Jeffrey Brian Fowlkes

Many experimental and clinical measurements require simultaneous acquisition from independent instruments that do not share a clock. We describe a device that enables such pairings via event-based synchronization: an electrical trigger from one instrument drives an Arduino-controlled stepper actuator that produces a brief mechanical event at a location sensed by the other instrument, leaving a fiducial marker for post-hoc temporal alignment. The motivating application is synchronization of high-resolution manometry (HRM) with transperineal ultrasound for phenotyping stress urinary incontinence. Bench characterization yielded a device latency of 802.5 μ m ± 46.2 μ m (mean ± SD, n = 30 ), of which the electromechanical response of the actuator accounts for ≈ 84 % of the mean latency and the dominant share of the trial-to-trial variation (i.e. the SD of 46.2 μ m ). For the ultrasound–HRM application, the synchronization uncertainty is ≈ 6 ms ( n = 30 markers), set by the HRM rather than the device, whose intrinsic jitter contributes negligibly. The approach generalizes to instrument pairings in which one instrument provides a stable electrical trigger with a known relation to its recording timeline and the other is a mechanically-sensitive receiver of adequate bandwidth, provided the marker does not contaminate the signal of interest.

Objectives: Stress urinary incontinence (SUI) has been linked to excessive urethral mobility, yet clinical evaluation has been largely limited to assessing maximal excursion rather than capturing the full dynamics of visible urethral movement. In this study, we hypothesize that an automated, ultrasound‐based method can objectively differentiate urethral mobility patterns between women with SUI and continent controls. Methods: We used a previously validated optical flow‐based algorithm to automatically track urethral motion from transperineal ultrasound images during cough, Valsalva maneuver, and pelvic muscle contraction (PMC) in 11 women with SUI and 10 continent controls. Urethral motion was assessed by defining three regions of interest along the urethra (proximal, mid, and distal). Segmental urethral kinematics were computed and statistically compared between groups. Results: Substantial variability and overlap between groups were observed, with coefficient of variation ranging 25%–90%. On average, women with SUI demonstrated significantly larger urethral displacement compared to controls, particularly at the proximal segment during Valsalva (10.6 ± 1.2 mm vs. 6.0 ± 0.6 mm, p < 0.01), with pronounced inferior‐posterior motion. Additionally, displacement between the upper and lower urethra was significantly larger in the SUI group (0.47 ± 0.10 mm / mm vs. 0.13 ± 0.03 mm / mm, p < 0.05), indicating localized hypermobility particularly near the proximal urethra. Maneuver‐specific differences were also noted within the SUI group, with Valsalva producing significantly larger and less uniform urethral movements compared to cough (10.6 ± 1.2 mm vs. 6.6 ± 0.5 mm, p < 0.05). Conclusion: Our results demonstrate that the automated method is capable of capturing urethral mobility characteristics associated with SUI. Significant inter‐individual variability in both continent and SUI groups indicates that urethral kinematics are heterogeneous. The detailed kinematic data have the potential to identify distinct sub‐types of urethral mobility, facilitating systematic comparisons with underlying structural and neuromuscular defects. This approach can move clinical evaluation from simple group comparisons toward personalized SUI diagnosis and targeted treatment selection. Future studies with larger sample sizes and inclusion of additional pelvic floor conditions will be needed to validate these findings and advance their translation into clinical practice.

Conventional color flow processing is primarily optimized for qualitative visualization of flow dynamics, limiting its diagnostic use in regions where vascular structures are small relative to the ultrasound beamwidth. Leveraging the statistical properties of color flow data may provide a pathway toward quantitative discrimination between blood and tissue signals. This could enhance detection of vascular abnormalities, improve diagnostic accuracy, and support monitoring in diseases with small hemodynamic changes. Experimental data was obtained using a clinical GE LOGIQ 9 ultrasound system with a 10L linear array probe (3.75 MHz) positioned on an in-house made half-space flow phantom with the focus located at 3 cm depth. The simulation data obtained from Field II used a setup analogous to the experimental settings. Theoretical probability density function of ultrasound color flow power was derived using a gamma distribution. Shape parameters for blood and tissue were estimated using maximum likelihood estimation (MLE) in both simulation and experimental data. Color flow power was found to follow the gamma distribution in both simulation and experimental data. The estimated shape parameters aligned with theoretical predictions and distinguished between blood and tissue. Estimated shape parameters are less than or equal to 1 for tissue samples and greater than 1 for blood samples. This study presents a statistical modeling approach to enhance blood-tissue differentiation in color flow ultrasound, enabling blood characterization and perfusion quantification for improved detection and monitoring of vascular abnormalities.

Preclinical and clinical histotripsy studies demonstrating feasibility, safety, efficacy, and antitumor immunomodulatory effects support the clinical translation of histotripsy, making it a unique local-regional treatment option for primary and metastatic liver cancer.

Objective: Focused ultrasound has emerged as a precise and minimally invasive modality for effective cancer treatment. In this study, we propose a novel method that integrates the mechanical effects of focused ultrasound, known as histotripsy, with heating to enhance both the immediate and sustained cytotoxic effects on cancer cells.Methods: Our investigation focused on VX2 cancer cells in suspension, examining five experimental groups: blank control, negative control, heating alone, histotripsy alone, and histotripsy with subsequent heating. B‐mode ultrasound imaging was utilized to visualize cavitation bubble cloud formation and its motion during histotripsy. The suspension was contained in individually sealed compartments obtained from bubble wrap (referred to as bubble wrap compartments) embedded within the agarose phantom. Residual living cells were examined immediately after treatment and cultured for 96 hours to analyze the growth patterns. Additionally, CFDA SE staining was employed to assess cell proliferation. Furthermore, both intracellular and extracellular heat shock protein 70 (HSP70) levels were measured to investigate the potential initiation of an immune response.Results: The combination of histotripsy and subsequent heating significantly reduced the normalized concentration of living cells immediately after treatment. It also decreased the proliferation rate of residual cells compared with the other experimental groups. Histotripsy with subsequent heating also increased the generation and release of HSP70, which might potentially enhance an innate anti‐tumor immune response in vivo.Conclusion: Histotripsy and subsequent heating improved the immediate lethal impact on VX2 cancer cells and curtailed the proliferation of residual cancer cells in suspension. This study presents a promising strategy for cancer therapy in the future.

Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding microenvironment. This study investigated the acoustic and imaging characteristics of phase-shift nanodroplets in fibrin-based, tissue-mimicking hydrogels using passive cavitation detection and active imaging techniques, including B-mode and contrast-enhanced ultrasound. The findings demonstrated that the backscattered signal intensities and pronounced nonlinear acoustic responses, including subharmonic and higher harmonic frequencies, of ADV-generated bubbles correlated inversely with fibrin density. Additionally, we quantified the mean echo intensity, bubble cloud area, and second-order texture features of the generated ADV bubbles across varying fibrin densities. ADV bubbles in softer hydrogels displayed significantly higher mean echo intensities, larger bubble cloud areas, and more heterogeneous textures. In contrast, texture uniformity, characterized by variance, homogeneity, and energy, correlated directly with fibrin density. Furthermore, we incorporated transfer learning with convolutional neural networks, adapting AlexNet into two specialized models for differentiating fibrin hydrogels. The integration of deep learning techniques with ADV offers great potential, paving the way for future advancements in biomedical diagnostics.