Annals of Biomedical Engineering

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

Angiogenesis drives the formation and remodeling of capillary networks throughout tissue repair, regulating the vascular environment that supports healing and tissue remodeling. Experimental characterization of these processes is commonly performed using CD31-stained histological tissue sections to quantify capillary surface density and morphology throughout healing. However, these measurements provide only two-dimensional characterization of an underlying three-dimensional (3D) vascular network, limiting direct estimation of volumetric capillary density and vascular architecture. To address this limitation, an experimentally informed framework was developed to generate representative 3D capillary networks, enabling estimation of volumetric capillary density from histologically quantified vascular measurements. CD31-stained histological sections obtained from a longitudinal porcine lumpectomy study were analyzed to quantify the percentage of CD31-positive area (%CD31+) and capillary morphology within healthy tissue and healing surgical cavities. Histologically quantified morphology distributions and literature-informed vascular branching characteristics were incorporated into a capillary network generation framework to construct representative 3D vascular networks. Capillary branches were iteratively generated within representative tissue volumes until virtual histological sections reproduced experimental %CD31+ measurements, enabling estimation of volumetric capillary density. Generated capillary networks demonstrated good agreement with experimentally characterized 3D vascular architecture, while simulated histological sections accurately reproduced experimentally quantified capillary counts and vascularization measurements. Application of the framework to the porcine lumpectomy dataset captured temporal changes in vascular remodeling throughout healing, revealing progressive increases in volumetric capillary density and vascular maturation. Collectively, this framework provides an experimentally informed methodology for relating histological vascular measurements to volumetric capillary density estimates, supporting future computational studies of angiogenesis and tissue repair.

PurposeComputational models of the uterus during pregnancy enable analysis of electro-chemo-mechanical pathways to predict labor timing and guide treatment planning. We aim to develop a robust image-based modeling pipeline to investigate uterine passive mechanics during late pregnancy. MethodsA parametric model of the uterus and cervix was created using a patients MRI measurements at 38 weeks of gestation. Inspired by advances in cardiac mechanics models, we created Laplace-Dirichlet solutions to inform tissue domains, fiber structure within the uterus and cervix, and spatially varying Robin boundary conditions. Prior imaging and mechanical testing data were used to fit material parameters. Boundary condition parameters were tuned to match the displacements of a previously established approach that employed contact with surrounding tissue. The tissue mechanical response to a physiologic load was assessed across varying material properties and fiber architectures. ResultsDiscrepancies in nodal displacements between the current approach and the contact-based model were limited to 3.4 {+/-} 1.8 mm, yielding nearly 90 % computational savings. Uterine tensile strains were more sensitive to ground substance elastic modulus (E) compared to fiber properties. Reduced E and fiber stiffness increased cervical strains and compression. Fiber dispersion and architecture modulated the opening of the cervical internal ostium but had a reduced impact on compression. ConclusionWe developed a novel workflow for modeling passive uterine mechanics, informed by patient-specific measurements and in vitro mechanical tests. The robust workflow may prove useful for studying labor progression and conducting longitudinal studies to enhance our understanding of normal and pathological pregnancies.

Background: Acute pulmonary embolism (PE) is a leading cause of morbidity and mortality with persistent difficulties in choosing interventions and predicting outcomes for patients defined clinically as intermediate risk. Computational fluid dynamics (CFD) tools have been used to understand the hemodynamic environment and plan interventions in the pulmonary arteries across a variety of disease conditions. Several biomechanical metrics have been used to evaluate risk in narrowed vessels, including hemodynamic resistance, power dissipation, and fractional flow reserve (FFR). In this study, we evaluate differences in these CFD-derived biomarkers between healthy controls (HC) and intermediate risk, acute PE patients. Additionally, we examine the response of patient hemodynamics to mechanical thrombectomy and compare values of these biomarkers across post-intervention pressure status. Methods: A CFD framework was developed to simulate patient-specific hemodynamics within the pulmonary vasculature identifiable from clinical imaging. The pipeline involved reconstructing three-dimensional (3D) structures of the pulmonary arteries and modeling blood flow with the finite element method. Patient-specific boundary conditions were derived from matching pre-intervention inlet mPAP to the patient's measured value given their measured CO as steady inflow. Converged simulations allowed for precise quantification of primary hemodynamic characteristics (flow and pressure) as well as secondary flow phenomena, primarily wall shear stress (WSS) and simulated pressure metrics such as fractional flow reserve (FFR). Results: Our simulations revealed significant elevations in resistance, power dissipation, and the number of vessels with low FFR in those patients with acute PE (n=6) compared to HC (n=3). Occlusions of hemodynamic significance were generally found in segmental pulmonary arteries. For patients with normalized pulmonary pressures post-thrombectomy (n=3), we found significantly higher proximal power dissipation and counts of low FFR vessels in comparison to those with elevated pressures after intervention (n=3). Distal resistance, which was derived from the portion of resistance attributed to the outflow boundary conditions, was significantly higher in patients with elevated pressures post-intervention. Across all PE patients, FFR count was significantly correlated with post-thrombectomy pulmonary pressure and cardiac index. Discussion: CFD-derived biomarkers offer a promising tool for understanding disease severity in acute PE. Differences between HCs and acute PE patients reveal expected increases in metrics associated with proximal disease burden. Yet, in examining acute PE patients with varying post-intervention hemodynamics, we found that these metrics of proximal disease burden could also be useful to predict the efficacy of mechanical thrombectomy. Those patients with normalized pressures had higher values for proximal disease metrics and lower values for distal disease metrics than those with continued elevations in pressure. This suggests that accessibility of hemodynamically-significant emboli to thrombectomy may be useful as a predictor for outcomes.

PurposePatient-specific models of left atrial (LA) mechanics often assume uniform left atrial wall thickness (LAWT), but the effect of LAWT on the mechanics and hemodynamics remains less quantified. MethodsFour LA myocardium models were built from gated CTA images: a baseline variable thickness (VT#0), two reduced-dilation variants, and a 2mm uniform thickness model. Multi-scale mechanics and blood flow simulations were performed across all the thickness variants using model parameters personalized on the baseline model. Predicted displacements, wall stresses and strains, and hemodynamics were compared. ResultsAcross all LAWT variants, myocardial volume spanned 14.4-19.9mL (38%), while cavity volume remained mostly within 5% of image data throughout the cardiac cycle. Circulatory system output, myocardial displacements, and strains varied by 5-6% relative to the baseline model. Instantaneous stresses increased by up to 19% in the thinner variable thickness models and decreased by up to 16% in the uniformly thick case. Globally, the area under low time-averaged wall shear stress (TAWSS) varied between 23% and 30% across all thickness variants, while LA exposed to elevated oscillatory shear index (OSI) increased from nearly 6% to 19%. Over 90% of LAA was exposed to low shear, but the high-OSI area increased from 7% in VT#0 to over 30% in Uniform. ConclusionA personalized multiscale modeling framework was leveraged to demonstrate that the left atrial myocardial stresses and oscillatory shear had a greater sensitivity to local wall thickness representation compared to cavity volumes, tissue displacements, strains, and mean blood shear.

Musculoskeletal models are widely used to study human movement, investigate musculoskeletal disorders and evaluate athletic performance. The accuracy of these models depends primarily on representing subject-specific musculoskeletal geometry, which determines joint definitions and muscle paths. Subject-specific models can be derived from medical imaging, however the task remains labour-intensive with numerous subjective decisions, which limits their reproducibility and use in large-scale studies. Automated methods that preserve anatomical model topology while adapting models to individual bone geometries are therefore needed. Here, we develop and demonstrate a landmark-based morphing framework, MSK-Morph, to systematically transform template musculoskeletal models into subject-specific models based on bone geometry derived from medical imaging. MSK-Morph introduces an anatomical landmark-defined musculoskeletal model that embeds segment and joint definitions, and muscle paths, and uses them to systematically and reproducibly morph the model to target bone geometries. MSK-Morph automatically updates the joint definitions and muscle paths to reflect inter-individual skeletal variation while maintaining the structural topology of the original model. MSK-Morph produces landmark-defined musculoskeletal models that remain compatible with existing simulation workflows. By enabling rapid generation of models with subject-specific skeletal geometry, this framework facilitates large-scale musculoskeletal modelling and the development of more diverse generic model libraries.

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X-ray imaging, which provides limited depth perception and exposes patients and clinicians to ionizing radiation. Here we present an augmented reality (AR) system that fuses intravascular ultrasound (IVUS) and electromagnetic (EM) position tracking with preoperative computed tomography (CT) to produce an anatomically accurate, deformation-corrected navigational reference. A robotic device performs ECG-gated pullback of the IVUS probe, capturing 4D aortic motion across the cardiac cycle. We introduce a deep learning architecture for extracting vascular lumen boundaries and side-branch orifices from artifact-prone IVUS streams, and a semantically driven non-rigid CT-IVUS fusion pipeline robust to false positive landmarks. We evaluate the platform with trained surgeons in benchtop phantom studies and in-vivo ovine models, and demonstrate its application to fenestrated endovascular aneurysm repair (FEVAR). Compared to fluoroscopy alone, AR guidance significantly reduces cannulation time, radiation exposure, and cognitive workload, while improving procedural efficiency and safety. Our IVUS-EM and CT aortic datasets are released open source.

Background and Objective: Indications for right ventricular assist device (RVAD) insertion include right heart failure after implantation of a left ventricular assist device or early graft failure following heart transplantation. This study aimed to investigate how the upstream and downstream circulatory network interacts with the Impella RP(R) device. Methods: A numerical model of the Impella RP(R) was implemented within CARDIOSIM(C) software platform for this study. In the numerical configuration, the RVAD aspirated blood from either the right atrium (RA-PA connection) or the right ventricle (RV-PA connection) and delivered it to the pulmonary artery. Only RA-PA connection is the currently used setting for Impella RP(R) in clinical practice. Based on right ventricular (RV) decompression and total flow, our study may help define the need for a direct RV-unloading Impella RP(R). Results: The simulations showed that activating the RVAD in RA-PA mode, regardless of its rotational speed, the mean pulmonary artery pressure (PAP) percentage change was higher than the unsupported condition when the mean systemic venous pressure (SVP) and the pulmonary artery wedge pressure (PAWP) were both set to 20 mmHg. When RV-PA connection was applied, a similar trend was observed although the PAP percentage changes were about halved compared to the RA-PA connection. Conclusions: The Impella RP(R) has the potential to become a valid option for RV support based on current experimental and simulation data. Although already in use, further evaluation in the clinical setting will likely confirm its potential and lead to a more routinely application for RV support.

Fluid shear stress is a critical regulator of endothelial cell function and cardiovascular development, yet in vitro platforms often lack the ability to reproduce physiologically relevant, time-dependent flow environments with quantitative precision. Here, we present the design and validation of a macro-scale cone-plate bioreactor engineered to deliver controlled steady and pulsatile shear stress waveforms to endothelial monolayers and engineered tissues. The system integrates a geometry optimized to minimize secondary flow effects, a feedback-controlled motor capable of reproducing complex waveforms, and a viscosity-informed control framework to account for shear-dependent fluid behavior. Using this platform, endothelial cells were exposed to steady and physiologically derived pulsatile shear stresses. Cells exhibited increased alignment and eccentricity under shear, confirming biologically relevant mechanical stimulation. While pulsatile shear did not significantly alter endothelial neuregulin-1 expression, exogenous administration studies revealed a nonlinear, dose-dependent increase in cardiomyocyte proliferation. Furthermore, co-culture experiments demonstrated that shear-conditioned endothelial cells promote cardiomyocyte proliferation, suggesting a mechanotransduction-mediated paracrine signaling mechanism. Together, these results establish a versatile and quantitatively controlled platform for studying cardiovascular mechanobiology. This device enables systematic investigation of shear-dependent cellular responses and provides a foundation for integrating co-culture systems and three-dimensional engineered tissues under physiologically relevant hemodynamic conditions.

Various ankle-foot conditions (e.g., fractures, diabetic foot ulcers, and post-surgical recovery) require periods of complete non-weightbearing followed by gradually increasing partial loadings. However, existing assistive devices often provide inconsistent or uncomfortable offloading during gait. Additionally, prolonged proximal leg offloading can contribute to muscle atrophy, reduced bone density, and overuse of other body segments. We present a novel offloading ankle-foot orthosis (OLAFO) designed to overcome these limitations. The OLAFO features a patient-specific load-bearing shank brace, designed through a digital workflow and fabricated from a 3D-printed core reinforced with carbon-fiber composite lamination. Interlocking serrated side struts, adjustable in 2 mm increments, modulate load sharing between the shank and plantar surfaces. Furthermore, the OLAFO incorporates contact plates with a rocker profile informed by roll-over-shape measurements to support forward progression and gait symmetry. Proof-of-concept biomechanical verification in one able-bodied participant evaluated complete offloading, five partial-loading levels, and normal gait using a pressure walkway to compute vertical ground reaction forces and impulses. In complete offloading, the affected foot generated no contact pressures. Across partial-loading levels, the foot impulse increased from 14% to 53% of the total load and scaled linearly with strut height adjustments, supporting clinician-prescribed loading increments. Contralateral stance duration increased only modestly compared to commonly used assistive devices, indicating reduced compensatory loading on the intact limb. These findings demonstrate the proof-of-concept feasibility of the OLAFO, highlighting its potential for verifying full offloading and prescribing partial-loading targets during rehabilitation. Future research will evaluate performance across patient populations and clinical rehabilitation tasks.

Targeted drug delivery offers a promising approach for personalized medicine in treating vascular stenosis. However, biomechanical constraints, such as drug washout by high-velocity central blood flow and unintended absorption by healthy vascular walls, complicate the determination of optimal dosing locations. Conventional three-dimensional computational fluid dynamics (CFD) provides precise flow analysis but incurs prohibitive computational costs, making long-term tracking of plaque growth and reverse-engineering of optimal delivery highly inefficient. In this study, we propose a pseudo-3D stochastic growth model that dramatically reduces computational load while capturing the essential dynamics of plaque progression and regression. By modeling the advection-diffusion of lipid and drug particles as a discrete Markov process within a Stokes flow field, we simulate the morphological evolution of plaques under continuous and interrupted targeted therapies. Furthermore, by formulating the drug transport process as an absorbing Markov chain with boundaries at the healthy walls and vessel outlet, we calculate the exact reaching probability and mean first passage time (MFPT) to the plaque. Based on these probability distributions, we discover continuous "Optimal Dosing Curves", which indicate the most effective spatial coordinates for catheter-based drug release to maximize therapeutic efficacy. This mathematical framework not only elucidates the stochastic nature of vascular plaque dynamics but also provides a scalable, computationally efficient foundation for optimizing targeted drug delivery in personalized medicine.

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

Computational analysis of physiological and biomedical systems necessitate efficient geometry representations for high fidelity model predictions, including patient or device specificity. Particle-based Lagrangian computational approaches comprise a valuable approach to gain insights from quantitative velocity and pressure data from computational models. Examples include particle dynamics and transport in human vasculature for diseases such as stroke, thrombosis, and embolisms; and modern targeted drug delivery systems in the vascular network and respiratory airways. However, current particle simulation approaches can bear significant computational expense that scales with both number of particles and background fluid mesh resolution. A significant determinant of this computational expense is the contact resolution between particles and anatomically realistic vessel wall. Here, we develop an efficient particle dynamics model that leverages an implicit representation of real anatomical features using a signed distance field to efficiently resolve particle-wall contact. We outline the underlying algorithmic details, followed by a systematic illustration of performance and accuracy using simplified and analytically defined geometries and flow fields. Subsequently, we present a representative simulation of embolic particles along a human vascular segment where we compare our distance field-based approach against classical wall-contact checks based on assessing particle boundary intersection with triangulated surface mesh. Our approach transforms the underlying Lagrangian contact detection operation into an equivalent Eulerian operation, significantly speeding up bulk particle dynamics computations without significantly impacting accuracy or geometric fidelity.

The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic do-decahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

Evaluating bioinspired design principles in wearable assistive devices provides a unique opportunity to interrogate our understanding of the critical factors that enable agile, stable, and economical human movement. We introduce the BiArticular Thigh EXosuit (BATEX), a wearable device integrating two morphological features found in biological legged systems: biarticular muscles and elastic tissues. BATEX employs two biarticular springs spanning the hip and knee to emulate the human rectus femoris and hamstring muscles, creating beneficial synergy to enhance walking economy. This design enables two energy-shuffling mechanisms: temporal (spring-like storage/return at a joint) and spatial (strut-like transfer across joints). In walking experiments at 1.3 m/s with N = 9 participants, a single compliant biarticular spring yielded a 7% metabolic cost reduction compared to walking without BATEX. Individually optimized configurations further improved metabolic reduction to 9%. BATEX morphology allowed users not only to off-load biological joint power (Assist) but also to increase total power (Augment). Across all exosuit configurations, the mechanical impact of the exosuit was reflected by a significant correlation between changes in users biarticular muscles activity and changes in net metabolic rate. In sum, compliant-biarticular exosuit architectures can concurrently assist and augment human lower-limb joint function, providing significant metabolic savings during walking.

Accurate estimation of body segment inertial properties is essential for biomechanical analyses, yet commonly used scaling methods rely on limited datasets and do not generalize well across diverse adult body morphologies. We developed a data-driven framework that estimates segment lengths, masses, centers of mass, and moments of inertia using regression models trained on large anthropometric datasets (ANSUR II and NHANES) combined with a geometric representation of 16 body segments. The framework uses height, weight, and sex as primary inputs and incorporates waist and hip circumferences or other length and cross-sectional measurements when available to refine body-shape predictions. For individuals with obesity, additional geometric rules redistribute excess mass based on segment-specific volume changes. The resulting models reproduced segment lengths, cross-sectional dimensions, and lumped segment masses within the ranges observed in the training datasets and outperformed published regression equations, particularly at higher body mass index (BMI) values. To promote broad adoption, we provide an open-source API in Python that performs the full parameter estimation using the trained models. This framework offers an accurate and accessible method for estimating adult body segment properties across a wide range of body sizes and shapes, supporting improved motion analysis, musculoskeletal simulation, and clinical biomechanics.

Purpose: Accurate assessment of the pressure gradient ({Delta}P) across aortic coarctation (CoA) is critical for determining disease severity and the need for intervention. Current non-invasive methods are unreliable, while invasive catheterization remains the clinical gold standard. This study evaluates a novel MRI acquisition strategy, 4D-FlowP, that simultaneously encodes blood velocity and acceleration to enable reliable non-invasive pressure gradient mapping in CoA. Methods: Patient-specific compliant aortic phantoms were created from clinical MRI data of two patients with CoA. Additional geometries were synthetically generated by increasing stenosis severity. Phantoms were studied in an MRI compatible flow loop under physiologically realistic flow and pressure conditions. Pressure gradients were estimated using conventional 4D-Flow MRI, 4D-FlowP, and fluid-structure interaction (FSI) simulations. Results were compared against ground-truth catheter-based measurements across multiple flow rates and stenosis severities. Results: Conventional 4D-Flow consistently underestimated {Delta}P (slope = 0.63, R2=0.75) relative to catheter measurements. In contrast, 4D-FlowP demonstrated substantially improved agreement (slope = 0.95, R2=0.75). FSI simulations showed the highest overall agreement with catheter-derived {Delta}P (slope = 1.14, R2=0.82). Scan times for 4D-FlowP were comparable to 4D-Flow (26 vs. 24 minutes). Conclusion: 4D-FlowP enables a more accurate MRI-based pressure gradient mapping in CoA than conventional 4D-Flow, when compared to ground truth catheter measurements. These findings support further in vivo evaluation of 4D-FlowP as a non-invasive alternative for functional assessment of CoA severity

Dysphagia, difficulty swallowing due to irritation or damage to the esophagus, is one of the most common complications following anterior cervical discectomy and fusion (ACDF), the most frequently performed cervical spine procedure in the United States. Surgical retraction hardware imposes sustained compression on the esophagus during surgery, generating nonuniform stress and strain fields that may contribute to temporary postoperative soft tissue damage. Current intraoperative assessment relies on visual inspection and manual inspection by the surgical team and does not provide quantitative measures of esophageal deformation, strain, or retraction displacement. Here, we present a comprehensive mechanics analysis of esophageal compression during ACDF that integrates experiments on esophageal phantoms, nonlinear finite element modeling, and theoretical thick-wall scaling relationships. Modeling results quantify peak contact pressures and corresponding stress distributions, identifying conditions under which circumferential strain in the compressed esophageal wall increases sharply as localized pressures approach the upper physiological range ([~]6-17 kPa). Parametric investigation of retractor blade width, placement depth, and polymeric biocompatible coating properties demonstrates that targeted, yet mechanically simple, design modifications can help to attenuate strain concentrations. In particular, the introduction of compliant polymeric coatings redistributes contact loads and reduces peak wall stress by up to 20% relative to unbuffered blades (17 kPa to 13.5 kPa). Increasing blade width from 20 mm to 50 mm further decreases peak interface stress from 2.48 kPa to 0.45 kPa, corresponding to an 82% reduction. Reducing these stresses may help limit mechanically induced complications such as postoperative dysphagia. Experiments performed on esophageal phantoms with embedded pressure sensors replicate surgical ACDF retraction protocols under displacement-controlled conditions. This setup establishes physiologically relevant loading and enables quantitative validation of computational predictions by correlating measured voltage output with contact pressure and esophageal deformation. Measured relationships between applied retraction displacement, contact pressure, and tissue deformation govern stress amplification during ACDF retraction. Together, these results establish a predictive mechanics framework that links retractor blade design variables to esophageal stress fields, providing quantitative criteria to mitigate soft tissue damage during ACDF. HIGHLIGHTSO_LI2D and 3D finite element models quantify esophageal wall stress during anterior cervical discectomy and fusion (ACDF) retraction. C_LIO_LIRetractor blade geometry influences stress distribution, with wider blades reducing localized tissue loading by up to 82% likely associated with post-surgical dysphagia. C_LIO_LICompliant polymeric buffer layers attenuate pressure and smoothen stress gradients to reduce peak tissue loading by up to 20% during retraction. C_LI

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

Transcranial focused ultrasound is an emerging noninvasive neuromodulation technique offering high spatial precision and deep penetration. However, in deep brain neuromodulation in mice, the skull base attenuates the signal, distorting the focal region and creating off-target peaks. This study presents a machine-learning-driven simulation framework to optimize a bowl-shaped phased-array transducer design for hypothalamic targeting and compares its performance with that of time-reversal phase conjugation and a single-element baseline. A computed tomography-based mouse head model was used for full-wave acoustic simulations with a fixed bowl geometry (10 mm aperture, 6 mm radius of curvature). Designs were evaluated across various parameters, including operating frequency (0.2-1.5 MHz), active element count (16, 32, 64, 128), and element diameter (300-550 m). The evaluation employed four metrics: the presence of a -3 dB focal region within the hypothalamic area, axial focal length defined by the -3 dB full-width at half maximum, focal fragmentation measured by the -3 dB blob count, and targeting displacement. Random Forest surrogate models were trained in simulation outputs and paired with the Non-dominated Sorting Genetic Algorithm II to reduce computational costs during multi-objective optimization. The forward-excitation-optimized phased-array design (0.73 MHz, 128 elements, 381 m element diameter) achieved a focal region at the hypothalamic target with a full width at half maximum of 0.67 mm, a blob count of 1, and a targeting displacement of 0.38 mm when placed 1 mm below the nominal position. Time-reversal phase conjugation further improved confinement and targeting (full width at half maximum: 0.59 mm; displacement: 0.37 mm). Limitations include reliance on a single mouse anatomy, and incorporating additional CT-derived anatomies should enhance generalizability across strains, ages, and sexes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/727023v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@62de15org.highwire.dtl.DTLVardef@e26e57org.highwire.dtl.DTLVardef@1ba4893org.highwire.dtl.DTLVardef@f2c77a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA CT-based acoustic simulation and machine-learning framework was developed to optimize bowl-shaped phased-array transducers for mouse hypothalamic tFUS neuromodulation. C_LIO_LIRandom Forest surrogate models coupled with NSGA-II efficiently identified optimized array designs across frequency, element count, and element diameter. C_LIO_LIThe optimized phased-array design produced a compact hypothalamic focus with submillimeter targeting displacement, with further confinement achieved using time-reversal phase conjugation. C_LI