Minimally invasive medical procedures, such as endovascular catheterization, have considerably reduced procedure time and associated complications. However, many regions inside the body, such as in the brain vasculature, still remain inaccessible due to the lack of appropriate guidance technologies. Here, experimentally and through numerical simulations, we show that tethered ultra-flexible endovascular microscopic probes can be transported through tortuous vascular networks with minimal external intervention by harnessing hydrokinetic energy. Dynamic steering at bifurcations is performed by deformation of the probe head using magnetic actuation. We developed an endovascular microrobotic toolkit with a cross-sectional area that is orders of magnitude smaller than the smallest catheter currently available. Our technology has the potential to improve state-of-the-art practices as it enhances the reachability, reduces the risk of iatrogenic damage, significantly increases the speed of robot-assisted interventions, and enables the deployment of multiple leads simultaneously through a standard needle injection and saline perfusion.

A design, manufacturing, and control methodology is presented for the transduction of ultrasound into frequency-selective actuation of multibody hydrogel mechanical systems. The modular design of compliant mechanisms is compatible with direct laser writing and the multiple degrees of freedom actuation scheme does not require incorporation of any specific material such as air bubbles. These features pave the way for the development of active scaffolds and soft robotic microsystems from biomaterials with tailored performance and functionality. Finite element analysis and computational fluid dynamics are used to quantitatively predict the performance of acoustically powered hydrogels immersed in fluid and guide the design process. The outcome is the remotely controlled operation of a repertoire of untethered biomanipulation tools including monolithic compound micromachinery with multiple pumps connected to various functional devices. The potential of the presented technology for minimally invasive diagnosis and targeted therapy is demonstrated by a soft microrobot that can on-demand collect, encapsulate, and process microscopic samples. Microfabricated devices have led to revolutionary changes in our ability to manipulate small volumes of fluid and microscopic samples contained therein. [1] As a result, majority of state-of-the-art in vitro biomedical platforms contain microfluidic components. Operating these devices requires the use of bulky pumps, compressors, or tethered electrical powering units, which significantly increase the overall size and limit the portability. A key technological challenge has been the development of untethered microfluidic systems that are capable of providing such functionality with wireless control for in vivo applications. Ideally, such systems are expected to determine the timing, duration, and dosage of the intervention and allow remote, noninvasive, repeatable, and reliable control of diagnostic or

We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier–Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.

We present a pipeline to synthesize patient-specific pulsatile turbulent 4D flow MRI datasets of the aorta. Aortic motion and inflow are extracted from in-vivo 2D cine and time-resolved 2D phase-contrast data. Computational fluid dynamics is used to obtain 4D velocity and turbulence fields to simulate MR signals using multipoint 4D flow tensor MRI protocols, which are reconstructed into velocity and turbulence maps with a Bayesian approach. As a result, realistic paired data of ground truth and their projection into MR images enable assessing accuracy and precision of encoding and inference, training of inference machines and, ultimately, deriving optimal experimental designs.