3D Printing for Cardiovascular Applications: From End-to-End Processes to Emerging Developments

The Application of Precision Medicine in Structural Heart Diseases: A Step towards the Future

The personalized applications of 3D printing in interventional cardiology and cardiac surgery represent a transformative paradigm in the management of structural heart diseases. This review underscores the pivotal role of 3D printing in enhancing procedural precision, from preoperative planning to procedural simulation, particularly in valvular heart diseases, such as aortic stenosis and mitral regurgitation. The ability to create patient-specific models contributes significantly to predicting and preventing complications like paravalvular leakage, ensuring optimal device selection, and improving outcomes. Additionally, 3D printing extends its impact beyond valvular diseases to tricuspid regurgitation and non-valvular structural heart conditions. The comprehensive synthesis of the existing literature presented here emphasizes the promising trajectory of individualized approaches facilitated by 3D printing, promising a future where tailored interventions based on precise anatomical considerations become standard practice in cardiovascular care.

Fabrication of Soft Transparent Patient-Specific Vascular Models with Stereolithographic 3D printing and Thiol-Based Photopolymerizable Coatings

An ideal vascular phantom should be anatomically accurate, have mechanical properties as close as possible to the tissue, and be sufficiently transparent for ease of visualization. However, materials that enable the convergence of these characteristics have remained elusive. The fabrication of patient-specific vascular phantoms with high anatomical fidelity, optical transparency, and mechanical properties close to those of vascular tissue is reported. These final properties are achieved by 3D printing patient-specific vascular models with commercial elastomeric acrylic-based resins before coating them with thiol-based photopolymerizable resins. Ternary thiol-ene-acrylate chemistry is found optimal. A PETMP/allyl glycerol ether (AGE)/polyethylene glycol diacrylate (PEGDA) coating with a 30/70% AGE/PEGDA ratio applied on a flexible resin yielded elastic modulus, UTS, and elongation of 3.41 MPa, 1.76 MPa, and 63.2%, respectively, in range with the human aortic wall. The PETMP/AGE/PEGDA coating doubled the optical transmission from 40% to 80%, approaching 88% of the benchmark silicone-based elastomer. Higher transparency correlates with a decrease in surface roughness from 2000 to 90 nm after coating. Coated 3D-printed anatomical replicas are showcased for pre-procedural planning and medical training with good radio-opacity and echogenicity. Thiol-click chemistry coatings, as a surface treatment for elastomeric stereolithographic 3D-printed objects, address inherent limitations of photopolymer-based additive manufacturing.

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

"From Vision to Reality: Virtual Reality's Impact on Baffle Planning in Congenital Heart Disease"

This study aims to evaluate the feasibility and utility of virtual reality (VR) for baffle planning in congenital heart disease (CHD), specifically by creating patient-specific 3D heart models and assessing a user-friendly VR interface. Patient-specific 3D heart models were created using high-resolution imaging data and a VR interface was developed for baffle planning. The process of model creation and the VR interface were assessed for their feasibility, usability, and clinical relevance. Collaborative and interactive planning within the VR space were also explored. The study findings demonstrate the feasibility and usefulness of VR in baffle planning for CHD. Patient-specific 3D heart models generated from imaging data provided valuable insights into complex spatial relationships. The developed VR interface allowed clinicians to interact with the models, simulate different baffle configurations, and assess their impact on blood flow. The VR space's collaborative and interactive planning enhanced the baffle planning process. This study highlights the potential of VR as a valuable tool in baffle planning for CHD. The findings demonstrate the feasibility of using patient-specific 3D heart models and a user-friendly VR interface to enhance surgical planning and patient outcomes. Further research and development in this field are warranted to harness the full benefits of VR technology in CHD surgical management.

Fabrication of deformable patient-specific AAA models by material casting techniques

Background

Abdominal Aortic Aneurysm (AAA) is a balloon-like dilatation that can be life-threatening if not treated. Fabricating patient-specific AAA models can be beneficial for in-vitro investigations of hemodynamics, as well as for pre-surgical planning and training, testing the effectiveness of different interventions, or developing new surgical procedures. The current direct additive manufacturing techniques cannot simultaneously ensure the flexibility and transparency of models required by some applications. Therefore, casting techniques are presented to overcome these limitations and make the manufactured models suitable for in-vitro hemodynamic investigations, such as particle image velocimetry (PIV) measurements or medical imaging.

Methods

Two complex patient-specific AAA geometries were considered, and the related 3D models were fabricated through material casting. In particular, two casting approaches, i.e. lost molds and lost core casting, were investigated and tested to manufacture the deformable AAA models. The manufactured models were acquired by magnetic resonance, computed tomography (CT), ultrasound imaging, and PIV. In particular, CT scans were segmented to generate a volumetric reconstruction for each manufactured model that was compared to a reference model to assess the accuracy of the manufacturing process.

Results

Both lost molds and lost core casting techniques were successful in the manufacturing of the models. The lost molds casting allowed a high-level surface finish in the final 3D model. In this first case, the average signed distance between the manufactured model and the reference was (- 0.2 ± 0.2 ) mm. However, this approach was more expensive and time-consuming. On the other hand, the lost core casting was more affordable and allowed the reuse of the external molds to fabricate multiple copies of the same AAA model. In this second case, the average signed distance between the manufactured model and the reference was (0.1 ± 0.6 ) mm. However, the final model's surface finish quality was poorer compared to the model obtained by lost molds casting as the sealing of the outer molds was not as firm as the other casting technique.

Conclusions

Both lost molds and lost core casting techniques can be used for manufacturing patient-specific deformable AAA models suitable for hemodynamic investigations, including medical imaging and PIV.

Three-dimensional printing in modelling mitral valve interventions

Mitral interventions remain technically challenging owing to the anatomical complexity and heterogeneity of mitral pathologies. As such, multi-disciplinary pre-procedural planning assisted by advanced cardiac imaging is pivotal to successful outcomes. Modern imaging techniques offer accurate 3D renderings of cardiac anatomy; however, users are required to derive a spatial understanding of complex mitral pathologies from a 2D projection thus generating an 'imaging gap' which limits procedural planning. Physical mitral modelling using 3D printing has the potential to bridge this gap and is increasingly being employed in conjunction with other transformative technologies to assess feasibility of intervention, direct prosthesis choice and avoid complications. Such platforms have also shown value in training and patient education. Despite important limitations, the pace of innovation and synergistic integration with other technologies is likely to ensure that 3D printing assumes a central role in the journey towards delivering personalised care for patients undergoing mitral valve interventions.

Trends and Challenges in the Development of 3D-Printed Heart Valves and Other Cardiac Implants: A Review of Current Advances

This article provides a comprehensive review of the current trends and challenges in the development of 3D-printed heart valves and other cardiac implants. By providing personalized solutions and pushing the limits of regenerative medicine, 3D printing technology has revolutionized the field of cardiac healthcare. The use of several organic and synthetic polymers in 3D printing heart valves is explored in this article, with emphasis on both their benefits and drawbacks. In cardiac tissue engineering, stem cells are essential, and their potential to lessen immunological rejection and thrombogenic consequences is highlighted. In the clinical applications section, the article emphasizes the importance of 3D printing in preoperative planning. Surgery results are enhanced when surgeons can visualize and assess the size and placement of implants using patient-specific anatomical models. Customized implants that are designed to match the anatomy of a particular patient reduce the likelihood of complications and enhance postoperative results. The development of physiologically active cardiac implants, made possible by 3D bioprinting, shows promise by eliminating the need for artificial valves. In conclusion, this paper highlights cutting-edge research and the promise of 3D-printed cardiac implants to improve patient outcomes and revolutionize cardiac treatment.

Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of the Coronary Atlas. Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

Combining Cell Technologies With Biomimetic Tissue Engineering Applications: A New Paradigm for Translational Cardiovascular Therapies

Cardiovascular disease is a major cause of morbidity and mortality worldwide and, to date, the clinically available prostheses still present several limitations. The design of next-generation regenerative replacements either based on cellular or extracellular matrix technologies can address these shortcomings. Therefore, tissue engineered constructs could potentially become a promising alterative to the current therapeutic options for patients with cardiovascular diseases. In this review, we selectively present an overview of the current tissue engineering tools such as induced pluripotent stem cells, biomimetic materials, computational modeling, and additive manufacturing technologies, with a focus on their application to translational cardiovascular therapies. We discuss how these advanced technologies can help the development of biomimetic tissue engineered constructs and we finally summarize the latest clinical evidence for their use, and their potential therapeutic outcome.

Algorithms used in medical image segmentation for 3D printing and how to understand and quantify their performance

BACKGROUND

3D printing (3DP) has enabled medical professionals to create patient-specific medical devices to assist in surgical planning. Anatomical models can be generated from patient scans using a wide array of software, but there are limited studies on the geometric variance that is introduced during the digital conversion of images to models. The final accuracy of the 3D printed model is a function of manufacturing hardware quality control and the variability introduced during the multiple digital steps that convert patient scans to a printable format. This study provides a brief summary of common algorithms used for segmentation and refinement. Parameters for each that can introduce geometric variability are also identified. Several metrics for measuring variability between models and validating processes are explored and assessed.

METHODS

Using a clinical maxillofacial CT scan of a patient with a tumor of the mandible, four segmentation and refinement workflows were processed using four software packages. Differences in segmentation were calculated using several techniques including volumetric, surface, linear, global, and local measurements.

RESULTS

Visual inspection of print-ready models showed distinct differences in the thickness of the medial wall of the mandible adjacent to the tumor. Volumetric intersections and heatmaps provided useful local metrics of mismatch or variance between models made by different workflows. They also allowed calculations of aggregate percentage agreement and disagreement which provided a global benchmark metric. For the relevant regions of interest (ROIs), statistically significant differences were found in the volume and surface area comparisons for the final mandible and tumor models, as well as between measurements of the nerve central path. As with all clinical use cases, statistically significant results must be weighed against the clinical significance of any deviations found.

CONCLUSIONS

Statistically significant geometric variations from differences in segmentation and refinement algorithms can be introduced into patient-specific models. No single metric was able to capture the true accuracy of the final models. However, a combination of global and local measurements provided an understanding of important geometric variations. The clinical implications of each geometric variation is different for each anatomical location and should be evaluated on a case-by-case basis by clinicians familiar with the process. Understanding the basic segmentation and refinement functions of software is essential for sites to create a baseline from which to evaluate their standard workflows, user training, and inter-user variability when using patient-specific models for clinical interventions or decisions.