Tali Ilovitsh

Stimulus‐responsive nanomedicines promise controlled therapy, yet most systems rely on passive delivery and lack precise, externally programmable activation while maintaining clinical compatibility. Here we engineer sub‐200 nm, perfluorocarbon (PFC)‐core nanodroplets (ND) that integrate efficient drug loading, physiological stability, and acoustically programmable activation within a nanoscale agent. These NDs are fabricated using microfluidic nanoassembly to achieve controlled size and composition, and are designed to encapsulate fluorinated payloads within the liquid core. Upon exposure to sequential dual‐frequency ultrasound (US), the NDs undergo acoustic droplet vaporization followed by low‐frequency cavitation, enabling spatially confined disruption and on‐demand payload release within clinically relevant acoustic limits. These properties are engineered to overcome physicochemical barriers in solid tumors, including dense extracellular matrix and restricted drug penetration. This approach achieves enhanced payload release and induces mechanochemical cytotoxicity in vitro. In vivo, NDs exhibit prolonged circulation and tumor accumulation, while US activation drives tissue fractionation, controls drug release, and increases subsequent nanoparticle uptake. When applied to a solid tumor model, this combined mechanochemical strategy improves tumor control and extends survival compared to either modality alone. These acoustically activatable NDs provide a versatile system for stimulus‐responsive, site‐targeted drug delivery and tumor disruption, with strong potential for clinical translation.

Blood clot formation and thrombolysis are dynamic biological processes that play central roles in hemostasis, thrombosis, and thrombolytic therapy. Monitoring clot evolution is challenging, as existing approaches often rely on specialized hardware or complex acquisition protocols. This study presents dense speed-of-sound shift imaging (DSI), a noninvasive ultrasound framework for spatiotemporal monitoring of coagulation and lysis from ultrasound image sequences acquired with a single imaging transducer. DSI estimates interframe displacements using dense optical flow and reconstructs slowness-shift maps by solving a regularized inverse problem, from which relative speed-of-sound (SoS) shifts are derived. Using this approach, we quantified spatially localized acoustic signatures of material solidification, clot formation, and enzymatic clot dissolution across systems of increasing biological complexity, including thermally gelling gelatin, fibrin clots, and porcine and human whole blood. DSI detected composition-dependent clot properties, with fibrinogen primarily affecting SoS shift magnitude and thrombin primarily affecting clotting kinetics. In both porcine and human whole blood, DSI tracked the full transition from rapid clot formation to tPA-mediated thrombolysis, revealing markedly slower lysis kinetics and species-dependent differences in clotting amplitude and stabilization time. Together, these results establish DSI as a simple, ultrasound-based platform for quantitative monitoring of coagulation, thrombolysis, and related biological material transitions.

Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg–Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for continuous-wave excitation but may exhibit reduced performance under single-cycle conditions, particularly for multi-focus patterns. In diagnostic imaging, high frame rate is critical for applications demanding rapid scanning. In this context, multi-line transmission (MLT) leverages beam shaping to synthesize multiple simultaneous foci, thereby increasing frame rate. In parallel, structured illumination methods for super-resolution and acoustical holography likewise depend on actively shaping single-cycle pulses to produce controlled patterns, highlighting the need for precise short-pulse beam shaping. To address this challenge, we introduce the spatio-temporal adaptive reconstruction (STAR) algorithm, which performs active beam shaping directly in the time domain by integrating the generalized angular spectrum method (GASM) into an iterative optimization scheme. STAR enforces constraints on both the transducer and focal planes, enabling accurate control of single-cycle excitations. Simulations showed that STAR consistently outperformed GS for multi-focus patterns. For example, in a four-foci configuration, STAR achieved a correlation of 0.80 compared to 0.64 for GS, with significantly improved uniformity across focal peaks. Resolution analysis demonstrated that STAR reduced the minimum distinguishable foci spacing from 1.09 mm with GS to 0.87 mm. Experimental hydrophone measurements confirmed these improvements. Across multi-foci patterns, STAR produced more distinct and balanced foci compared to those observed with GS. These results demonstrate that STAR provides robust and efficient active beam shaping of single-cycle pulses, maintaining accuracy across different depths and frequencies for diagnostic applications.

Objective. To accurately monitor temperature during cryoablation, a minimally invasive technique that destroys tissue locally by forming an ice ball around an inserted cryoprobe. Approach. We present a dense slowness-shift imaging method that estimates local speed-of-sound changes from ultrasound (US) B-mode images using optical flow. This single-transducer, image-based approach enables mapping of spatial temperature change without requiring additional hardware. Cryoablation experiments were conducted in a tissue-mimicking phantom and ex vivo turkey breast. Main results. Slowness deviation increased with decreasing temperature. In the phantom, the dependence was linear ( α _a = − 20.70 η s·m ⁻¹ C° ⁻¹ ), while in turkey breast it followed an exponential relationship ( α _t = 34.04 × exp (0.075(−ΔT)−1) η s·m ⁻¹ C° ⁻¹ ). The algorithm detected sub-degree temperature variations and accurately tracked cooling down to −39.4 ± 5.6 °C. Significance. Accurate temperature monitoring is essential for effective and safe cryoablation. This work demonstrates the feasibility of US-based, noninvasive temperature monitoring during cryoablation, providing a scalable, real-time alternative to existing invasive or high-cost thermal assessment techniques.

Tracking immune cells deep within living tissue remains a fundamental challenge due to the diffraction-limited resolution of ultrasound imaging and the inability to resolve dense cellular populations. Here, we introduce an intracellular super-resolution ultrasound imaging framework based on stochastic phase-changing nanodroplets (NDs) and ultrasound localization microscopy (ULM). We engineer ~170 nm perfluorocarbon NDs that undergo reversible, stochastic liquid-gas transitions under acoustic excitation, generating temporally sparse "blinking" signals. Leveraging the intrinsic endocytic activity of macrophages, these NDs are internalized, enabling intracellular contrast generation independent of vascular flow. We validate this approach across imaging scales, from controlled phantoms and in vitro systems to in vivo tumor models, demonstrating robust intracellular blinking, high cell viability, and consistent super-resolution reconstruction in dense cellular environments. The stochastic blinking of internalized NDs provides the temporal separation required to localize individual sources, overcoming a central limitation of conventional ULM. Following systemic administration, ND-labeled macrophages are tracked in vivo after homing to the liver, where super-resolution ULM resolves cellular distributions with a spatial resolution of 26.3 ± 3.2 μm, corresponding to a 6.1-fold improvement over diffraction-limited imaging. This work establishes a previously unexplored paradigm for ultrasound-based intracellular super-resolution imaging, enabling non-invasive visualization of immune cell organization in deep tissue. By introducing spatiotemporally programmable intracellular contrast, this approach expands ultrasound beyond vascular imaging toward functional cellular imaging, with broad implications for immunology, diagnostics, and cell-based therapies.

The development of porous scaffolds with tunable mechanical and structural properties is essential for advancing tissue engineering strategies. In this study, we present a noninvasive, adjustable method for generating porous collagen scaffolds by utilizing micron-sized phase-shift droplets in combination with dual-frequency ultrasound. These microdroplets, generated via a microfluidic chip and composed of a liquid perfluoropentane core stabilized by a phospholipid shell, were embedded within collagen hydrogels and served as ultrasound-responsive cavitation nuclei. A 3.5 MHz imaging transducer was employed to trigger acoustic droplet vaporization of the embedded microdroplets, transitioning them into microbubbles. Then, a 200 kHz therapeutic transducer induced bubble oscillation and collapse, leading to localized pore formation. This combined ultrasound strategy enabled both vaporization and bubble implosion at reduced pressure thresholds compared to conventional acoustic droplet vaporization methods. Theoretical modeling using the Marmottant model predicted microbubble dynamics and corresponding pore sizes, which were validated through scanning electron microscopy and histological analysis. Ultrasound-treated scaffolds containing droplets exhibited significantly increased porosity of 56.53 ± 3.91% compared to untreated controls, with a pore diameter of 39.42 ± 10.28 μm, observed via scanning electron microscopy. Rheological analysis revealed enhanced elasticity and structural resilience in ultrasound-treated scaffolds. Finally, in vitro studies confirmed that fibroblast viability remained high within the treated scaffolds, with cells observed in close proximity to ultrasound-generated pores. This work introduces a tunable and clinically relevant strategy for fabricating functional scaffolds that could support tissue regeneration and customizable healing environments.

Stimulus-responsive nanomedicines promise spatiotemporally controlled therapy, yet most systems rely on passive delivery and lack precise, externally programmable activation while maintaining clinical compatibility. Here we engineer sub-200 nm, perfluorocarbon (PFC)-core nanodroplets (NDs) that integrate efficient core drug loading, physiological stability, and acoustically programmable activation within a single nanoscale agent. These NDs are fabricated using microfluidic nanoassembly to achieve controlled size and composition, and are designed to encapsulate fluorinated payloads directly within the liquid core. Upon exposure to a sequential dual-frequency ultrasound (US) paradigm, the NDs undergo acoustic droplet vaporization followed by low-frequency cavitation, enabling spatially confined mechanical disruption and on-demand payload release within clinically relevant acoustic limits. These properties are engineered to overcome physicochemical barriers in solid tumors, including dense extracellular matrix and restricted drug penetration. This approach achieves enhanced payload release and induces potent mechanochemical cytotoxicity in vitro. In vivo, NDs exhibit prolonged circulation and tumor accumulation, while US activation drives substantial tissue fractionation, control drug release, and increases subsequent nanoparticle uptake. When applied to a solid tumor model, this combined mechanochemical strategy improves tumor control and significantly extends survival compared to either modality alone. These acoustically activatable NDs provide a versatile system for stimulus-responsive, site-targeted drug delivery and mechanical tumor disruption, with strong potential for clinical translation. Graphical

Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg–Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for continuous-wave excitation but degrade significantly under single-cycle conditions. In diagnostic imaging, high frame rate is critical for applications demanding rapid scanning. In this context, multi-line transmission (MLT) leverages beam shaping to synthesize multiple simultaneous foci, thereby increasing frame rate. In parallel, structured illumination methods for super-resolution and acoustical holography likewise depend on actively shaping single-cycle pulses to produce controlled patterns, highlighting the need for precise short-pulse beam shaping. To address this challenge, we introduce the spatio-temporal adaptive reconstruction (STAR) algorithm, which performs active beam shaping directly in the time domain by integrating the generalized angular spectrum method (GASM) into an iterative optimization scheme. STAR enforces constraints on both the transducer and focal planes, enabling accurate control of single-cycle excitations. Simulations showed that STAR consistently outperformed GS for multi-focus patterns. For example, in a four-foci configuration, STAR achieved a correlation of 0.80 compared to 0.64 for GS, with significantly improved uniformity across focal peaks. Resolution analysis demonstrated that STAR reduced the minimum distinguishable foci spacing from 1.09 mm with GS to 0.87 mm. Experimental hydrophone measurements confirmed these improvements. Across multi-foci patterns, STAR produced more distinct and balanced foci compared to those observed with GS. These results demonstrate that STAR provides robust and efficient active beam shaping of single-cycle pulses, maintaining accuracy across different depths and frequencies for diagnostic applications.

Ultrasound is the most widely used medical imaging modality, yet the images it produces are fundamentally unique, arising from tissue-dependent scattering, reflection, and speed-of-sound variations that produce a constrained set of characteristic textures that differ markedly from natural-image statistics. These acoustically driven patterns make ultrasound challenging for algorithms originally designed for natural images. To bridge this gap, the field has increasingly turned to foundation models, hoping to leverage their generalization capabilities. However, these models often falter in ultrasound applications because they are not designed for ultrasound physics, they are merely trained on ultrasound data. Therefore, it is essential to integrate ultrasound-specific domain knowledge into established learning frameworks. We achieve this by reformulating self-supervised learning as a texture-analysis problem, introducing texture ultrasound semantic analysis (TUSA). Using TUSA, models learn to leverage highly scalable contrastive methods to extract true domain-specific representations directly from simple B-mode images. We train a TUSA model on a combination of open-source, simulated, and in vivo data. The latent space is compared to several larger foundation models, demonstrating that our approach gives TUSA models better generalizability for difficult downstream tasks on unique online datasets as well as a clinical eye dataset collected for this study. Our model achieves higher accuracy in detecting COVID (70%), spinal hematoma (100%) and vitreous hemorrhage (97%) and correlates more closely with quantitative parameters like liver steatosis (r = 0.83), ejection fraction (r = 0.63), and oxygen saturation (r = 0.38). We open-source the model weights and training script: https://github.com/talg2324/tusa