International Journal for Numerical Methods in Biomedical Engineering

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

Devireddy et al. [1] previously showed that an enthalpy-based, macroscale model simulated tissue freezing histories are in close agreement with a coupled model incorporating cellular water transport and intracellular ice formation (IIF). That comparison, however, was performed for a limited set of tissue geometries, cooling conditions, and biophysical parameters. Here, we revisit that conclusion by reimplementing the published model from its governing equations and systematically expanding the parameter space to determine when microscale processes alter the predicted macroscale freezing response. We incorporate both surface catalyzed nucleation (SCN) and volume catalyzed nucleation (VCN) of intracellular ice with cellular osmotic dehydration and examine a broad range of tissue dimensions, cooling rates, convective boundary conditions, membrane permeabilities, cell sizes, activation energies, intracellular water fractions, and nucleation rates. Linearization of the cellular water transport equation about osmotic equilibrium yields an osmotic relaxation timethat can be compared with the local residence time of tissue undergoing phase change. Their ratio defines a dimensionless group, that organizes the transition between regimes in which the uncoupled enthalpy formulation and the coupled micro and macroscale formulation produce similar or substantially different results. Across the one-dimensional parameter space examined here, the two models remain within a prescribed thermal-error bound for the dimensionless group below approximately 0.1, whereas the discrepancy increases systematically above this threshold. Model divergence also depends on intracellular ice nucleation, i.e., when nucleation is sufficiently slow, cellular water transport can become rate limiting for latent heat release. Conversely, sufficiently rapid intracellular ice formation reduces the macroscopic discrepancy by providing an additional pathway for latent-heat release. Multidimensional simulations further show that geometry alone can move a nominally conventional freezing protocol into the coupled regime. In particular, multidirectional cooling can reduce the local residence time sufficiently to produce substantial differences in predicted frozen volume even when the corresponding thermal histories appear similar. Finally, we formulate an inverse approach for estimating IIF parameters from tissue scale thermal histories. Synthetic simulations indicate that parameter recovery is feasible within a finite range of cooling conditions, providing a testable strategy for future experimental measurements. Together, these results provide a mechanistic criterion for deciding when microscale biophysical processes must be retained in macroscale tissue-freezing models.

Purpose: Atrial fibrosis is associated with thromboembolic risk in atrial fibrillation, but how fibrosis-related contractile impairment alters blood transport in the left atrium (LA) and left atrial appendage (LAA) remains unclear. Prior multi-physics simulations coupled electromechanical (EM) and computational fluid dynamics (CFD) models to show that fibrosis impairs LA function and flow, but were limited to booster-only contraction and could not separate the effects of fibrosis burden from its spatial distribution. Here we examined how fibrosis burden and spatial pattern independently relate to long-term LA and LAA hemodynamics in full-cycle, multi-beat simulations on a common LA anatomy. Methods: Three fibrosis patterns derived from late gadolinium enhancement MRI were considered: one from the patient providing the common anatomy and two from additional patients, with the latter mapped onto the common anatomy via universal atrial coordinates. All three patterns were applied globally at matched burdens of 15.6% and 31.2%, yielding six global-fibrosis cases and a non-fibrotic reference. Two patterns were also confined to the LAA at three local burdens (10%, 25%, and 50%), yielding six LAA-fibrosis cases. A 3D EM model for the LA anatomy was coupled to a 0D circulatory model, so that the simulations resolved the reservoir, conduit, and booster phases. Full-cycle EM-derived wall motion drove subsequent multi-beat CFD simulations. Region-specific emptying fraction (EF), non-dimensional kinetic energy (KE), and the 90th percentile of blood residence time (TR) were evaluated for the LA and LAA. Results: Left ventricular stroke volume remained nearly constant across cases, while increasing global fibrosis burden progressively reduced LA and LAA EF (LA: 36% to 30-31%; LAA: 44% to 28-38%) and monotonically increased LA TR across all patterns. Non-dimensional LA KE showed an approximately quadratic association with LA EF across patterns, whereas LAA KE was more scattered but still increased with LAA EF. In contrast, the relationship between the 90th percentile of LAA TR and LAA EF showed pattern-specific, non-monotonic trends. Conclusions: Fibrosis burden and spatial distribution jointly shape LA and LAA hemodynamics, but through different mechanisms. LAA EF is a robust functional descriptor of chamber-averaged kinetic energy regardless of fibrosis pattern, whereas LAA blood stasis depends additionally on the spatial distribution of fibrosis beyond what bulk emptying fraction predicts. These findings support incorporating imaging-derived fibrosis pattern, and not only burden, alongside functional metrics when assessing thrombogenic risk in the LAA.

Healing of the gastrointestinal (GI) tract following surgical interventions is governed by coupled mechanobiological processes and may lead to severe complications such as perforation or stricture formation. A first comprehensive mechanobiological modeling framework of GI healing is herein proposed. The framework addresses two clinically relevant applications of bioprinting: endoscopic treatment of GI tissue defects and bioprinting-assisted surgical anastomosis. Tissue remodeling is based upon a hyperelastic material model with fiber dispersion coupling the spatiotemporal reaction diffusion dynamics of fibroblasts and cytokines with collagen deposition, reorientation, and plastic deformation. An active stress formulation drives fibroblasts and myofibroblast contraction while a multiplicative decomposition approach is adopted for collagen remodeling as a permanent change. The governing equations are discretised in time with an implicit Euler scheme, finite element in space and solved by a staggered algorithm implemented in the open-source platform FEniCS. Numerical simulations show that the initial collagen fibers content and orientation strongly influence fibroblast infiltration and scar contracture formation. The study quantifies the advantage of having a collagen bioprinted architecture aligned with tissue fibers, to reduce structural remodeling distortion. The model also predicts a strong correlation between collagen deposition and luminal burst pressure during the recovery of mechanical integrity in GI anastomosis.

A densely vascularized tissue can still be starved of oxygen. Tissue oxygenation depends heavily on vessel arrangement, blood flow distributions, and cellular metabolism. Resolving these effects from individual constituent vessels to whole organs has historically been computationally prohibitive. We introduce the Cardiovascular Analysis and Simulation of Circulatory Advection, Diffusion, and Exchange (CASCADE) toolkit, which makes vessel-resolved oxygen transport tractable at the organ scale through a novel screened Green's-function formulation. On laptop-class hardware, CASCADE simulates networks containing thousands of vessels in under one second and a full biventricular human heart model in about two minutes. We validate spatial oxygen and cell-survival predictions against experiments in perfused engineered tissues and matched finite-element simulations. In engineered tissues, sweeps across thousands of network designs and physiological conditions reveal when vessel density, oxygen supply, or network architecture limits viability, establishing design rules for sustaining thick constructs. In a human heart model, CASCADE translates coronary occlusion, anemia, polycythemia, and hypoxemia into distinct myocardial oxygenation patterns, revealing where oxygen delivery fails and how much tissue is at risk.

Fibrotic strictures are among the most disabling complications of Crohn's disease, permanently narrowing the bowel and impairing motility, yet no approved therapy reverses them. Chronic inflammation alters pacemaker-network coupling, smooth-muscle excitability, and calcium-dependent contractility, while fibrosis thickens the bowel wall, narrows the lumen, and changes tissue mechanics. The relative contributions of these coupled electrical, contractile, and structural alterations to motility loss remain unclear. To address this gap, we develop an integrated electromechanical finite-element framework for fibrostenosing Crohn's disease that couples a fibrosis-driven growth model with a FitzHugh-Nagumo electromechanical model. A full-factorial 25 design of experiments is used to quantify the relative effects of electrical diffusivity, excitation threshold, peak active stress, wall stiffness, and hypertrophic remodeling on cyclic lumen-volume deformation. Motility is quantified by the standard deviation of lumen volume over one contraction cycle. Within the parameter ranges examined, increased wall stiffness emerged as the dominant contributor to motility loss, followed by impaired smooth-muscle contractility. Changes in excitation threshold, hypertrophic remodeling, and electrical diffusivity produced substantially smaller effects. Pairwise interactions were small relative to the dominant main effects, indicating that the mechanisms contributed largely through their individual effects. Our findings suggest that limiting wall stiffening while preserving smooth-muscle contractile function may provide a therapeutic strategy for maintaining intestinal motility in fibrostenosing Crohn's disease.

Predictive biomechanical models of the tricuspid valve require accurate representation of the chordae tendineae, yet subject-specific chordal geometry is difficult to reconstruct from non-invasive imaging. Here, we adopt a framework for generating functionally equivalent synthetic chordae without prior knowledge of in vivo chordal attachments. To this end, we first adapt an anatomy-informed hyperelastic shape-matching method that establishes correspondence between end-diastolic and end-systolic leaflet configurations using chordal-mimicking forces, a rigid contact template, and a Gaussian-smoothed locally corrective pressure field. We then generate synthetic chordal insertion sites using zone-based rejection sampling and calibrate the unloaded length of each chord by combining reaction forces with the chordal stress-stretch relationship. The framework was evaluated using Texas TriValve 1.1, a high-fidelity finite element model of a human tricuspid valve validated against beating-heart echocardiography. We found that shape matching reproduced the target end-systolic geometry with a mean inter-surface distance of 0.29 {+/-} 0.35 mm. Moreover, synthetic chordal insertions faithfully reproduce end-systolic leaflet deformations. We subsequently examined synthetic chordal configurations containing 202, 225, and 450 insertions, informed by measurements from eight explanted human tricuspid valves. Here, increasing insertion number reduced mean inter-surface distance from 0.63 {+/-} 0.52 mm to 0.49 {+/-} 0.44 mm and reduced contact area errors from 3.52% to 0.59%. Across all configurations, mean maximum principal stretch errors in leaflet belly regions remained below 2.4%, while areal strain errors ranged from 0.34% to 10.01%. These results demonstrate that anatomically informed shape matching coupled with stress-based chordal calibration can reproduce tricuspid valve closure without explicit subject-specific chordal geometry. This framework provides a foundation for generating synthetic subvalvular anatomy for future imaging-derived, predictive tricuspid valve models.

Fractional differential equations are increasingly used to represent temporal memory in tumour immune and pharmacokinetic models, but changing the derivative order without a consistent time scale can make comparisons difficult to interpret. We study a dimensionless, commensurate Caputo system for tumour, immune, normal cell, and drug states. We establish positivity, boundedness, the tumour invasion threshold, and local stability conditions. We then use a fractional Adams Bashforth Moulton predictor corrector method with an explicit mesh convergence study. Two conclusions correct potentially misleading interpretations of the model. First, the equilibrium locations are independent of the fractional order: decreasing the order changes transient time scales but does not by itself alter the model's asymptotic states. Second, after a unit bolus the drug equation has the exact solution C({tau})=E(-K{tau}). Its maximum is therefore the initial concentration, not a delayed peak. For 0<<1, the algebraic tail makes the infinite-horizon area under the concentration curve diverge. In the illustrative coupled simulations, the apparent late tumour reduction at small is consequently driven by persistent modelled drug exposure and must not be read as evidence of superior therapy. All parameters are dimensionless and illustrative; no patient data are fitted. Source code and generated tables are included to make every numerical statement reproducible.

Introduction: The femoropopliteal artery undergoes substantial axial compression, bending, torsion, and pinching during limb flexion. When an implanted stent does not accommodate these deformations, repetitive biomechanical mismatch may alter the vascular response to implantation and contribute to restenosis. We tested whether designing a peripheral stent to better accommodate physiologic limb-flexion deformation reduces the subsequent inflammatory and neointimal response. Methods: A nitinol stent was developed using multi-objective computational optimization to maintain arterial apposition while reducing wall stress and luminal pinching during physiologic deformation. Sixteen Yucatan minipigs were studied in independent 3- and 6-month cohorts (n=8 each). Each animal received the optimized stent in one limb and a commercial stent with representative biomechanical behavior contralaterally. CTA quantified in vivo limb-flexion deformation, while CT and histology assessed stenosis, neointimal accumulation, inflammatory-cell burden, SMA-positive tissue, and extracellular-matrix composition. Results: The optimized stent accommodated greater axial shortening than the commercial comparator at both 3 months (11.2{+/-}2.7% vs. 6.6{+/-}2.1%, p=0.003) and 6 months (13.7{+/-}4.4% vs. 6.8{+/-}2.3%, p=0.004). Intimal hyperplasia volume was lower with the optimized stent at 3 months (227.5{+/-}85.2 vs. 350.0{+/-}130.5 mm3, p=0.002) and 6 months (183.6{+/-}46.2 vs. 261.2{+/-}60.6 mm3, p=0.001), with significantly lower maximum, mean, and median stenosis at both timepoints. At 3 months, macrophage and total inflammatory-cell counts were 51% and 45% lower, respectively, with the optimized stent. Histologic tissue area was 28% lower at 3 months and 12% lower at 6 months. SMA-positive tissue fraction was higher with the optimized stent at both timepoints, whereas elastin, glycosaminoglycan, and collagen fractions were similar between devices. Discussion: Improved accommodation of limb-flexion deformation was associated with a smaller inflammatory and neointimal response to stent implantation through 6 months, while overall neointimal matrix composition remained largely unchanged. These findings provide in vivo evidence that biomechanical compatibility with the dynamic lower-limb environment can influence vascular healing and should be considered a mechanistic design criterion for peripheral artery stents.

Endothelial cells integrate biomechanical and biochemical cues to regulate vascular function, which can be modeled using cell signaling networks. Yet, the extent to which their signaling behavior is determined by network topology versus reaction-specific parameterization remains unclear. We developed a continuous logic-based ordinary differential equation model of vascular endothelial signaling containing 70 species and 123 regulatory reactions assembled from the literature. Model predictions were evaluated against 78 literature-defined directional responses to increased wall stress, wall shear stress, hypoxia, and TNF. We first tested whether a single global parameter triplet describing reaction weight (w), Hill coefficient (n), and half-maximal activation (EC50) could reproduce these observations. Investigator-selected global parameters reproduced 62 of 78 responses (79.49%). Monte Carlo sampling of 100,000 globally constant parameter sets improved agreement to 69 of 78 responses (88.46%), indicating that network topology alone captured most qualitative signaling behavior but remained sensitive to parameter choice. Allowing reaction-specific parameters increased the best agreement to 75 of 78 responses (96.15%). We then used a genetic algorithm to identify the smallest subset of reactions requiring reaction-specific specification while retaining a common constant-parameter spine for the remainder of the network. Complete agreement (100%) with all 78 literature-defined responses was achieved with reaction-specific parameterization of only 10 of 123 reactions. The optimized model was subsequently used to simulate continuous changes in wall shear stress, revealing pathway-dependent nonlinear responses, including monotonic increases in nitric oxide and pSmad1/5, nonmonotonic responses in endothelin-1 and NF-{kappa}B, and a biphasic pSmad2/3 response. These findings show that most observed endothelial signaling behavior can be reproduced by a fixed network topology with broadly shared reaction parameters with only a limited subset of interactions requiring quantitative specialization. This framework provides a compact endothelial signaling model for investigating mechanobiological regulation and for future integration with multiscale models of vascular adaptation and disease.

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

Background: Ascending thoracic aortic aneurysm (ATAA) is characterized by progressive biomechanical remodeling that is incompletely captured by maximum aortic diameter. The relationships between age, sex, aortic valve phenotype (BAV vs TAV), and aneurysmal mechanics remains incompletely understood. Methods: Ascending aortic specimens from 54 patients undergoing elective ATAA repair were evaluated. Biaxial testing quantified Green strain and local stiffness at membrane tensions of 60 and 120 N/m. Opening angle was used to assess circumferential residual stress and stretch. Associations with age were evaluated using Pearson correlations, and differences between sex and valve phenotype were also explored. Multivariable linear regression models including age, sex, and valve phenotype were used to evaluate their adjusted associations with each mechanical outcome. Results: Increasing age was associated with increased local stiffness and reduced Green strain and remained the strongest determinant of biaxial mechanical properties after adjustment. Compared with tricuspid aortic valve specimens, BAV specimens exhibited lower Green strain together with lower local stiffness. Females demonstrated greater Green strain in unadjusted analyses; however, after adjustment, sex remained associated only with longitudinal Green strain at 60 N/m. None of the evaluated clinical characteristics consistently explained opening angle or residual stretch. Conclusions: Age was the predominant clinical determinant of biaxial mechanical remodeling in ATAA, whereas valve phenotype demonstrated selective associations and sex contributed comparatively little after adjustment. Circumferential residual stress appeared largely independent of these clinical characteristics, suggesting that residual stress and biaxial mechanics capture complementary aspects of aneurysmal remodeling.

Buruli ulcer (BU) is a neglected tropical skin disease caused by infection with Mycobacterium ulcerans. The mode of its transmission into the skin has not been fully clarified, but it is believed to involve mechanical transfer, perhaps by insect bites. M. ulcerans secretes a toxin called mycolactone which causes necrosis of the skin and formation of large skin lesions. Understanding the development of these skin lesions over time is crucial to understand the pathogenesis of BU. We formulate a mathematical model to describe the space-time dynamics of M. ulcerans bacteria, mycolactone and skin cell densities as well as fibrin deposition. We investigate the relative contributions to ulcer formation of direct cytotoxicity of mycolactone and ischaemia following the loss of coagulation control. Our results are consistent with existing biological evidence that higher mycolactone concentrations accelerate cell death. Furthermore, the model indicates that the rate of wound expansion is primarily controlled by the diffusion and net birth rate of bacteria, which sets the overall scaling for the speed of wound expansion, along with two dimensionless constants that define the shape of the trajectory of wound edge position against time, namely the ratio of the mycolactone diffusion coefficient to the bacterial diffusion coefficient, and the ratio of the mycolactone degradation rate to the bacterial net birth rate. When mycolactone diffuses significantly faster than bacteria spread or when mycolactone degrades more slowly than bacteria reproduce, the initial development of the wound is faster. In contrast, the long-term wound expansion speed appears to be relatively unchanged across a wide range of the dimensionless parameters.

Computational models of the middle ear are typically developed within the linear range of the middle ear's response, which corresponds to typical sounds in daily life. Understanding middle-ear biomechanics under high-intensity sounds is also important for assessing potential auditory damage under adverse conditions. In this study, a finite-element (FE) model of the human middle ear plus the external ear canal is presented, employing a first-order Ogden hyperelastic material formulation and time-domain nonlinear analysis to capture nonlinear deformation of soft tissues and the resulting middle-ear response. The model was analyzed using pure-tone pressure excitations applied at the ear canal entrance over the 80 Hz to 8 kHz frequency range and 110 to 180 dB sound pressure levels (SPL). The ear canal pressure near the tympanic membrane (TM) and full-field three-dimensional response of the middle ear structures were examined. Across the investigated frequency range, the simulated middle-ear motion exhibited a transition from predominantly piston-like behavior at low frequencies, up to 1 kHz, to increasingly intricate multi-modal and multi-directional vibration patterns at higher frequencies. Nonlinear behaviors became increasingly significant at high frequencies and high stimulus levels, including compressive/expansive pressure-displacement relationships, waveform distortions, and the emergence of non-harmonic spectral components. The model developed in this study provides a computational framework for studying nonlinear sound transmission through the middle ear and its implications for high-intensity acoustic exposure. The findings contribute to a better understanding of middle-ear mechanics under adverse conditions and support future investigations of acoustic trauma and hearing protection strategies.

Purpose: to introduce a novel magnetic resonance imaging (MRI) technique, MUSLIS MRI (muscle and lipid suppression), that generates two co-registered 3D sets of images in a single acquisition: one set with lipid signal suppression (MUSLIS1) and a second set with muscle signal suppression (MUSLIS2). Materials and Methods: Numerical simulations based on Bloch equations were performed to optimize the sequence parameters; the MP2RAGE sequence was employed for the implementation of MUSLIS imaging. MRI experiments were performed in vivo at 3 T. Quantitative measurements of signal contrast were carried out on MUSLIS1 and MUSLIS2. Various combination images and T1 parametric maps of the thighs were generated from the two co-registered 3D sets. Results and Discussion: Optimal sequence parameters to achieve lipid suppression in one dataset (MUSLIS1) and muscle suppression in the other dataset (MUSLIS2) were determined by numerical simulations. MUSLIS imaging was validated in vivo at 3 T: MUSLIS1 images and MUSLIS2 images displayed excellent lipid and muscle suppression, respectively. The values of the signal contrast between subcutaneous lipids and muscle, as well as between bone marrow lipids and muscle reached values close to 0.8. Thus, MUSLIS would allow for tissue characterization of lesion/masses and direct visualization of the adipose tissue. Combination images, generated from these two intrinsically co-registered sets of images (MUSLIS1 and MUSLIS2), displayed different contrasts and improved signal characteristics, such as mitigation of signal inhomogeneities. The development of MUSLIS imaging for musculoskeletal imaging followed the concept introduced with the FLAWS, an MRI sequence designed for brain MR imaging to generate two 3D sets of images, one with white matter suppression and the other with cerebrospinal fluid suppression. Similarly to FLAWS, which provides T1 maps of the brain, MUSLIS imaging yields T1 parametric maps of the musculoskeletal system. Furthermore, the unique characteristics of the combination images make MUSLIS imaging a potentially attractive technique at field strengths higher than 3 T. Conclusion: The proof-of-concept of a novel approach to musculoskeletal imaging, MUSLIS MRI, is proposed here and demonstrated by numerical simulations and MRI experiments performed in vivo at 3 T. Keywords: MRI, muscle, lipids, musculoskeletal imaging, high field MRI, fat suppression, FLAWS MRI, MP2RAGE, MUSLIS, inversion recovery, adipose tissue, bone marrow, subcutaneous fat, T1 mapping, 3 T, 7 T, neuromuscular disorders, metabolic diseases, muscular dystrophy.

Marker-based optical motion capture remains the gold standard for kinematic analysis across biomechanical research. However, when tracking dynamic movements, severe occlusions often create missing data and noisy signals that require time-consuming manual cleaning. Existing automated methods usually rely on predefined skeleton models or need massive training datasets for machine learning solutions. This study presents the AMP: an automated marker based motion capture processing pipeline. The AMP is a fully analytical approach that uses hierarchical algorithms including 1) rigid-body constraint, 2) Kalman Filter state estimation and 3) kinematic pattern match to label markers and fill data gaps without manual human intervention. First, the algorithm identifies and removes mislabeling markers before any data scrubbing. During the data cleaning process, our hierarchical algorithm uses a translation-based step to track segments even when there are insufficient donor markers to form a full rigid body. To reconstruct missing marker trajectories, the pipeline initially uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for short marker gaps. However, the PCHIP creates massive spatial errors during long occlusions, the system automatically switches to rigid-body tracking for gaps over 40 ms. Results showed the pipeline drastically reduced processing time compared to manual human expert methods by 73.5%. In addition, the AMP kept spatial errors within 10 mm of the manual ground truth. By using this combined hierarchical approach to match marker processing accuracy, the pipeline provides a fast, general-purpose tool that fully automates data cleaning and saves hours of manual effort.

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

It is important to determine whether native state dynamics drive the misfolding and oligomerization of the prion protein, which are important events in prion disease, and how they are modulated by conformational conversion. Native (N) mouse prion protein (moPrP) is known to form small (OS) and large (OL) oligomers rich in {beta}-sheet, and in this study, photoinduced electron transfer-fluorescence correlation spectroscopy (PET-FCS) has been used to characterize intramolecular dynamics within individual monomeric units in both isolated OS and OL, as well as the diffusion properties of the oligomers. It is estimated that OS and OL comprise of about 15 and 55 monomeric units, respectively. Microsecond dynamics at each of the two regions that are the 1-3 and 2-3 interfaces of native protein are distinct in N, OS and OL, although they occur on very similar timescales. Analysis of the evolution of the distribution of diffusion times, determined using the maximum entropy method, indicates heterogeneity in the oligomerization reaction. Analysis of the change in the fluctuations which occur in two different timescales in the native state ensemble shows that they are damped more at the erstwhile 1-3 interface than the erstwhile 2-3 interface. The difference in the extent of damping at the erstwhile 1-3 and 2-3 interfaces can be explained on the basis of the structural changes known to occur across each region. The changes in dynamics occur concurrently in both regions, indicating that the structural changes accompanying conformational conversion also occur simultaneously during the oligomerization of moPrP.

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

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