-cultured with irradiated or non-irradiated AC16 cells at different E:T ratios, with or without PD-1 blockade. A C57BL/6J mouse model of iRT-induced myocardial injury was established, and myocardial PD-L1 expression and T-cell infiltration were evaluated. Results: Irradiation upregulated PD-L1 expression in AC16 cells, with increased membrane and cytoplasmic signals at 48 h and prominent nuclear-associated fluorescence at 72 h. Irradiation increased IL-6, MCP-1, CCL5, and CXCL10 release, decreased IL-10, reduced cell viability, and increased apoptosis. PD-L1 knockdown further increased inflammatory cytokine and chemokine release but did not significantly alter apoptosis or further reduce viability in irradiated AC16 cells. In the presence of a high density of activated CD8 T cells, disruption of PD-1/PD-L1 signaling was associated with a further reduction in AC16 cell viability. In vivo, myocardial PD-L1 expression was increased, whereas CD8 T-cell infiltration remained limited and focal. Conclusion: PD-L1 upregulation in cardiomyocytes may exert an adaptive protective and immunoregulatory role during iRT-associated injury, potentially by limiting inflammatory mediator release and modulating susceptibility to activated CD8 T-cell mediated injury. CD8 T-cell density and activation are the key issues.

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.

Purpose: To develop an automated pipeline (PERADS.net) that segments acute pulmonary embolism on CT pulmonary angiography, assigns a Pulmonary Embolism Reporting and Data System (PE-RADS) grade with named anatomic localization, and measures the right-to-left ventricular (RV/LV) diameter ratio, and to assess radiologist agreement. Materials and Methods: In this retrospective study, 120 CT pulmonary angiograms (70 peripheral, 35 central, 15 negative) from the public RSNA Pulmonary Embolism CT dataset were analyzed. A two-channel five-fold nnU-Net ensemble segmented the embolus; the pulmonary arterial tree was reduced to a branching graph, and the grade was set by the most proximal level with at least 1% of embolic volume. Three radiologists, blinded to the algorithm-assigned grade, independently assigned grades and rated RV/LV plausibility. Agreement was assessed with percent agreement and Cohen or Fleiss kappa on five-grade and grouped scales (grade 0 versus 1-2 versus 3-4). The automated ratio was compared with the dataset's binary RV/LV label in 2119 examinations. Results: Agreement between the algorithm and three-radiologist consensus (n = 119) was 60.5% (kappa, 0.38) for individual grades and 90.8% (kappa, 0.74; 95% CI: 0.59, 0.87) for the grouped scale; 34 of 47 discordant examinations fell within grades 3-4. As a fourth reader, the algorithm matched grouped-scale interobserver agreement (mean kappa, 0.62 versus 0.67). Radiologists showed no agreement rating RV/LV plausibility as favorable or incorrect (Fleiss kappa, -0.00), whereas the automated ratio agreed with the external label (kappa, 0.49), overestimating strain 3.1:1. Conclusion: Automated PE-RADS grading agreed with radiologist consensus comparably to a fourth reader on the grouped scale.

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 AND PURPOSE: Measured intracranial vessel dimensions guide device selection in cerebrovascular intervention, yet accuracy depends on imaging modality, luminal contrast concentration, and the lumen-edge detection criteria. Prior accuracy studies benchmark one angiographic modality against another rather than physical ground truth. This study quantifies accuracy of CT angiography (CTA), 3D rotational angiography (3DRA), and 2D digital subtraction angiography (DSA) against a phantom of tubular contrast-filled structures serving as surrogates for intracranial vessels. MATERIALS AND METHODS: Phantoms with cylindrical lumen diameters 1.2-8.5 mm at graded iodinated contrast concentrations (6.25-100%) were imaged with clinical devices in air and a tissue-equivalent gel surround. Tubular cross-sections perpendicular to centerlines were measured by maximum-gradient, full-width-half-maximum (FWHM), and fixed-Hounsfield criteria and compared with physical phantom measurements using mixed-effects models. RESULTS: Across 1682 cross-sections (73 lumens), all modalities recovered true diameter within ~0.5 mm but consistently measured larger than the true dimensions. Over-measurement occurred minimally in the 2-5 mm range, maximally at the extremes of caliber and contrast concentration. 3DRA was modestly closer to physical phantom measurements than CTA (gradient bias +0.40 vs +0.60 mm). Maximum-gradient edge over-measured diameters versus the conventionally preferred FWHM, with the latter carrying larger, more concentration-sensitive bias. Fixed-HU thresholds were concentration-biased on CTA and unusable on 3DRA. 2D DSA, currently the clinical reference standard, was itself biased in analysis of its auto-calibrated measurements, over-measuring large lumens, under-measuring small lumens 1.5 mm, and reading larger frontally than laterally through magnification. CONCLUSIONS: Conventional CTA, 3DRA, and 2D DSA each measure intracranial-scale caliber lumens within ~0.5 mm but with consistent bias. Maximum-gradient lumen edge detection minimizes bias and outperforms FWHM, while fixed-Hounsfield contour tracing is unreliable, particularly on 3DRA. Characterizing these error modes provides a calibration basis for automated vessel-measurement platforms.

Objective: Rotator cuff tears involving the supraspinatus tendon are a common cause of shoulder pain. Ultrasound (US) is accessible, dynamic, and lower cost than MRI, but diagnostic performance may vary by tear severity, operator expertise. This systematic review and meta-analysis compared the US with MRI for supraspinatus tear detection. Methods: MEDLINE, Embase, Scopus, and CINAHL Plus were searched for studies published from January 2015 to June 2026. Study quality was assessed using QUADAS-2. Diagnostic outcomes included sensitivity, specificity, accuracy, PPV, NPV. Meta-analyses were performed separately for any, partial-thickness, and full-thickness tears when complete 2 x 2 data were available. Results: 23 studies comprising 1,452 participants were included. US demonstrated stronger and more consistent performance for full-thickness than partial-thickness tears. Mean sensitivity, specificity, and accuracy were 88.5%, 95.2%, and 93.4%, respectively, for full-thickness tears, compared with 71.5%, 91.5%, and 79.2% for partial-thickness tears. HSROC analysis showed pooled sensitivity and specificity of 96% and 76% for any tear, 94% and 80% for partial-thickness tears, and 96% and 98% for full-thickness tears. Agreement measures also generally favored full-thickness tears. Operator expertise, study quality varied, and only one study evaluated a handheld ultrasound system. Conclusion: Ultrasound demonstrates strong diagnostic performance for supraspinatus tears, particularly full-thickness tears, while partial-thickness tears remain more challenging. Given the high observed sensitivity, well-performed US may serve as a practical first-line modality to rule out full-thickness tears, with MRI remaining complementary for equivocal partial-thickness tears, associated pathology, and preoperative assessment.

Propose: Routine echocardiographic assessment can be affected by operator variability and time-consuming post-processing, which may limit rapid quantitative analysis. Real-time cine ultrasound demands low-latency automated processing for clinical usage. Existing approaches lack standardized evaluation frameworks, verified edge deployment, and structured reproducibility guarantees. To address these limitations, we built Myocardial Stiffness Tracking & Assessment Toolkit driven by Artificial Intelligence (MyoSTAT.AI). We report a deterministic, fully reproducible pipeline that performs real-time segmentation and SWE velocity estimation, evaluated entirely on synthetic data. Approach: Four U-Net-based architectures spanning single-frame and temporal designs were evaluated via systematic ablation: a 2D baseline, a 2.5D stacked-frame model, a 3D volumetric model, and a ConvLSTM variant. Experiments used synthetic reference corpora with split accounting the segmentation corpus contained 1,200 synthetic frames, and the SWE corpus contained 900 synthetic velocity-field cases. The SWE branch used Radon-transform-based propagation-direction estimation, time-domain shear-wave speed estimation, and configurable temporal stabilization on synthetic velocity fields. TensorRT FP16 deployment benchmarks were performed separately for the exported UNet2.5D segmentation model on RTX 3060 and Jetson Orin Nano hardware. Results: On the synthetic evaluation quantities, the ConvLSTM variant achieved the highest segmentation accuracy (Dice 0.994, IoU 0.987), an 18.6% improvement over the 2D baseline (Dice 0.808). A 2.5D model with a 3-frame temporal window achieved Dice 0.983 at substantially lower latency (433 ms vs. 1193 ms). TensorRT FP16 deployment yielded 341 FPS on the RTX 3060 and 90.4 FPS on the Jetson Orin Nano. Penalized least-squares temporal stabilization (smoothn, {lambda}=10) reduced shear-wave speed CoV by 63.8% at 2.7 ms latency overhead per frame, with no loss of edge preservation. Conclusions: We demonstrate a deterministic, reproducible computational benchmarking framework for cardiac segmentation and SWE velocity-field stabilization, evaluated entirely on synthetic data, together with preliminary inference feasibility on workstation and embedded hardware. These results represent a synthetic proof-of-concept rather than a validated clinical or operator-independent tool; acquisition of real echocardiographic data and clinical validation are required before any claim of diagnostic or deployment readiness can be made.