Biomedical Physics & Engineering Express

This study aims to assess women's perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.

Background: Precise localisation of intracranial electrode contacts is essential for the interpretation of stereoelectroencephalography recordings and planning epilepsy surgery. In current clinical practice, this is typically a manual process, which is time-consuming and prone to variability. Existing automated solutions are often fragmented across multiple tools requiring technical expertise, limiting their adoption in routine clinical workflows. This study presents an open-source extension for 3D Slicer that provides an integrated, user-friendly standalone solution for the direct automatic detection of electrode contacts within a widely used medical imaging platform. Results: The proposed method combines anchor bolt-based initialisation, probabilistic segmentation of electrode structures, and non-linear modelling to precisely track true electrode trajectories. The approach was evaluated on a dataset comprising 78 cases from 73 patients, including 1,078 electrodes with 14,480 contacts. The method achieved high localisation accuracy, with a median (interquartile range) deviation of 0.10 (0.06, 0.15) mm. Only 7/1078 (0.65%) electrodes required manual correction; these specific cases were handled using tools provided within the proposed extension. Conclusions: The presented extension enables fast, accurate, and reproducible electrode contact localisation within a single integrated environment. By combining automation with intuitive user interaction, it significantly reduces processing time while maintaining clinical reliability. The tool's free availability as an extension in 3D Slicer lowers the barrier to adoption and supports the standardisation of workflows across clinical and research centres.

Background: Independent end-to-end verification of adaptive radiotherapy on MR-Linac systems is limited by the lack of patient-specific phantoms able to reproduce imaging and dosimetric properties from CT and MRI scanners. We present a method for automated generation of 4D, patient-specific, multi-material 3D-printable phantoms for quality assurance of adaptive radiotherapy on a 1.5T MR-Linac. Methods: Patient images were automatically segmented using a pretrained deep learning model. The segmented structures were converted into high-resolution 3D meshes and assembled into printable phantoms. A dosimeter holder was inserted at user-defined anatomical locations, with orientation optimized to avoid traversal across heterogeneous tissue interfaces. Physiological motion was incorporated by generating phantoms from images at different timepoints and interpolating deformation fields to create continuous 4D models. Multi-material organs designed by mixing a set of six polymers at various proportions were used to reproduce tissue-specific imaging properties. The properties of material mixtures were evaluated in a clinical CT simulator and a 1.5T MR-Linac. Results: The proposed workflow enables automated generation of anatomically realistic phantoms with several types of embedded dosimeters. A discrete search method was designed for placement and immobilization of OSLD, film, and ion chamber dosimeters. Calibration curves for Hounsfield units were derived through variations in radiopaque material content, while MR signal intensity was modulated by gel and tissue matrix mixtures. Patient-derived abdominal phantoms were fabricated at multiple scales while replicating internal anatomical detail. Multi-dimensional phantom generation enabled continuous representation of motion states with consistent mesh topology across phases. Conclusions: We demonstrate an end-to-end workflow for automated generation of 4D patient-specific phantoms for MR-Linac quality assurance. The method combines realistic anatomy, embedded dosimetry, multimodal imaging properties, and physiological motion within a single fabrication framework. This approachmay enable an improved validation of adaptive radiotherapy workflows in MR-guided treatment devices.

Background: MRI is widely used in radiotherapy planning due to its high soft-tissue contrast, but geometric distortions can compromise target localization accuracy. Objective: This study aimed to develop an accessible method for assessing geometric distortion in MRI using two phantoms - a commercial anthropomorphic phantom and a custom-made phantom fabricated from ABS plastic. Approach: CT imaging was used as the reference standard. Distortion was assessed through linear measurements of periodic structures in a DICOM viewer, followed by statistical analysis. Significance: The study evaluates the clinical impact of distortion on radiotherapy planning and proposes a cost-effective solution for routine quality assurance in resource-limited settings.

Background. Roughly, half of new type 1 diabetes (T1D) diagnoses occur in individuals under 18 years old and represent a more aggressive destruction of beta cell mass (BCM). [11C]-(+)-PHNO positron emission tomography (PET) imaging is used to assess BCM, but current pancreas PET imaging protocols are limited to adults. Previously published full count data from six healthy controls and five T1Ds (6M/5F; 22 to 53 years old) were used for retrospective analysis. Dynamic [11C]-(+)-PHNO PET/CT scans were acquired and reconstructed using full-count list-mode data. For the current comparison to full count data, 50%, 25% and 10% down-sampled count data were re-reconstructed. Pancreas and spleen (reference region) time-activity-curves (TACs) were assessed, and volume of distribution (VT, mL/cm3) was estimated using the reversible 1-tissue compartment model (1TC) with tmax of 30 min for all count levels. Pancreas and Spleen VT estimates (1TC; tmax= 30 min) were used to calculate non-displaceable binding potential (BPND) and were then correlated to semi-quantitative methods of standardized uptake value ratio (SUVR-1) (20-30 min; ref: spleen) to examine simplified methods using simulated low dose protocols. Finally, we performed dosimetry in adult, adolescent and pediatric phantoms to assess radiation dose for simulated low-dose protocols. Results. Qualitatively, increasing noise can be visualized at successive reduced-count levels images, compared to full-count images. Despite progressively increasing noise in reduced-count images, TACs at each reduced-count level remained similar to full-count TACs in both HC and individuals with T1D. Quantitatively, 1TC VT estimates were similar for all reduced count levels and range of tmax values, compared to full-count (all R2[≥]0.99). Pancreas SUVR-1 (20-30 min) and pancreas BPND (tmax = 30; ref: spleen) were highly correlated for all count levels (all R2[≥]0.80). All age groups were under both the yearly occupational and research scan radiation dose limits when examining mean effective dose equivalent with reduced (1/10th) injected dose protocols. Conclusion. Low-count reconstructed data and simplified reference region approaches provide accurate quantification compared to full-count reconstructions. These results provide evidence that it is possible to perform accurate quantification using simulated low dose protocols to quantify BCM for use in individuals with T1D under 18 years old.

Background. Accurate delineation of the gross tumour volume (GTV) - primary tumour (GTVp) and nodal disease (GTVn) - on FDG-PET/CT is a critical step of head and neck radiotherapy planning. Comparisons between lightweight custom networks and the auto-configured nnU-Net v2 are usually reported as end-to-end pipelines, conflating the contribution of the network with that of the inference-time post-processing applied on top of it. We separated the two. Methods. MiniUNet3D (custom 3D U-Net, 18.3 M parameters) and nnU-Net v2 (3d_fullres, 88.2 M parameters) were trained on the same 578 FDG-PET/CT cases (85/15 author-defined split of the HECKTOR 2025 Task 1 set, 8 centres) and evaluated on the same internal cohort. Three arms were compared pairwise: MiniUNet3D raw output at a fixed 0.5 threshold, MiniUNet3D with a locked adaptive post-processing pipeline, and nnU-Net v2. Comparisons used paired Wilcoxon tests with bootstrap confidence intervals, Bonferroni and Benjamini-Hochberg correction, and Cohen's d; catastrophic failure (Dice < 0.01) was compared with an exact McNemar test. Cases with an empty reference for a given target were excluded from that target's analysis (n = 98 GTVp, n = 93 GTVn). Results. With post-processing matched off, nnU-Net v2 was superior: median GTVp Dice 0.799 versus 0.592 (mean difference -0.244, 95 % CI -0.300 to -0.191; d = -0.88) and GTVn 0.774 versus 0.598 (d = -0.82). Post-processing raised MiniUNet3D to 0.800 (GTVp) and 0.738 (GTVn), recovering 79 % of that difference. Post-processed, MiniUNet3D matched nnU-Net v2 on GTVp Dice (p = 0.113) but remained inferior on nodal disease after Bonferroni correction (Dice p = 0.041; surface Dice p = 0.049). Catastrophic GTVp failures were 25/98 raw, 8/98 post-processed and 1/98 for nnU-Net v2 (McNemar p = 0.016). Inference took 34 s versus 78 s per case on the same GPU. Conclusions. Post-processing recovered most, but not all, of the difference between the two models, and it did not confer robustness: an eight-fold higher rate of empty contours on small primaries persisted, which is the more consequential difference for planning safety. Pipeline comparisons reported without a post-processing ablation risk attributing to a network what post-processing supplied.

Purpose: Attenuation correction (AC) of PET images is essential for accurate quantification. Brain PET studies comprising simultaneous EEG (PETEEG) may suffer from metal artifacts in CT images (CTEEG), or improper correction when electrodes are not present in the CT (CT0). As these influences are not well-characterized, we aim to compare metal artifact reduction (MAR) techniques for CTEEG images, and evaluate differences between attenuated-corrected PETEEG using CT0 and CTEEG with MAR, synthetically placed electrodes (CTEEG-synth) and extended Hounsfield unit (HU) range. Methods: 19 healthy participants underwent two total-body PET/CT scans with [18F]FDG, with and without 32 EEG scalp electrodes, respectively. We evaluated five MARs to reduce streaks caused by the EEG electrodes in the CTEEG. Finally, CT0, CTEEG with (CTEEG-iMAR-Ext) and without extended HU range (CTEEG-iMAR) and CTEEG-synth were used to perform attenuation correction of PETEEG. We compared our results to PET0/CT0 scan using relative differences. Results: CTEEG and CTEEG-iMAR showed the smallest differences to CT0. PETEEG/CTEEG-iMAR-Ext exhibited the lowest differences to PET0/CT0 (average bias across all regions of -0.46%), followed by similar performance of PETEEG/CTEEG-iMAR (-0.73%) and PETEEG/CTEEG (-0.76%). Conversely, PETEEG/CT0 demonstrated the largest average differences (-1.81%), with values reaching -2.71% in the parietal lobe. These differences were consistent across subjects, yielding significant effects in most of the brain (pFWE < 0.05). CTEEG-synth performed not as good as CTEEG (-1.21%). Conclusions: CTEEG with extended HU range is most suitable for attenuation correction of PETEEG images, with MAR correction offering little additional improvement.

Accurate determination of surgical margins is critical in tumor resection to ensure complete removal of tumor-infiltrated tissue while preserving healthy tissue. Pathological assessment provides reliable information but is time-consuming. This study investigates the feasibility of using impedance spectroscopy to detect tissue transitions at a macroscopic level. Two electrode arrays--one-dimensional and two-dimensional--were applied to ex vivo porcine brain tissue. Measurements were performed using both two- and four-electrode configurations, and data were corrected using the multiple-load compensation method. Results demonstrate that the one-dimensional array provides continuous conductivity profiles corresponding to tissue transitions, while the two-dimensional array showed less consistent results. These findings suggest that impedance spectroscopy is a promising tool for intraoperative margin detection, but further optimization of electrode geometry and measurement data processing is required.

A test technique for measuring high-frequency transient current components in deep brain tissue is presented. The technique applies a voltage pulse with a high value in dV/dt, generating a corresponding current pulse with high dI/dt that can elicit measurable transient current responses from the electrode/tissue interface and adjacent brain tissue; responses are analyzed in the frequency domain by Fast Fourier Transform at a 200 kHz sampling frequency. The method was motivated by prior evidence that ferritin and neuromelanin in catecholaminergic tissue may support high-frequency conduction properties that have not previously been characterized in vivo. The protocol was applied in 277 measurements across five Sprague Dawley rats at cortical and basal ganglia locations in different locations in the brain. Preliminary spectral results show differences between catecholaminergic regions and cortical tissue that support further development and validation of the method.

Point-of-care ultrasound has transformed bedside diagnostics, yet current systems remain limited by rigid form factors, bulky external electronics and the need for skilled operators. Here we report a conformable ultrasound imaging patch that integrates a 1024-channel CMOS ultrasound application-specific integrated circuit (ASIC) directly beneath a conformable piezocomposite transducer array. The 10 mm X 8 mm, 1024-element ASIC contains on-chip transmit and receive beamforming, reducing the effective off-chip channel count by 16X while preserving image fidelity. Fabricated on a flexible polyimide substrate and bonded using anisotropic conductive film, the patch operates untethered from conventional ultrasound consoles and requires only a laptop for control and data acquisition. The device supports focused, plane-wave and diverging-wave transmission with steering over {+/-}30{degrees} in azimuth and {+/-}15{degrees} in elevation, achieving peak-to-peak acoustic pressures up to 7 MPa at a 4.4-MHz center frequency (mechanical index of 1.7), within diagnostic safety limits. Phantom experiments demonstrate three-dimensional imaging with axial and lateral resolutions (in both XZ and YZ planes) of 0.5 mm and 2 mm, respectively, and accurate contrast reproduction in tissue-mimicking phantoms. Human studies further demonstrate three-dimensional (3D) visualization of the internal jugular vein and carotid artery, as well as rib-shadow-free imaging of pleural motion during respiration. This work establishes a scalable architecture for chronic, wearable ultrasound imaging and highlights the potential of CMOS-integrated, conformable ultrasound systems for continuous physiological monitoring and remote diagnostics.

Cervical cancer represents a pressing global health challenge, emphasizing the critical need for accurate and timely diagnostic methods to facilitate effective treatment and improve survival rates. In response to this challenge, the study presents CerViX-Net, an innovative classification framework designed to advance cervical cancer detection through enhanced computational efficiency and diagnostic accuracy. The development of CerViX-Net is motivated by the limitations of traditional diagnostic models, particularly in handling the computational and memory demands of large-scale data, while ensuring precise feature extraction and classification. CerViX-Net employs a hybrid deep learning architecture that combines the capabilities of ResNet50, EfficientNet-B0, and a Modified Vision Transformer (ViT) module. The ResNet50 branch extracts hierarchical features through stacked convolutional and identity blocks. In another path, the modified ViT module transforms image patches via linear projection, augments them with positional and class embeddings, and processes them using Parallel Transformer Encoder layers to model contextual relationships. Concurrently, EfficientNet-B0 utilizes MBConv blocks to extract multi-scale representations. The feature outputs from all three branches are integrated and passed through a classification head consisting of dropout layers and dense layers to ensure robust and accurate predictions. The proposed framework is rigorously evaluated on the Mendeley LBC dataset, achieving exceptional performance metrics with an accuracy of 99.69%, precision of 99.28%, recall of 99.48%, and an F1-score of 99.52%. The robustness of CerViX-Net is further validated on the SIPaKMeD and Herlev Pap Smear datasets, where it demonstrates comparable excellence, underscoring its efficacy and adaptability across diverse cytology datasets. Statistical validation using Friedman's test further reinforces its superiority over competing methods.

Objective To evaluate the effect of vendor-integrated AI-assisted abdominal ultrasound software on operational efficiency and sonographer workload compared with manual scanning. Methods In this prospective randomised crossover study (January to February 2026), 32 healthy adults each underwent two upper abdominal examinations, one manual and one using vendor-integrated AI software (AI Abdomen Release 3.5; ACUSON Sequoia), in randomised order by two experienced sonographers, each participant scanned once by each sonographer. Scan time, hand-console interaction (keystrokes, hand travel, hover, jerk) from a custom depth-camera hand-tracking system, and operator modifications to AI outputs were recorded. Workload was assessed after each scan with the weighted NASA Task Load Index (NASA-TLX). Analysis used linear mixed-effects models. Results AI-assisted scanning reduced scan time (52.4 s, approximately 9%; 95% CI 23.7 to 81.2; P = 0.001), keystrokes (55, approximately 28%; P < 0.001) and hand travel (4.57 m, approximately 39%; P < 0.001), although the time saving was concentrated in one sonographer. Weighted NASA-TLX did not differ between conditions (-3.9 points; 95% CI - 9.3 to 1.5; P = 0.17), but subscale analyses showed reductions in mental demand (- 6.3; P = 0.03) and effort (- 7.0; P = 0.04), with no compensating increases. Sonographers modified 48 of 184 AI-generated values. Conclusion AI assistance improved operational efficiency and reduced self-reported mental demand and effort, with no compensating increase on other subscales. Gains arose under a controlled, abbreviated protocol in healthy volunteers and varied between operators, and are better read as a reshaping of operator work than its removal.

Breast cancer is the leading cause of cancer-related deaths among women in the Philippines. Over 65% of these cases are diagnosed when they are advanced (Montemayor, 2023). This highlights the need for improved early screening devices. E-HAPLOS, or Electrical Impedance Human-guided Assessment with Pressure for Lump Observation System, is a low-cost glove with sensors designed to improve early detection of suspicious breast lump through touch. It integrates force-sensitive resistors (FSRs) to measure tissue stiffness and Electrical Impedance Spectroscopy (EIS) to analyze conductivity across different frequencies--properties that are closely linked to breast cancer. The prototype uses an ESP32 microcontroller that transmits real-time pressure and impedance data to the website. Tested on gelatin breast models with simulated lump, the FSRs effectively identified lump locations by recording higher mean force values (45.81 kPa vs. 33.57 kPa). This guided approach allowed the combined FSR-EIS system to reach a diagnostic performance with an Area Under the Curve (AUC) above 0.94, a significant improvement over unguided measurement (AUC {approx} 0.78). A two-way ANOVA confirmed a significant difference in diagnostic performance based on the system modality (p < 0.001). Tukeys Honesty Significant Difference (HSD) test showed that the FSR-EIS system was statistically superior to both the unguided EIS (p < 0.001) and FSR-only system (p = 0.041). Results demonstrate the synergistic effect of the integrated system, enabling accurate differentiation of suspicious lumps from normal tissue. The FSR-EIS system of the E-HAPLOS glove shows a great potential for detection of lumps in simulated breasts as a screening tool.

Surface electromyography (sEMG) is the most practical non-invasive interface for myoelectric prostheses, exoskeletons, and rehabilitation systems, but power-line interference (PLI) contamination and excessive digital pipeline group delay still limit its clinical adoption. This paper proposes a co-designed analog-digital correction system combining a high-CMRR front-end with an exponentially-windowed RMS (EMRMS) envelope estimator and a recursive single-tone PLI canceller. We present a closed-form CMRR model capturing the electrode-skin imbalance, and provide a complete stability analysis of the LMS canceller. The EMRMS estimator reduces the computational overhead from[O] (L) to strictly[O] (1) in both time and space complexities. Featuring no data-dependent branching, the algorithm achieves deterministic algorithmic execution time (zero jitter under an RTOS environment) and is natively compatible with fixed-point arithmetic on microcontrollers lacking a hardware Floating-Point Unit (FPU). A reference implementation reaches an 8.2 {micro}s median per-sample latency, yielding an end-to-end delay of[~] 30 ms -- leaving a generous >90 ms budget for electromechanical actuation -- while requiring an active CPU duty cycle of merely 1.6%, enabling prolonged deep-sleep intervals. Validation on the public Ninapro DB2 dataset demonstrates a 13.9 dB mean SNR improvement (averaged across 12 channels; single-channel comparison: 9.7 dB, Table 3) and a 70.0 {micro}V envelope RMSE against a length-200 rectangular reference. Paired Wilcoxon signed-rank tests confirm statistical significance (p < 0.001) over static baselines, and Pearson correlation analysis ({rho} = 0.993 {+/-} 0.0002) confirms strict morphological fidelity. The full open-source codebase and benchmarks are publicly released. O_TBL View this table: org.highwire.dtl.DTLVardef@299dc5org.highwire.dtl.DTLVardef@3519a0org.highwire.dtl.DTLVardef@2586aborg.highwire.dtl.DTLVardef@1ac5610org.highwire.dtl.DTLVardef@1465c46_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 3:C_FLOATNO O_TABLECAPTIONQuantitative comparison on a common 60 s segment of Ninapro-like synthetic sEMG (single channel) with a 3 mV 50.3 Hz mains tone slightly drifted from the static notchs design centre at 50.0 Hz, stress-testing the adaptive corrector under a frequency mismatch. The Ninapro multi-channel aggregate (13.9 dB) reported in Section 3.4 uses mains exactly at 50 Hz (matched notch) and so achieves a higher {Delta} SNR. "MAC/sample" excludes the EMRMS square root and the pre-computed LMS sine/cosine. C_TABLECAPTION C_TBL

Background: Nuclear medicine and radiopharmaceutical development require coordinated radiochemistry, dosimetry, molecular imaging, radiation-safety and clinical decision processes. Current workflows remain fragmented, difficult to audit and poorly standardised for evaluating domain-specific AI support. Methods: We developed RadGuide AI, a nuclear medicine agent built around a traceable data-model-tool loop. Patent, literature and clinical-trial records were converted into 15,596 initial QA items; relevance screening, completeness checks, semantic deduplication and cross-validation retained 5,474 core QA items. MedGemma-27B-Instruct served as the foundation model and was adapted with LoRA. The system incorporated 55 MCP-wrapped tools covering radiopharmaceutical R&D, clinical decision support, imaging analysis and radiation-safety/dosimetry. Evaluation used a locked N=200 benchmark with predefined denominators, leakage control, expert scoring, statistical procedures, factuality audits and tool-execution metrics. Results: RadGuide-LLM achieved 88.5% answer accuracy (177/200; 95% CI, 83.3-92.2%) and a Macro-Average score of 21.5/25 (bootstrap 95% CI, 20.9-22.0), exceeding GPT-4o, DeepSeek-V3.2 and the base MedGemma model in this technical evaluation. Supplementary audits reported guideline compliance, terminology recall, knowledge coverage, tool-routing success and preclinical/phantom dosimetry agreement with explicit denominators and confidence intervals. Interpretation: RadGuide AI converts nuclear medicine queries into auditable retrieval, tool selection, calculation, verification and reporting workflows. The findings support technical feasibility, not definitive patient-level clinical validation; prospective multicentre studies and external benchmark release remain required before clinical deployment.

Existing radiological artificial intelligence (AI) systems are difficult to modify, validate, and adapt to new clinical applications. We present a large language model (LLM)-driven agentic framework capable of reconstructing, optimizing, and customizing deep-learning (DL) systems for radiological image analysis using a single consumer-grade PC. The agent reconstructed the missing pre-training model and corrected a clinical reasoning flaw in an example mammography DL workflow. The improved model performance surpassed all 1,687 submitted models in the Radiological Society of North America Breast Cancer AI Challenge. Across international datasets (n>13,000) from US and China, the model demonstrated robust generalizability (AUC: 0.9). In a reader study (n>1,200), the model outperformed radiologists by an absolute AUC margin of 24% on extended follow-up. Our findings demonstrate that LLM-driven agents enable radiologist-guided customization of radiological AI systems on a consumer-grade PC while reducing the technical expertise required for implementation. This work paves the way for accessible radiological AI.

BackgroundDirect current (DC) stimulation can modulate neuronal activity in ways that differ from pulsatile stimulation, but its intracranial use has been limited by concerns about tissue injury at the electrode/tissue interface. Quantitative safety limits for DC delivered through metal electrodes directly to the brain remain poorly defined. ObjectiveTo estimate histological safety boundaries for DC stimulation delivered through metal electrodes in a large-brain gyrencephalic animal model. MethodsCathodal DC stimulation was applied to the exposed cortical surface of ten anesthetized sheep using platinum-iridium disc electrodes typically used in clinical applications (surface area [&le;] 4.15 mm2). Currents of 5 to 1000 {micro}A were delivered for 10 to 15 minutes at 36 cortical sites. Stimulation dose was quantified as charge density. Brains were removed shortly after stimulation and examined histologically for tissue damage, including necrosis, inflammation, gliosis, and demyelination. Lesion volumes were quantified and related to charge density. ResultsNo lesions were observed at sites where no current or a low charge density (0.7 mC/mm2) was delivered. With stimulation, lesion probability and volume increased with charge density, although variability was substantial. Lesions occurred in 3 of 18 sites at lower charge densities (1.4 to 10 mC/mm2) and in 5 of 9 sites at higher charge densities (14.4 to 144.4 mC/mm2). Linear regression of lesion volume against charge density yielded an estimated zero-lesion intercept of 2.3 mC/mm2, whereas alternative nonlinear models predicted thresholds up to 8.7 mC/mm2. ConclusionThese findings suggest that it may be possible to apply cathodal DC stimulation directly to the cortical surface through metal electrodes without detectable histological damage when current intensity, duration, and electrode size are appropriately constrained. These findings provide quantitative guidance for the safe application of DC directly to neural tissue in experimental and translational neuromodulation studies.

Objective: Low-intensity transcranial magnetic stimulation (LI-TMS) is being investigated as a gel-free alternative to transcranial electrical stimulation (tES), but existing systems remain almost exclusively single-channel and cannot electronically steer the induced electric field. We present the design, modeling, and experimental measurement of a wearable whole-head, multichannel, steerable LI-TMS array. Methods: The system comprises a 102-channel conformal coil array with independently controlled drivers capable of arbitrary waveform synthesis, together with a boundary element fast multipole method (BEM-FMM) framework that computes the coil currents required to produce prescribed cortical field patterns. A 12-channel prototype was characterized by coil-current, electric-field, and thermal measurements. Results: The prototype produced a peak primary electric field of approximately 1 V/m measured in air 4 cm from the inner helmet surface. Whole-array modeling attained cortical fields of up to 1.5 V/m, reproduced the field distribution of a clinically validated low-intensity stimulator to within 3%-5%, and demonstrated focal targeting of the dorsolateral prefrontal cortex, simultaneous delivery of electric field to the default mode network nodes, and synthesis of electric fields following the traveling alpha wave. Conclusion: Electronically steerable, whole-head LI-TMS is feasible using accessible microprocessor-controlled power electronics. Significance: The array reaches the cortical field regime of tES without scalp contact or the associated shunting of current through the scalp, offering a route to testing network-level, phaselocked weak-field neuromodulation.

The advent of photon-counting computed tomography (PCCT) provides new opportunities to quantitatively measure musculoskeletal tissues such as bone, muscle and adipose because of the intrinsic use of spectral imaging. We aimed to evaluate the accuracy of measuring these tissues by PCCT under a range of scan protocols and compared our results to the current standard dual-energy CT (DECT). Phantoms containing inserts ranging from 50 to 200 mg/cm3 of calcium hydroxyapatite (HA) for estimating bone mineral density (BMD), and another phantom containing inserts for muscle and adipose tissues were scanned on PCCT and DECT at 120 and 140 kVp. We created virtual monoenergetic images (VMI) at energy levels from 40 keV to 190 keV for quantitative analyses. The averaged linear attenuation of phantom inserts was compared to theoretical values calculated from standardized attenuation profiles. Material decomposition using VMIs was compared to known HA concentration inserts to determine optimal image pairs for BMD measurement, notably without the need of phantom calibration. For most VMI energy levels the attenuation error was <1% for BMD at both 120 kVp and 140 kVp by PCCT compared to errors of <2% by DECT. The linear attenuation errors were <2.5% for muscle and <3.0% for adipose and results were similar for PCCT and DECT. Generally, errors were highest for low energy VMIs. Material decomposition using VMI pairs with a low energy at 50 or 60 keV and high energy between 150 and 190 keV produced calibration phantom-free estimates of BMD with <1% error. Results were similar for PCCT and DECT at 120 and 140 kVp. PCCT provides an accurate estimate of bone, muscle and adipose attenuation, and using material decomposition, estimations of BMD can be obtained without the need of phantom calibration.

Musculoskeletal ultrasound (MSK-US) enables real-time imaging of muscle structure and function, and wearable ultrasound (WUS) has extended this capability to dynamic movement tasks. Accurate tracking of muscle fascia displacement in M-mode WUS images is essential for quantifying muscle function, yet the relative performance of existing fascia-tracking algorithms remains uncharacterized. This study directly compares five fascia-tracking algorithms: Maximum Pixel Intensity (MPI), Muscle Boundary Tracking Algorithm (MBTA), Principal Component Analysis (PCA), Composite-Factorization PCA (CF-PCA), and U-Net segmentation, against expert-annotated ground truth to identify which approach best supports wearable muscle-monitoring applications. A total of 572 M-mode ultrasound images were collected during isometric quadricep activations (QA) and squats (SQ) using a multi-site WUS system with transducers positioned on the vastus lateralis (VL), rectus femoris (RF), and vastus medialis oblique (VMO). Fascia tracking using U-Net segmentation exhibited the lowest mean absolute error (median QA=0.57, median SQ=1.22; p<0.05), functional range not statistically different from expert traces (QA p=0.33; SQ p=1) and the most accurate estimates of functional error (median QA=-0.21; median SQ=-0.65; p<0.05). PCA-based methods demonstrated the highest correlation with the expert traces (PCA median QA=0.88; CF-PCA median QA=0.88; PCA median SQ=0.78; CF-PCA median SQ=0.75; p<0.005), reflecting superior tracking of relative contraction patterns. These results indicate U-Net segmentation is best suited for applications requiring precise fascia-depth estimation when labeled training data are available, while PCA-based methods are preferable for tracking relative contraction patterns without supervised training, informing algorithm selection for wearable neuromuscular monitoring in clinical and performance settings.