IEEE Transactions on Biomedical Engineering

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.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5 to 3 degrees across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7-7.8 degrees; manual: 8.3-24.2 degrees; neutral: 15.5-35.5 degrees). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.

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.

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

Measurements of human movement are critical for evaluating patients with neuromuscular diseases. However, current clinical tools are limited in sensitivity and can fail to detect the full spectrum of movement changes patients may exhibit. These limitations can obscure early signs of decline, delaying interventions that could preserve function and independence. OpenCap, a smartphone application that performs video-based markerless motion capture, can help address this gap by enabling scalable, out-of-lab measurements of human movement. Here, we present a method for both detecting disease and quantifying the severity of impaired movement from smartphone videos of patient motion. We used OpenCap to measure kinematics from 337 individuals with diagnoses spanning 12 rare, genetically-defined neuromuscular diseases and 78 age-matched healthy controls. Our dataset is the largest existing publicly available resource of body segment kinematics for this class of diseases. These data were collected in neuromuscular clinics during regular clinic visits as well as at conferences and community events across the United States. We trained deep learning models on these kinematic time series to differentiate between participants with neuromuscular disease and controls (AUROC = 0.97 [0.93-1.00]). We also developed a movement impairment severity score that correlates with both participant ratings of their own functional abilities ({rho} = -0.81, p < 0.001) and existing clinical tests of movement capabilities such as run speed ({rho} = -0.83, p < 0.001). Our methods for detecting disease and deriving quantitative biomarkers of movement impairment from smartphone videos have the potential to complement clinical assessments, inform screening protocols, and improve clinical trial endpoints.

Objective: The aim of the current study was to evaluate the feasibility of a multi-channel distributed wearable ultrasound (D-WUS) system capable of simultaneously probing dynamic muscle activity across multiple anatomical sites to assess neuromuscular coordination during physical activity. Methods: Twenty NCAA Division I women athletes (8 with prior knee injury) performed countermovement jumps (CMJs) on dual force plates while wearing a four-channel D-WUS system targeting the vastus lateralis (VL) and vastus medialis oblique (VMO) in both limbs. Real-time M-mode ultrasound data were processed using a composite factorization approach integrating nonnegative matrix factorization and principal component analysis (CF-PCA) to extract dominant muscle-contraction patterns. Correlation and timing metrics between VL and VMO were compared across injury status. Results: CMJ performance metrics (jump height, RSImod) did not differ between groups (p > 0.05). However, D-WUS revealed significant differences in muscle coordination in the right limb: VL-VMO correlation (p = 0.029) and timing symmetry (p = 0.019) were reduced in participants with prior knee injury; left-limb differences were not significant. Right-limb timing symmetry was negatively correlated with SMFA score (rho = -0.48, p = 0.033). Functional survey scores (SMFA) also differed significantly (p = 0.001), while VISA-P scores did not. Conclusion: These preliminary findings provide feasibility-level evidence that distributed wearable ultrasound can detect limb-specific neuromuscular coordination differences not reflected in force-plate biomechanics, motivating larger prospective studies to establish reliability and clinical utility. Significance: With further validation, this approach may offer a scalable, objective means of monitoring muscle contraction during rehabilitation.

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.

Background and Objectives: Code-modulated visual evoked potentials (c-VEPs) provide fast and reliable control signals for reactive brain-computer interfaces (rBCIs). However, deployment beyond laboratories remains limited by visually intrusive stimulation, reliance on wet electroencephalography (EEG), and false activations during non-control periods. We introduce a usability-oriented c-VEP rBCI combining low-salience textured stimulation, slow flashing codes, asynchronous evidence accumulation, and semi-dry EEG acquisition. Methods: Thirty participants evaluated the system on a standard screen using an 11-class keypad typing task. Two identical online typing sessions were separated by a 2-min video-viewing period serving as an explicit idle condition. Feasibility was then assessed in virtual reality (VR) through a proof-of-concept shell-game task involving head movements and immersive rendering constraints. Visual comfort, peripheral distraction, and fatigue were assessed subjectively in both conditions. Results: In the keypad task, the system achieved 99.8% accuracy with 33 s of calibration data and a mean decoding time of 4.3 s. No spurious commands occurred during the tested 2-min idle condition. In VR, online accuracy reached 100%, with a mean decoding time of 5.6 s. Participants reported high visual comfort and minimal peripheral distraction in both conditions, although fatigue was higher in VR. Conclusion: The proposed c-VEP rBCI combines short calibration, high accuracy, comfortable stimulation, semi-dry EEG, and asynchronous decision-making. Performance during the explicit idle condition and VR implementation support the feasibility of wearable and practical c-VEP systems for self-paced interaction beyond laboratory environments. These findings encourage evaluation in longer idle periods and real-world tasks.

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.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

Background: Articulated laparoscopic instruments require a reliable mechanical connection between the handle and shaft to maintain the intended instrument configuration during surgical manipulation. The handle shaft locking interface should resist unintended relative displacement under external loading. However, the appropriateness of a specific slip resistance threshold must be evaluated in relation to the instrument design and intended conditions of use. Objective: To evaluate the mechanical performance associated with an 8 N slip resistance specification for the locked handle shaft interface of an articulated laparoscopic instrument using a controlled bench model. Methods: A bench based mechanical evaluation method was designed using the instrument under test, mechanical fixtures, a calibrated force measurement device, and displacement measurement equipment. After engagement of the handle shaft locking mechanism, external forces were applied to evaluate resistance to relative displacement. Testing included axial loading, loading in relevant transverse directions, and repeated locking unlocking cycles. The primary outcome was the force corresponding to the onset of relative slippage at the locked interface. Secondary outcomes included displacement under load, complete locking failure, and changes in mechanical performance after repeated operation. Acceptance criteria should be established before confirmatory testing. Results: All 30 instruments from three production batches met the 8 N axial requirement (30/30). The axial slip onset force was 12.40 N . Under a sustained load of 8 N for 60 s, mean relative displacement increased from 0.06 mm to 0.10 mm, and mean residual displacement was 0.02 mm. After 1,000 cycles, the axial slip-onset force was 12.00 N . Conclusions: This study provides a bench-testing framework for evaluating an 8 N slip resistance specification for the handle shaft locking interface of an articulated laparoscopic instrument. Whether this specification provides adequate mechanical stability can be determined only by comparing the measured performance with engineering acceptance criteria established in advance on the basis of the instrument's intended use and foreseeable loading conditions.

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

Point-of-care (POC) molecular detection has enabled rapid and accessible testing at or near the site of need, facilitating timely decision-making in clinical, field, and industrial settings. Despite these advancements, molecular detection platforms remain limited in their ability to prepare DNA from complex tissue matrices, restricting their applicability to a narrow range of sample types. To address sample preparation inefficiencies, focused ultrasound extraction (FUSE) has been developed for the rapid release of DNA from robust tissue matrices. FUSE disintegrates target tissue and releases DNA by delivering high pressure ultrasound pulses that generate a cavitation bubble cloud. In prior work, FUSE was performed using large, laboratory-based equipment. Here, a miniaturized FUSE device was designed and tested to enable POC DNA sample preparation. Design constraints were defined based on prior FUSE experiments, and based on these metrics, a 750 kHz cylindrical transducer was fabricated. The pressure output was measured, and high-speed optical imaging demonstrated that the transducer generated sustained cavitation within the defined focal region. The feasibility of DNA extraction from piscine and timber tissues was evaluated, and results show that the device can release DNA with quality suitable for PCR-based detection from both tissue types. These results demonstrate the potential of this device to rapidly extract DNA from complex samples and expand the applicability of molecular detection platforms.

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

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.

Balance is fundamental to mobility and independence, yet it remains difficult to measure in a way that is both clinically meaningful and feasible for widespread adoption. There is a need for more quantitative, objective, and accessible balance assessments. To this end, we created the Stanford Balance Level Evaluation (StaBLE), a battery of 18 balance-challenging tasks and accompanying digital performance metrics computed from video that aimed to capture widely ranging balance abilities across 180 participants. We validated the StaBLE score against existing clinical scales and found that it correlated with age ({rho} = -0.76, p<0.001), the Activities-Specific Balance Confidence Scale ({rho} = 0.60, p<0.001), a Mini-Balance Evaluation Systems Test task ({tau}=0.55, p<0.001), 4-Stage Balance Test ({tau} = 0.55, p<0.001), and Short Physical Performance Battery ({tau} =0.57, p<0.001). StaBLE overcame ceiling effects in these existing scales and had a different underlying distribution for those identified at risk of falling and not (KS: 0.67, p<0.001). To reduce the time it takes to perform the test, we identified subsets of balance tasks that best predicted the StaBLE score across different populations. Together, our dataset, balance assessment protocol, and performance-based score demonstrate the validity of digital measures of balance.

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.

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.

Purpose: To develop and evaluate a recurrent neural network that operates directly on undersampled golden-angle radial k-space, enabling low-latency cardiac cine reconstruction for interventional cardiac MRI (iCMR). Methods: KOSTAL (K-space Optimized Spatial Temporal Architecture for Low latency) combines a per-coil k-space convolutional gated recurrent unit (convGRU), which learns a mask-conditioned view-sharing rule over the accumulated k-space, with an image-space recurrent module. It was trained and tested on the open-access multi-coil OCMR dataset using 10 simulated golden-angle spokes per frame against a 1000-spoke reference. Ablations varied recurrent module architecture and placement; temporal behavior was assessed using a imaging-plane transition test. Generalization was evaluated without retraining in 23 pediatric patients prospectively scanned during free-breathing using a radial golden-angle bSSFP sequence with binned XD-GRASP and clinical breath-held cine serving as references. Results: At 10 spokes per frame, KOSTAL reached a median SSIM of 0.907 (gridding: 0.622) in under 34 ms of processing time per frame. Image quality (IQ) matched gridded results formed from 7-11 times more spokes. Recurrence in k-space rather than image space accounted for most of this gain (0.891 versus [&le;]0.650). IQ recovered from an abrupt plane transition in 75 ms, versus 303 ms for 200-spoke gridding. KOSTAL had the best agreement with the binned reference and the highest long-axis CNR. KOSTAL accurately tracked left-ventricular blood-pool volume (end-diastolic volume bias -0.7 mL) with a shorter lag (71 ms) than iterative reconstruction (239-287 ms). Conclusion: Learned, mask-conditioned recurrence in k-space yields high image quality at a latency compatible with interventional guidance.

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