Patient-specific tissue engineered vascular graft for aortic arch reconstruction.

Objectives: The complexity of aortic arch reconstruction due to diverse 3-dimensional geometrical abnormalities is a major challenge. This study introduces 3-dimensional printed tissue-engineered vascular grafts, which can fit patient-specific dimensions, optimize hemodynamics, exhibit antithrombotic and anti-infective properties, and accommodate growth. Methods: We procured cardiac magnetic resonance imaging with 4-dimensional flow for native porcine anatomy (n = 10), from which we designed tissue-engineered vascular grafts for the distal aortic arch, 4 weeks before surgery. An optimal shape of the curved vascular graft was designed using computer-aided design informed by computational fluid dynamics analysis. Grafts were manufactured and implanted into the distal aortic arch of porcine models, and postoperative cardiac magnetic resonance imaging data were collected. Pre- and postimplant hemodynamic data and histology were analyzed. Results: Postoperative magnetic resonance imaging of all pigs with 1:1 ratio of polycaprolactone and poly-L-lactide-co-ε-caprolactone demonstrated no specific dilatation or stenosis of the graft, revealing a positive growth trend in the graft area from the day after surgery to 3 months later, with maintaining a similar shape. The peak wall shear stress of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft portion did not change significantly between the day after surgery and 3 months later. Immunohistochemistry showed endothelization and smooth muscle layer formation without calcification of the polycaprolactone/poly-L-lactide-co-ε-caprolactone graft. Conclusions: Our patient-specific polycaprolactone/poly-L-lactide-co-ε-caprolactone tissue-engineered vascular grafts demonstrated optimal anatomical fit maintaining ideal hemodynamics and neotissue formation in a porcine model. This study provides a proof of concept of patient-specific tissue-engineered vascular grafts for aortic arch reconstruction.

FABRICATION OF MULTILUMEN MICROFLUIDIC TUBING FOR EX SITU DIRECT LASER WRITING.

Among the numerous additive manufacturing or "three-dimensional (3D) printing" techniques, two-photon Direct Laser Writing (DLW) is distinctively suited for applications that demand high geometric versatility with micron-to-submicron-scale feature resolutions. Recently, "ex situ DLW (esDLW)" has emerged as a powerful approach for printing 3D microfluidic structures directly atop meso/macroscale fluidic tubing that can be manipulated by hand; however, difficulties in creating custom esDLW-compatible multilumen tubing at such scales has hindered progress. To address this impediment, here we introduce a novel methodology for fabricating submillimeter multilumen tubing for esDLW 3D printing. Preliminary fabrication results demonstrate the utility of the presented strategy for resolving 743 µm-in-diameter tubing with three lumens-each with an inner diameter (ID) of 80 µm. Experimental results not only revealed independent flow of discrete fluorescently labelled fluids through each of the three lumens, but also effective esDLW-printing of a demonstrative 3D "MEMS" microstructure atop the tubing. These results suggest that the presented approach could offer a promising pathway to enable geometrically sophisticated microfluidic systems to be 3D printed with input and/or output ports fully sealed to multiple, distinct lumens of fluidic tubing for emerging applications in fields ranging from drug delivery and medical diagnostics to soft surgical robotics.

A 3D-MICROPRINTED COAXIAL NOZZLE FOR FABRICATING LONG, FLEXIBLE MICROFLUIDIC TUBING.

A variety of emerging applications, particularly those in medical and soft robotics fields, are predicated on the ability to fabricate long, flexible meso/microfluidic tubing with high customization. To address this need, here we present a hybrid additive manufacturing (or "three-dimensional (3D) printing") strategy that involves three key steps: (i) using the "Vat Photopolymerization (VPP) technique, "Liquid-Crystal Display (LCD)" 3D printing to print a bulk microfluidic device with three inlets and three concentric outlets; (ii) using "Two-Photon Direct Laser Writing (DLW)" to 3D microprint a coaxial nozzle directly atop the concentric outlets of the bulk microdevice, and then (iii) extruding paraffin oil and a liquid-phase photocurable resin through the coaxial nozzle and into a polydimethylsiloxane (PDMS) channel for UV exposure, ultimately producing the desired tubing. In addition to fabricating the resulting tubing-composed of polymerized photomaterial-at arbitrary lengths (e.g., > 10 cm), the distinct input pressures can be adjusted to tune the inner diameter (ID) and outer diameter (OD) of the fabricated tubing. For example, experimental results revealed that increasing the driving pressure of the liquid-phase photomaterial from 50 kPa to 100 kPa led to fluidic tubing with IDs and ODs of 291±99 µm and 546±76 µm up to 741±31 µm and 888±39 µm, respectively. Furthermore, preliminary results for DLW-printing a microfluidic "M" structure directly atop the tubing suggest that the tubing could be used for "ex situ DLW (esDLW)" fabrication, which would further enhance the utility of the tubing.

Surgical Planning and Optimization of Patient-Specific Fontan Grafts With Uncertain Post-Operative Boundary Conditions and Anastomosis Displacement.

OBJECTIVE: Fontan surgical planning involves designing grafts to perform optimized hemodynamic performance for the patient's long-term health benefit. The uncertainty of post-operative boundary conditions (BC) and graft anastomosis displacements can significantly affect optimized graft designs and lead to undesirable outcomes, especially for hepatic flow distribution (HFD). We aim to develop a computation framework to automatically optimize patient-specific Fontan grafts with the maximized possibility of keeping post-operative results within clinical acceptable thresholds. METHODS: The uncertainties of BC and anastomosis displacements were modeled using Gaussian distributions according to prior research studies. By parameterizing the Fontan grafts, we built surrogate models of hemodynamic parameters taking the design parameters and BC as input. A two-phase reliability-based robust optimization (RBRO) strategy was developed by combining deterministic optimization (DO) and optimization under uncertainty (OUU) to reduce computational cost. RESULTS: We evaluated the performance of the RBRO framework by comparing it with the DO method in four cases of Fontan patients. The results showed that the surgical plans computed from the proposed method yield up to 79.2% improvement in the reliability of the HFD than those of the DO method ( ). The mean values of indexed power loss (iPL) and the percentage of non-physiologic wall shear stress (%WSS) for the optimized surgical plans met the clinically acceptable thresholds. CONCLUSION: This study demonstrated the effectiveness of our RBRO framework to address the uncertainties of BC and anastomosis displacements for Fontan surgical planning. SIGNIFICANCE: The technique developed in this paper demonstrates a significant improvement in the reliability of the predicted post-operative outcomes for Fontan surgical planning. This planning technique is immediately applicable as a building block to enable technology for optimal long-term outcomes for pediatric Fontan patients and can also be used in other pediatric and adult cardiac surgeries.

Semi-Automatic Planning and Three-Dimensional Electrospinning of Patient-Specific Grafts for Fontan Surgery.

This paper proposes a semi-automatic Fontan surgery planning method for designing and manufacturing hemodynamically optimized patient-specific grafts. Fontan surgery is a palliative procedure for patients with a single ventricle heart defect by creating a new path using a vascular graft for the deoxygenated blood to be directed to the lungs, bypassing the heart. However, designing patient-specific grafts with optimized hemodynamic performance is a complex task due to the variety of patient-specific anatomies, confined surgical planning space, and the requirement of simultaneously considering multiple design criteria for vascular graft optimization. To address these challenges, we used parameterized Fontan pathways to explore patient-specific vascular graft design spaces and search for optimal solutions by formulating a nonlinear constrained optimization problem, which minimizes indexed power loss (iPL) of the Fontan model by constraining hepatic flow distribution (HFD), percentage of abnormal wall shear stress (%WSS) and geometric interference between Fontan pathways and the heart models (InDep) within clinically acceptable thresholds. Gaussian process regression was employed to build surrogate models of the hemodynamic parameters as well as InDep and [Formula: see text] (conduit model smoothness indicator) for optimization by pattern search. We tested the proposed method on two patient-specific models (n=2). The results showed the automatically optimized (AutoOpt) Fontan models hemodynamically outperformed or at least are comparable to manually optimized Fontan models with significantly reduced surgical planning time (15 hours versus over 2 weeks). We also demonstrated feasibility of manufacturing the AutoOpt Fontan conduits by using electrospun nanofibers.

Bayesian Optimization for Design of Multi-Actuator Soft Catheter Robots.

Catheter-based diagnosis and therapy have grown increasingly in recent years due to their improved clinical outcomes including decreased morbidity, shorter recovery time and minimally invasiveness compared to open surgeries. Although the scalability, customizability, and diversity of soft catheter robots are widely recognized, designers and roboticists still lack comprehensive techniques for modeling and designing them. This difficulty arises due to their continuum nature, which makes characterizing the properties and predicting a soft catheter's behavior challenging, complicating robot design tasks. In this paper, we propose modeling multi-actuator soft catheters to enable alignment with desired vessel shapes near the target area. We develop mathematical models to simulate the catheter's positioning due to the moments exerted by multiple pneumatic actuators along the catheter and use those models to compare optimization approaches that can achieve catheter alignment along a desired vessel shape. Specifically, our approach proposes finding the optimal geometric and material properties for a multi-actuator soft catheter robot using a bi-level optimization framework. The upper-level optimization process uses a modified Bayesian technique to seek the optimal geometric and material properties of the soft catheter, which minimize the deviance of the actuated catheter from a desired vessel shape, while the lower-level optimization process uses a gradient-based technique to obtain the actuator moments required to achieve that vessel shape. The results demonstrate the capability of our proposed multi-actuator soft catheter to align with the desired vessel shapes, and show that the proposed framework which is in the context of Bayesian optimization has the potential to expedite the design process.

Locally Optimizable Joint Embedding Framework to Design Nitrogen-rich Molecules that are Similar but Improved.

Deep learning has shown great potential for generating molecules with desired properties. But the cost and time required to obtain relevant property data have limited study to only a few classes of materials for which extensive data have already been collected. We develop a deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class. Common low-level physicochemical properties are jointly embedded into a latent space that can be used to design molecules in the smaller class. The chemical space around the molecules in the training set is explored through local gradient ascent optimization. Based on nine molecules from the original training set, nine new molecules are found to have improved properties while remaining structurally similar to the training molecules thereby easing requirements for entirely new synthesis routes. Validation is performed using an equilibrium thermochemistry code to verify the molecules and target properties. A specific example targeting the Chapman-Jouguet velocity and small data for nitrogen-rich molecules is shown. Despite the relative lack of nitrogen-rich molecule data, the results demonstrate that fusing and joint embedding with plentiful low nitrogen molecular data can produce higher generative performance than using the scarce data alone.

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots.

Catheter-based endovascular interventional procedures have become increasingly popular in recent years as they are less invasive and patients spend less time in the hospital with less recovery time and less pain. These advantages have led to a significant growth in the number of procedures that are performed annually. However, it is still challenging to position a catheter in a target vessel branch within the highly complicated and delicate vascular structure. In fact, vessel tortuosity and angulation, which cause difficulties in catheterization and reaching the target site, have been reported as the main causes of failure in endovascular procedures. Maneuverability of a catheter for intravascular navigation is a key to reaching the target area; ability of a catheter to move within the target vessel during trajectory tracking thus affects to a great extent the length and success of the procedure. To address this issue, this paper models soft catheter robots with multiple actuators and provides a time-dependent model for characterizing the dynamics of multi-actuator soft catheter robots. Built on this model, an efficient and scalable optimization-based framework is developed for guiding the catheter to pass through arteries and reach the target where an aneurysm is located. The proposed framework models the deflection of the multi-actuator soft catheter robot and develops a control strategy for movement of catheter along a desired trajectory. This provides a simulation-based framework for selection of catheters prior to endovascular catheterization procedures, assuring that given a fixed design, the catheter is able to reach the target location. The results demonstrate the benefits that can be achieved by design and control of catheters with multiple number of actuators for navigation into small vessels.

Automatic Shape Optimization of Patient-Specific Tissue Engineered Vascular Grafts for Aortic Coarctation.

This paper proposes a computational framework for automatically optimizing the shapes of patient-specific tissue engineered vascular grafts. We demonstrate a proof-of-concept design optimization for aortic coarctation repair. The computational framework consists of three main components including 1) a free-form deformation technique exploring graft geometries, 2) high-fidelity computational fluid dynamics simulations for collecting data on the effects of design parameters on objective function values like energy loss, and 3) employing machine learning methods (Gaussian Processes) to develop a surrogate model for predicting results of high-fidelity simulations. The globally optimal design parameters are then computed by multistart conjugate gradient optimization on the surrogate model. In the experiment, we investigate the correlation among the design parameters and the objective function values. Our results achieve a 30% reduction in blood flow energy loss compared to the original coarctation by optimizing the aortic geometry.

Organizing Tagged Knowledge: Similarity Measures and Semantic Fluency in Structure Mining.

Recovering a system's underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system's behavior. For example, making reliable predictions about system failures based on maintenance work-order data requires determining how concepts described within the work order are related. Obtaining such structural information is challenging, requiring system understanding, synthesis, and representation design. This is often either too difficult or too time-consuming to produce. Consequently, a common approach to quickly eliciting tacit structural knowledge from experts is to gather uncontrolled keywords as record labels-i.e., "tags." One can then map those tags to concepts within the structure and quantitatively infer relationships between them. Existing models of tag similarity tend to either depend on correlation strength (e.g. overall co-occurrence frequencies), or on conditional strength (e.g. tag sequence probabilities). A key difficulty in applying either model is understanding under what conditions one is better than the other for overall structure recovery. In this paper, we investigate the core assumptions and implications of these two classes of similarity measures on structure recovery tasks. Then, using lessons from this characterization, we borrow from recent psychology literature on semantic fluency tasks to construct a tag similarity measure that emulates how humans recall tags from memory. We show through empirical testing that this method combines strengths of both common modeling paradigms. We also demonstrate its potential as a pre-processor for structure mining tasks via a case study in semi-supervised learning on real excavator maintenance work-orders.

Applying machine learning techniques to predict the properties of energetic materials.

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.