IEEE Transactions on Biomedical Engineering

Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive modality for precision neuromodulation. However, the heterogeneous acoustic properties of the skull often induce phase aberrations that shift the ultrasound focus and compromise energy delivery. In this study, we developed and validated a phase-reversal based aberration correction method to enhance the targeting specificity of tFUS using a 128-element random array ultrasound transducer. Individual head models were constructed from T1-weighted magnetic resonance (MR) images and corresponding pseudo-computed tomography (pCT) data to accurately represent subject-specific skull geometries and the targeted left V5 (V5L) region. Acoustic simulations were conducted with the k-Wave toolbox by first acquiring free-field pressure waveforms and then recording the aberrated waveforms in the presence of the skull. The phase differences between these conditions were used to compute corrective delays for each transducer element. Quantitative evaluation using metrics such as focal overlap with the target region, axial focal positioning, and the delivered ultrasound energy demonstrated significant improvements: the overlap volume increased by 98.70%, mean axial positioning errors were reduced by up to 14.36%, and energy delivery to the target improved by 17.58%. We further demonstrated that the proposed approach outperforms the conventional ray-tracing methods. The results show that phase-reversal based aberration correction markedly increases the spatial targeting accuracy of tFUS and enhances the efficiency of focused ultrasound energy deposition for the customized random array transducer, paving a way for effective and personalized non-invasive neuromodulation therapies.

Non-invasive human-machine interfaces (HMIs) are critical in developing prosthetic systems that offer intuitive, simultaneous, and proportional control over multiple degrees of freedom (DOFs). This study introduces a novel system for intuitive concurrent control of hand and wrist movements using sonomyography based imaging of muscle activity. Our method uses a sparse set of ultrasound scanlines to reduce computational complexity while enhancing usability. We evaluated four regression techniques for wrist and hand angle prediction, focusing on performance with a reduced sonomyographic feature set. We also explored the feasibility of a sonomyography-based system by simulating various factors that could affect prediction, including feature selection and scanline count. Our findings demonstrate that Gaussian process regression excels in predicting wrist and hand angles with just eight equispaced transducers in offline settings. Real-time evaluations with 10 non-disabled participants showed a 93 % success rate for two-DOF tasks using linear regression. The system was tested with an individual with amputation, achieving a 46 % success rate for two-DOF control in a 2D space, even though the ground truth data for model training was collected from the contralateral limb. This study validates our sonomyography-based approach for accurate wrist and hand angle estimation, reducing complexity and demonstrating potential in real-world scenarios.

1ObjectivePassive cavitation imaging and control has explored various beamforming algorithms to balance resolution, imaging artifacts, and computational speed. Optimizing these parameters is essential for clinical translation, as precise cavitation localization and dosage control are critical for Focused Ultrasound (FUS)-based targeted therapies, minimizing unintended tissue damage. Among commonly used methods, Delay-Sum-Integrate (DSI) and Robust Capon Beamforming (RCB) have demonstrated effectiveness but are limited by either significant artifacts or the need for extensive parameter tuning. MethodsThis work introduces Passive Acoustic Dynamic Differentiation and Mapping (PADAM), which adapts the Multiple Signal Classification algorithm to the time domain to improve cavitation localization. ResultsPADAM achieves up to a 6-fold improvement in lateral beam-width compared to RCB, and a 4-fold reduction in mean-square intensity of artifacts. It further unveils a novel physical insight: its input parameter dynamically gauges the richness of an incoming signals frequency content. This feature enables a more physically defined and intuitive parameter for distinguishing between stable and inertial cavitation based on spectral characteristics, simplifying parameter selection and enhancing the framework for cavitation monitoring and control. ConclusionWith its ability to improve resolution, reduce artifacts, and provide computational efficiency, PADAM represents a promising advancement for precise cavitation localization and therapy monitoring. SignificanceThis work introduces PADAM, a novel time-domain passive cavitation imaging method that offers superior resolution and artifact reduction compared to DSI and RCB. Its physically intuitive input parameter enables dynamic differentiation between stable and inertial cavitation, enhancing precision in the monitoring and control of FUS therapy.

Transcranial focused ultrasound is an emerging noninvasive neuromodulation technique that offers high spatial precision and the potential for deep brain penetration. However, due to skull-induced attenuation and acoustic aberrations, precisely stimulating deep brain regions in mice remains challenging. To address this challenge, this study introduces a machine-learning-based computational framework to optimize single-element transducer designs for accurate deep-brain targeting in a mouse model. This framework includes a surrogate model consisting of a Random Forest regressor and classifier, trained on acoustic simulation results to predict performance from design parameters. A total of 72 transducer designs were simulated across coronal and sagittal planes, systematically varying frequency (1-6 MHz), radius of curvature (5-7 mm), and f-number (0.58-1.0). Each design was evaluated using five performance metrics: focal length, focal shape, maximum pressure at the focal region, pressure maximum location, and sidelobe suppression. The surrogate models were then combined with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization and identify high-performing transducer designs. The optimized design produced a compact, symmetric focal region and accurate energy delivery to deep targets, with minimal off-target exposure, even in complex skull anatomy. Results show that lower f-numbers, moderate radius of curvature, and higher frequencies facilitate precise deep brain targeting. Overall, this data-driven approach enables practical design of single-element transducers for deep-brain neuromodulation in mice and provides a framework for designing transcranial transducers for other brain targets, potentially accelerating the clinical translation of focused ultrasound technologies.

Objective: Decomposition of high-density electromyographic signals enables non-invasive analysis of individual motor unit (MU) behavior, but reliable interpretation of physiological changes in health and disease depends on accurate MU discharge detection. This accuracy is compromised by mixed source estimates, where high amplitude peaks are associated with discharges from more than one MU. We introduce a post-decomposition framework to identify and separate suspected mixed sources using PCA-guided source refinement. Method: For each suspected mixed source, extended and whitened EMG vectors were extracted at source peaks and projected into a low-dimensional PCA subspace. This subspace highlighted MU-specific differences across candidate discharges, including subtle or spatially localized features of the spatiotemporal MUAP profile. Clusters in the PCA subspace were used to initialize source estimates for the constituent MUs. During iterative source refinement, source peak amplitudes were reweighted according to the distance of their corresponding points from the associated cluster center. Particle swarm optimization selected the reweighting factor that minimized the coefficient of variation of inter-spike intervals (CoVISI). Results: The algorithm separated mixed MU sources in simulated and experimental HDsEMG data. In simulated data, resolving mixed sources increased median rate of agreement (RoA) by >40%. In experimental recordings, MU yield increased by 1.27 per trial and CoVISI decreased by 0.28 (33% RoA improvement). Conclusions: PCA-based representation enhanced separability between MUs with similar MUAP profiles, while distance-based amplitude reweighting reduced re-merging during source refinement. Significance: This framework resolves merged MU discharge trains, improving decomposition accuracy and recovering MUs that might otherwise be excluded by quality thresholds.

Gastrointestinal magnetic resonance imaging (MRI) provides rich spatiotemporal data about the volume and movement of the food inside the stomach, but does not directly report on the muscular activity of the stomach itself. Here we describe a novel approach to characterize the motility of the stomach wall that drives the volumetric changes of the gastric content. In this approach, a surface template was used as a deformable model of the stomach wall. A neural ordinary differential equation (ODE) was optimized to model a diffeomorphic flow that ascribed the deformation of the stomach wall to a continuous biomedical process. Driven by this diffeomorphic flow, the surface template of the stomach progressively changes its shape over time or between conditions, while preserving its topology and manifoldness. We tested this approach with MRI data collected from 10 Sprague Dawley rats under a lightly anesthetized condition. Our proposed approach allowed us to characterize gastric anatomy and motility with a surface coordinate system common to every individual. Anatomical and motility features could be characterized for each individual, and then compared and summarized across individuals for group-level analysis. As a result, high-resolution functional maps were generated to reveal the spatial, temporal, and spectral characteristics of muscle activity as well as the coordination of motor events across different gastric regions. The relationship between muscle thickness and gastric motility was also evaluated throughout the entire stomach wall. Such a structure-function relationship was used to delineate two distinctive functional regions of the stomach. These results demonstrate the efficacy of using GI-MRI to measure and model gastric anatomy and function. This approach described herein is expected to enable non-invasive and accurate mapping of gastric motility throughout the entire stomach for both preclinical and clinical studies.

Recent years have demonstrated the feasibility of using intracortical Brain-Machine Interfaces (iBMIs), by decoding thoughts, for communication and cursor control tasks. iBMIs are increasingly becoming wireless due to the risk of infection and mechanical failure, typically associated with percutaneous connections. The wireless communication itself, however, increases the power consumption further; with the total dissipation being strictly limited due to safety heating limits of cortical tissue. Since wireless power is typically proportional to the communication bandwidth, the output Bit Rate (BR) must be minimised. Whilst most iBMIs utilise Multi-Unit activity (MUA), i.e. spike events, and this in itself significantly reduces the output BR (compared to raw data), it still limits the scalability (number of channels) that can be achieved. As such, additional compression for MUA signals are essential for fully-implantable, high-information-bandwidth systems. To meet this need, this work proposes various hardware-efficient, ultra-low power MUA compression schemes. We investigate them in terms of their BRs and hardware requirements as a function of various on-implant conditions such as MUA Binning Period (BP) and number of channels. It was found that for BPs [≤] 10 ms, the delta-asynchronous method had the lowest total power and reduced the BR by almost an order of magnitude relative to classical methods (e.g. to approx. 151 bps/channel for a BP of 1 ms and 1000 channels on-implant.). However, at larger BPs the synchronous method performed best (e.g. approx. 29 bps/channel for a BP of 50 ms, independent of channel count). As such, this work can guide the choice of MUA data compression scheme for BMI applications, where the BR can be significantly reduced in hardware efficient ways. This enables the next generation of wireless iBMIs, with small implant sizes, high channel counts, low-power, and small hardware footprint. All code and results have been made publicly available.

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

This work aims to evaluate a grip strength model, developed using a piecewise linear function based on the Woods and Bigland-Ritchie EMG-force model, which correlates the relationship between measured grip force and muscular activity. The grip strength model is compared against the results derived from an upper limb musculoskeletal model. Experimental results demonstrate the models efficacy in estimating surface electromyography (sEMG) readings from force measurements, with a mean root mean square error (RMSE) of 0.2035 and a standard deviation of 0.1207 for muscle activation (dimension-less). Moreover, incorporating sEMG readings associated with grip force does not significantly affect the optimization of muscle activation in the upper arm, as evidenced by kinematic data analysis from dynamic tasks. This validation underscores the models potential to enhance musculoskeletal model-based motion analysis pipelines without distorting results. Consequently, this research emphasizes the prospect of integrating external models into existing human motion analysis frameworks, presenting promising implications for physical Human-Robot Interactions (pHRI).

Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73% image classification accuracy. While these preliminary results benchmark Efficient-NetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.

Freezing of gait (FoG) in Parkinson's disease is a brief but hazardous gait failure that often precedes falls. For wearable cueing or other closed-loop assistance, a detector that reacts only after FoG onset is usually too late; the more useful task is to recognize the pre-freezing transition from physiological signals. This study presents PreFoGNet, a dual time-frequency deep learning framework for early FoG prediction using plantar pressure signals. The temporal stream combines a multi-scale Inception encoder with a bidirectional Mamba module to capture both short contact-related transients and several-second gait deterioration without the quadratic cost of attention. In parallel, the frequency stream uses band-wise spectral modeling and attention-based gating to emphasize physiologically meaningful changes in the locomotion, freeze-related, and high-frequency bands. On the WearGait-PD dataset, with a 2 s prediction horizon and subject-wise evaluation, PreFoGNet achieved a sensitivity of 93.94%, a specificity of 89.76%, a G-Mean of 0.9183, and an AUC-ROC of 0.9607. It outperformed classical machine-learning and deep learning baselines, and retained usable performance under moderate noise and single-channel loss. Additional horizon analysis showed that plantar pressure contains a stable pre-freezing signature within 0-3 s before onset, with a practical prediction boundary of approximately 6-7 s. These findings suggest that time-frequency modeling of plantar pressure is a promising signal-processing route for wearable FoG early-warning systems.

A number of recent and current efforts in brain machine interfaces are developing millimetre-sized wireless implants that achieve scalability in the number of recording channels by deploying a distributed swarm of devices. This trend poses two key challenges for the wireless power transfer: (1) the system as a whole needs to provide sufficient power to all devices regardless of their position and orientation; (2) each device needs to maintain a stable supply voltage autonomously. This work proposes two novel strategies towards addressing these challenges: a scalable resonator array to enhance inductive networks; and a self-regulated power management circuit for use in each independent mm-scale wireless device. The proposed passive 2-tier resonant array is shown to achieve an 11.9% average power transfer efficiency, with ultra-low variability of 1.77% across the network. The self-regulated power management unit then monitors and autonomously adjusts the supply voltage of each device to lie in the range between 1.7 V-1.9 V, providing both low-voltage and over-voltage protection.

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

Continuous long-term sensing of biopotential signals is vital to facilitate accurate diagnosis. The current state of the art in wearable health monitoring relies on radiative technology for communication. Due to their radiative nature, these systems result in lossy and inefficient transmission, limiting the devices life span. Human Body Communication has emerged as an energy-efficient secure communication modality, and literature has shown body communication to transmit biopotential signals at 100x lower power than traditional radiative technologies. Unlike radiative communication that uses airwaves, HBC, specifically Capacitive Electro-Quasistatic HBC (EQS-HBC), couple signals and confine them within the human body. In Capacitive EQS-HBC, the transmitter uses an electrode to modulate the body potential to transmit data. The modulation of body potential by HBC raises the following concerns. Will HBC transmissions affect the quality of biopotential signals sensed from the body? Additionally, since biopotential sensing systems commonly use Right Leg Drive (RLD) to bias body potential, there is also a concern if RLD can affect the quality of HBC transmissions. For the first time, our work studies the interactions between EQS-HBC and biopotential sensing. Our work is important since understanding HBC-RLD interactions is integral to developing EQS-HBC-based biosensors for Body Area Networks (BANs). For the studies, we conducted lab experiments and developed circuit theoretic models to back the experimental outcomes. We show that due to their higher frequency content and common-mode nature, HBC transmissions do not affect the differential sensing of low-frequency biopotential signals. We show that biopotential sensing using RLD affects HBC. RLD deteriorates the signal strength of HBC transmissions. We thus propose not to use RLD with HBC. We demonstrate our proposed solution by transmitting ECG signals using HBC with 96% correlation compared to the traditional wireless system at a fraction of the power.

The Jugular Venous Pulse (JVP) is a vital gauge of proper heart health, reflecting the venous pressure via the Jugular Vein observation. It offers crucial insights for discerning numerous cardiac and pulmonary conditions. Yet, its evaluation is often over-shadowed by the challenges in its process, especially in patients with neck obesity obstructing visibility. Although central venous catheterization provides an alternative, it is invasive and typically reserved for critical cases. Traditional JVP monitoring methods, both visual and via catheterization, present significant hurdles, limiting their frequent application despite their clinical significance. Therefore, there is a pressing need for a non-invasive, efficient JVP monitoring method accessible for home-based and hospitalized patients. Such a method could preempt numerous hospital admissions by offering early indicators. We introduce a non-invasive method using a frequency-modulated continuous wave (FMCW) radar for JVP estimation directly from the skin surface. Our signal processing technique involves an eigen beamforming method to enhance the signal-to-noise ratio for better estimation of JVP. By meticulously fine-tuning various parameters, we identified the optimal settings to enhance the JVP signal quality. In addition, we performed a detailed morphological analysis comparing the JVP and photoplethysmography signals. Our investigation effectively achieved signal localization within a Direction of Arrival (DoA) range from -20{degrees} to 20{degrees}. This initial study validates the effectiveness of using a 60 GHz far-field radar in measuring JVP. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/24308313v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@d95a7dorg.highwire.dtl.DTLVardef@1c3e6d4org.highwire.dtl.DTLVardef@678f28org.highwire.dtl.DTLVardef@e7a0d7_HPS_FORMAT_FIGEXP M_FIG C_FIG

Speckle is the major artifact in ultrasound imaging, and it is well-known that speckle can be reduced by compounding (averaging) images taken either at different frequencies or from different angles. By averaging images of a phantom taken over a frequency range [Formula] and a 90{degrees} span of angles, the combined speckle reduction is demonstrated to be ~ 9x compared to non-compounded images, while the reduction with frequency or angle averaging resulted in reductions of ~ 3x individually. The rf input to the transducer is altered to vary the sound frequency and the phantom is rotated with respect to the transducer to obtain different imaging angles. Numerical simulations of sound scattered by randomly distributed point scatterers showed quantitative agreement with the experiment. Using a commercial system, a 6x reduction in speckle is demonstrated imaging a human wrist. A robot arm is used to move the transducer along a circular path to acquire images at 9 angles separated by 10{degrees}. The commercial system does not allow direct control of the input to the transducer, so the broadband signal detected is Fourier filtered to obtain images at different frequencies with ~ 2x reduced frequency range. Images taken at different angles contain distortions from speed of sound variations and pressure induced by the probe. Two forms of non-rigid image registration are applied to correct for the distortions and create a higher resolution composite image. A design for achieving ~10x speckle reduction with essentially no loss in imaging speed is described.

Millimeter-wave radar can quietly monitor health and behavior at home, which is vital for supporting people living with dementia. Most studies, however, remain limited to short-term testing in controlled spaces. Real-world deployment requires robust activity classification as a prerequisite: vital-sign and behavioral sensing require fundamentally different processing pipelines, and absent periods need to be reliably distinguished from stationary states. Bridging the critical gap between controlled laboratory demonstrations and continuous home monitoring, this paper introduces a self-adapting radar framework that extracts meaningful behavioral segments from massive, unconstrained real-world data. The system performs continuous real-time activity classification (stationary, walking, and absent) and target localization, selectively directing downstream processing to the most informative segments. It addresses key real-world deployment challenges including adaptive thresholding across subjects and environments, and walking detection under naturalistic activity conditions. Prior to integration with the Minder platform, the system was validated in a fully instrumented studio apartment against ground truth. Across 12 subjects, the system achieved an overall classification accuracy of 0.98, with F1 scores of 0.99 for absence and stationary states, and 0.95 for walking. Event-based evaluation yielded a per-subject walking sensitivity of 0.916{+/-} 0.058 and F1 score of 0.935 {+/-}0.030. Localization root mean square error during movement was 0.40 m. The results demonstrate reliable performance suitable for transitioning to long-term real-world home deployment.

ObjectiveIndividuals with autism may perform repetitive or stereotyped rhythmic movements known as "stereotypies" or "stims." Per first-person reports, stereotypies provide benefits including sensory and emotional regulation. These movements are not often convenient to engage in and for some may be self-injurious. We propose the design of a device that discreetly provides rhythmic stimulation through vibrating motors that are incorporated into a vest designed to be worn under clothing. MethodsWe designed a vest that provides sensory stimulation through vibrations from 18 coin motors arranged on the interior surface of a compression vest. The vibration frequency and duty cycle are controlled by the user via a Bluetooth smartphone application, allowing them to optimize the stimulation for their unique needs. The vest is designed to be worn discreetly under the users clothing. It is rechargeable and can be used at full power for up to 4 hours. We performed experiments to quantify target values for the vibration pressure, signal frequency, signal duty cycle, battery life, noise level, and temperature. ResultsOur device accurately controls frequency and duty cycle with marginal error and meets all our engineering requirements except for battery life. It also conforms to ethical and regulatory guidelines. ConclusionFuture work, such as variable device weight, pressure and temperature control, and vibration patterns synchronized to music, could refine the product and deliver more value to the user. Significance: We have designed a vest using vibration to provide rhythmic sensory input to supplement the benefits of stereotyped movements.

Magnetic resonance elastography (MRE)-based muscle force estimation methods have been proposed to estimate individual muscle forces based on measurements of shear wave speed and joint torque in multiple postures. To estimate both the slope and offset parameters of the relationship between shear wave speed and muscle force, it is necessary to collect measurements in a plethora of postures in case of substantial muscle redundancy. However, anisotropic MRE requires a structural and diffusion-tensor imaging scan in each posture, which is infeasible given the time constraints of MRI imaging. The objectives of this work were to develop a muscle force estimator with sufficient accuracy that would only require a limited set of postures, and to evaluate its effectiveness under a variety of measurement conditions. We developed a novel MRE-based muscle force estimator, which decouples shear wave speed into its active and passive components and solves for the slope and offset parameters, independently. We assessed the effectiveness of the proposed estimator under different simulated measurement conditions, with varying levels of noise, and compared it to the original MRE-based estimator. The proposed estimator results in a reduction in the estimation error for the offset parameter, for all muscles, without significant degradation in the estimation error for the slope parameter. However, the proposed muscle force estimator does not improve the goodness-of-fit or the cross-validation error compared to the original estimator. In conclusion, the proposed MRE-based muscle force estimator improves the estimation of muscle-specific parameters and may yield increased muscle force estimation performance.

Abdominal ultrasound is a crucial first-line diagnostic tool, yet its efficacy is inherently constrained by a strong dependency on operator skill, leading to significant inter-operator variability and limiting its widespread adoption. While robotic ultrasound systems with artificial intelligence have emerged to mitigate this, current approaches face critical limitations: they often rely on simulated or phantom data for training, which hampers generalizability, and employ control strategies that lack the flexibility to adapt to complex human anatomy. To address these challenges, this paper introduces a novel, end-to-end robotic system for autonomous abdominal scanning. Our methodology is systematic: it begins with coarse scan planning via RGB and 3D point cloud-based localization of key anatomical landmarks (e.g., xiphoid process). The core of our approach involves training a reinforcement learning scanning policy directly on live human volunteers, enabling the development of a strategy that is robust to anatomical diversity. This high-level policy is executed by a sophisticated hybrid force-position controller, enhanced with real-time normal vector calibration and 3D point cloud-based respiratory phase detection to dynamically adjust contact force, particularly during inspiration. This innovation proves critical for improving scan quality in subjects with higher Body Mass Index (BMI). Extensive validation against expert sonographers demonstrates that our system achieves high precision in standard plane recognition and superior scanning efficiency. Furthermore, to holistically assess performance where standard planes are difficult to acquire, we introduce a 3D reconstruction-based coverage metric. Results confirm that our system delivers strong adaptability and significantly improved consistency, marking a substantial step toward clinically viable, operator-independent ultrasound robotics.