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Chinese University of Hong Kong Studies 3D Printing for Heart Disease

In the recently published ‘Three-dimensional printing in structural heart disease and intervention,’ authors Yiting Fan, Randolph H.L. Wong, and Alex Pui-Wai Lee, all from The Chinese University of Hong Kong, explore the potential for 3D printing in the world of medicine, as well as cardiology—and more specifically, structural heart disease (SHD).

SHD causes issues like:

  • Aortic stenosis
  • Mitral regurgitation
  • Atrial septal defect
  • Left atrial appendage (LAA) clots

Conventional imaging is limited, while the emergence of 3D printed models allows medical professionals to progress from mentally reconstructing 2D images to gaining a more complex understanding of pathology.

As 3D printing continues to make its way into the realm of medicine, models are used for:

  • Guiding treatment
  • Procedural simulation
  • Facilitating hemodynamic research
  • Improving interventional training
  • Promoting patient-clinician communication

To create a medical model, images must be attained, data must be processed, and the object must be 3D printed.

“The most commonly used imaging sources for SHD are echocardiography, computed tomography (CT) and magnetic resonance imaging (MRI),” state the researchers. “Other modalities, such as positron emission tomography, single photon emission CT and cone beam CT, are less commonly used. All images should use the common Digital Imaging and Communication in Medicine (DICOM) format.

Pre-procedural simulation of MitraClip on 3D-printed model. (A) Digital model of a heart. Different colors stand for different cardiac components (grey: mitral valves; light pink: tricuspid valves; light gold: atrial septum, left atrium, left and right ventricle). (B) Multi-material 3D-printed heart model for pre-procedural simulation. The valves were printed with flexible material and the rests were printed with hard material. (C) The 5 holes drilled in the atrial septum represents the different position for different kinds of structural heart interventions. (D) The MiraClip device was released via delivery catheter through the atrial septum to the mitral valve. Blue circle: MitraClip; red circle: left atrial appendage occlusion (LAAO). S, superior; A, anterior; P, posterior; I, inferior; IVC, inferior vena cava; LAA, left atrial appendage; MV, mitral valve.

A range of materials are both popular and possible for fabricating medical models:

“Multi-material printing by material jetting is increasingly used to create cardiac structures. Different tissue components were printed with different textures. For instance, an aortic valve was printed with flexible printing material, and the calcifications attached to valves were printed with hard printing material, respectively,” report the authors.

Application of 3D printing for peri-device leak. (A,B,C) A case found with peri-device leak post TAVI and needed peri-device leak occlusion: (A) routine TEE post-TAVI showed peri-device leak (yellow circle); (B) simulation of peri-device leak occlusion on 3D-printed aortic root model derived from post-TAVI CT; (C) the 10-mm vascular plug was found to be best-fit for this case. (D,E,F,G) A case found with residual leak after ASD closure: (D) multi-material 3D printed model showed residual leak (blue circle) next to the ASD occluder (asterisk *); (E) the delivery catheter went through the leak position; (F) the device (two asterisks **) was released in situ. (G) The bicaval view of 3D-printed model showed stable release and stay of the chosen device. 3D, three-dimensional; TAVI, transcatheter aortic valve implantation; TEE, transesophageal echo; CT, computed tomography; ASD, atrial septal defect.

There continue to be ongoing challenges in the creation of medical models, however, ‘despite the enthusiasm in applying 3D printing cardiovascular medicine.’ While there is an obvious lack of technical standards, mainly due to the novelty of the technology, the authors point out also that there are still issues with affordability—along with ‘scant evidence on the added clinical benefit.’

Greater accuracy is needed, along with improved standardization of data acquisition, and post-processing techniques. While deeper research is required into the creation and use of models and surgical guides, so are comparisons for offering up better information and creating industry standards. The authors also recommended a more streamlined workflow.

“The mechanical properties of the 3D-printed materials, such as tensile strength, elasticity, flexibility, hardness, and durability have utmost importance for cardiovascular applications. The majority of cardiovascular applications reported so far have employed materials with properties that have not been meticulously compared with the cardiovascular tissue they are mimicking. Validation of 3D-printed material properties against actual human patient tissues is important to ensure that procedural simulation is realistic,” conclude the authors.

“Further effort in technical standardization, and clinical evaluation of added benefit and cost-effectiveness of 3D printing are needed to bring this promising technique to clinical reality.”

3D printed medical models are extremely beneficial to doctors and patients as they allow not only for diagnosing but have also continued to change medicine—allowing for procedures involving complex reconstructions, fabrication of surgical guides, and much more.

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[Source / Images: ‘Three-dimensional printing in structural heart disease and intervention’]

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Collaborative Research Team Develops Density-Graded Structure for Extrusion 3D Printing of Functionally Graded Materials

Microscopic photos of top and side views of printing results with a 0.38 mm wide extrusion path: (a) without versus (b) with overlapping by 0.36 mm respectively. Overlapping extrusion paths exhibit over-extrusion of material at the overlapping region, which results in unwanted blobs on the surface of the print.

Plenty of research has been completed in regards to FDM (extrusion) 3D printing, such as how to improve part quality and how to reliably fabricate functionally graded materials (FGM). The latter is what a collaborative team of researchers from Ultimaker, the Delft University of Technology (TU Delft), and the Chinese University of Hong Kong are focusing on in their new research project.

The team – made up of researchers Tim Kuipers, Jun Wu Charlie, and C.L. Wang – recently published a paper, titled “CrossFill: Foam Structures with Graded Density for Continuous Material Extrusion,” which will be presented at this year’s Symposium for Solid and Physical Modeling.

“In our latest paper we present a type of microstructure which can be printed using continuous extrusion so that we can generate infill structures which follow a user specified density field to be printed reliably by standard desktop FDM printers,” Kuipers, a Software Engineer and Researcher for Ultimaker, wrote in an email.

“This is the first algorithm in the world which is able to generate spatially graded microstructures while adhering to continuous extrusion in order to ensure printing reliability.”

Because 3D printing offers such flexible fabrication, many people want to design structures with spatially graded material properties. But, it’s hard to achieve good print quality when using FDM technology to 3D print FGM, since these sorts of infill structures feature complex geometry. In terms of making foam structures with graded density using FDM, the researchers knew they needed to develop a method to generate “infill structures according to a user-specific density distribution.”

The abstract reads, “In this paper, we propose a new type of density graded structure that is particularly designed for 3D printing systems based on filament extrusion. In order to ensure high-quality fabrication results, extrusion-based 3D printing requires not only that the structures are self-supporting, but also that extrusion toolpaths are continuous and free of self-overlap. The structure proposed in this paper, called CrossFill, complies with these requirements. In particular, CrossFill is a self-supporting foam structure, for which each layer is fabricated by a single, continuous and overlap-free path of material extrusion. Our method for generating CrossFill is based on a space-filling surface that employs spatially varying subdivision levels. Dithering of the subdivision levels is performed to accurately reproduce a prescribed density distribution.”

Their method – a novel type of FDM printable foam structure – offers a way to refine the structure to match a prescribed density distribution, and provides a novel self-supporting, space-filling surface to support spatially graded density, as well as an algorithm that can merge an infill structure’s toolpath with the model’s boundary for continuity. This space-filling infill surface is called CrossFill, as the toolpath resembles crosses.

“Each layer of CrossFill is a space-filling curve that can be continuously extruded along a single overlap-free toolpath,” the researchers wrote. “The space-filling surface consists of surface patches which are embedded in prism-shaped cells, which can be adaptively subdivided to match the user-specified density distribution. The adaptive subdivision level results in graded mechanical properties throughout the foam structure. Our method consists of a step to determine a lower bound for the subdivision levels at each location and a dithering step to refine the local average densities, so that we can generate CrossFill that closely matches the required density distribution. A simple and effective algorithm is developed to merge a space-filling curve of CrossFill of a layer into the closed polygonal areas sliced from the input model. Physical printing tests have been conducted to verify the performance of the CrossFill structures.”

The researchers say that the user prescribes density distribution, and can use CrossFill and its space-filling surfaces, with continuous cross sections, to “reliably reproduce the distribution using extrusion-based printing.” CrossFill surfaces are built by using subdivision rules on prism-shaped cells, each of which contains a surface patch that’s “sliced into a line segment on each layer to be a segment” of the toolpath, which will be made with a constant width; cell size determines the density.

“By adaptively applying the subdivision rules to the prism cells, we create a subdivision structure of cells with a density distribution that closely matches a user-specified input,” the team wrote. “Continuity of the space-filling surface across adjacent cells with different subdivision levels – both horizontally and vertically – is ensured by the subdivision rules and by post-processing of the surface patches in neighboring cells.”

The subdivision system distinguishes an H-prism, which is built by cutting a cube in half vertically along a diagonal of the horizontal faces, and a Q-prism, generated by spitting a cube into quarters along the faces’ diagonals. To learn more about this system and the team’s algorithms, check out the paper in its entirety.

Schematic overview of our method. The top row shows a 2D analogue of our method for clear visualization. The prism-shaped cells in the bottom row are visualized as semi-opaque solids to keep the visualization uncluttered. Red lines in the bottom row highlight the local subdivisions performed in the dithering phase.

The researchers also explained the method’s toolpath generation in their paper, starting with how to slice the infill structure into a continuous 2D polygonal curve for each layer of the object, which is followed by fitting a layer’s curve “into the region of an input 3D model.”

Experiments measuring features like accuracy, computation time, and elastic behavior were completed on an Intel Core i7-7500U CPU @ 2.70 GHz, using test structures 3D printed out of white TPU 95A on Ultimaker 3 systems with the default Cura 4.0 profile of 0.1 mm layer thickness. The team also discussed various applications for CrossFill, such as imaging phantoms for the medical field or cushions and packaging.

“The study of experimental tests shows that CrossFill acts very much like a foam although future work needs to be conducted to further explore the mapping between density and other material properties,” the researchers concluded. “Another line of research is to further enhance the dithering technique, e.g. changing the weighing scheme of error diffusion.”

CrossFill applications. (a) Bicycle saddle with a density specification. A weight of 33 N is added on various locations to show the different response of different density infill. (b) Teddy bear with a density specification. (c) Shoe sole with densities based on a pressure map of a foot. (d) Stanford bunny painted with a density specification. (e) Medical phantom with an example density distribution for calibrating an MRI scanning procedure.

The team’s open source implementation is available here on GitHub. To learn more, check out their video below:

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Researchers Decrease Support Structures for Models Through Multidirectional 3D Printing

An illustration for the idea of the algorithm: (a) a progressively determined planar clipping results for generating the optimized base planes, and (b) the inverse order of clipping planes results in a sequence of regions to be fabricated where the printing direction of each region is the normal of its base plane. The orientation of a printing head is fixed during the procedure of physical fabrication. The parts under fabrication are reoriented to realize the multidirectional 3D printing.

In most planar-layer based 3D printing systems, material collapse is prevented on large overhangs by adding support structures to the bottom. But support structures in single-material 3D printing methods have some major issues, like material waste and the possibility of surface damage. This can be helped by introducing rotation and turning the hardware into a multidirectional system, where models are subdivided into separate regions and each one is 3D printed along a different direction.

L-R: Snowman models fabricated by an FDM 3D printer and the team’s multidirectional 3D printing system by adding only one rotational axis on the same 3D printer.

A team of researchers from Tsinghua University, TU Delft, and the Chinese University of Hong Kong developed two types of multidirectional 3D printing hardware systems: one modified from an off-the-shelf FDM 3D printer with an added rotational degree-of-freedom (DOF), and the other implemented on an industrial robotic arm to simulate a tilting table for two rotational DOFs. They outlined their work in a paper titled “General Support-Effective Decomposition for Multi-Directional 3D Printing.”

The abstract reads, “We present a method to fabricate general models by multi-directional 3D printing systems, in which different regions of a model are printed along different directions. The core of our method is a support-effective volume decomposition algorithm that targets on minimizing the usage of support-structures for the regions with large overhang. Optimal volume decomposition represented by a sequence of clipping planes is determined by a beam-guided searching algorithm according to manufacturing constraints. Different from existing approaches that need manually assemble 3D printed components into a final model, regions decomposed by our algorithm can be automatically fabricated in a collision-free way on a multi-directional 3D printing system. Our approach is general and can be applied to models with loops and handles. For those models that cannot completely eliminate support for large overhang, an algorithm is developed to generate special supporting structures for multi-directional 3D printing. We developed two different hardware systems to physically verify the effectiveness of our method: a Cartesian-motion based system and an angular-motion based system. A variety of 3D models have been successfully fabricated on these systems.”

The researchers wanted to create a 3D printing system that would be able to “add rotational motion into the material accumulation process” to ensure fewer supports, if any. To do so, they created a general volume decomposition algorithm, which “can be generally applied to models with different shape and topology.”

“Moreover, a support generation algorithm has been developed for multidirectional 3D printing,” the researchers explained. “The techniques developed here can speedup the manufacturing of 3D printed freeform models by saving the time of producing and removing supports.”

Progressive results of fabricating models on 4DOF multidirectional 3D printing system and a 5DOF system realized on a robotic arm.

The research team’s paper made several technical contributions, including their support-effective algorithm, which is based on beam-guided search and can be applied to 3D models with handles and loops. In addition, they also summarized decomposition criteria through their multidirectional 3D printing process and created “a region-projection based method” for generating supports for multidirectional 3D printing.

There are, however, some drawbacks involved when changing from one 3D printing direction to another, such as slowing down the process, which is why the researchers “prefer a solution with less number of components, which can be achieve by considering the following criterion of clipping.”

A comparison of decomposition results obtained from three schemes introduced in this paper.

“After relaxing the hard-constraint of support-free into minimizing the area of risky faces as described in JG, the scheme of generating support is considerately vital while both feasibility and reliability should be guaranteed,” the researchers wrote. “To tackle this problem, we propose a new pattern called projected supports that ensures the fabrication of remained overhanging regions through a collision-free multi-directional 3D printing.”

The decomposed and 3D printed results fabricated by the system with 4DOF and 5DOF in motion.

The team applied their algorithm to several models, and were able to reduce, and even eliminate in some cases, the need for support structures. In addition, their method’s “computational efficiency” was on par with general 3D printing time.

“We present a volume decomposition framework for the support-effective fabrication of general models by multidirectional 3D printing,” the researchers concluded. “A beam-guided search is conducted in our approach to avoid local optimum when computing decomposition. Different from prior work relying on a skeletal tree structure, our approach is general and can handle models with multiple loops and handles. Moreover, a support generation scheme has been developed in our framework to enable the fabrication of all models. Manufacturing constrains such as the number of rotational axes can be incorporated during the orientation sampling process. As a result, our algorithm supports both the 4DOF and the 5DOF systems. A variety of models have been tested on our approach as examples. Hareware setups have been developed to take the physical experiments for verifying the effectiveness of our system.”

Co-authors of the paper are Chenming Wu, Chengkai Dai, Guoxin Fang, Yong-Jin Liu, and Charlie C.L. Wang.

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