Three-dimensional printing in congenital heart disease

Advanced imaging for pre- and post-operative evaluation of tetralogy of Fallot

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease and imaging plays a key role in diagnosis, pre-operative planning and follow up, with MRI as the gold standard for imaging in these patients despite echocardiography being more widely available. While static MRI sequences are suitable for evaluation of anatomical structures, dynamic imaging is required for volume and flow measurements through valves, chambers, and surgical conduits. Newer techniques with 4D data acquisitions allow for feasible 2D cine reconstruction in desired planes. Ferumoxytol, a blood pool contrast agent with long intravascular half-life, facilitates acquisition of 4D flow and 4D MUSIC (multiphase, steady-state imaging with contrast) sequences, eliminating need for repeated contrast administrations. In this article we review conglomerate of TOF anomalies, their historical and current surgical managements with respective devices, as well as cutting-edge MRI techniques for their evaluation.

Application of Three-Dimensional Printing in Maternity and Pediatric Nursing Care

ABSTRACT

Thre e-dimensional (3D) printing is an emerging technology that expanded quickly into a diverse array of clinical applications over the last decade. 3D printing, often called additive manufacturing, uses specialized printers to create objects through the addition of materials layer-by-layer. Using computer-aided design software via a 3D scanner or a digital camera, objects can be printed to highly precise and specific dimensions. This technology, including both the hardware and software, has applications in surgical procedures, dental implants and crowns, pharmaceuticals, and biomedical products. With the enormous potential of using 3D printing in multiple health care sectors, there is still limited usage for this technology in maternal and child health nursing practice. We provide an overview of 3D printing technology, review the current health care applications, and explore the opportunities and challenges of 3D printing in maternal and child nursing.

Neo-aorta reconstruction with retained stented patch in Norwood procedure after hybrid palliation of high-risk hypoplastic left heart syndrome: a case report

Hybrid procedure of hypoplastic left heart syndrome, comprising ductus arteriosus stenting and bilateral pulmonary artery banding, is a good surgical option for initial palliative procedure for high-risk patients for Norwood procedure. However, ductal stenting may cause retrograde aortic blood flow obstruction. Furthermore, complete removal of stent while performing the Norwood procedure make the operation more difficult. We report a case that overcame these problems using a novel surgical technique.

Impact of 3D Printing on Cardiac Surgery in Congenital Heart Diseases: A Systematic Review and Meta-Analysis

BACKGROUND

Congenital heart disease (CHD) poses significant challenges in surgical management due to the complexity of cardiac anatomy. Three-dimensional (3D) printing has emerged as a promising tool in preoperative planning, intraoperative guidance, and medical education for CHD surgeries.

OBJECTIVES

We aimed to systematically review the literature on the utilization and benefits of 3D printing technology in CHD surgical interventions.

METHODS

A systematic search was conducted across PubMed and EMBASE for studies published up to February of 2024. We included controlled and uncontrolled studies investigating the surgical role of 3D printing in CHD patients. We conducted a single-arm meta-analysis estimating the proportion of change in treatment planning due to the use of 3D printed-models. Moreover, studies that compared 3D printing to conventional care were included into the meta-analysis. A p-value < 0.05 was considered statistically significant.

RESULTS

A total of 21 studies met the inclusion criteria, comprising 444 patients undergoing CHD surgeries with 3D printing assistance. Preoperative planning aided by 3D models led to changing surgical decisions in 35 of 75 cases (51.8%; 95% CI 26.6-77.0%, I2=80.68%, p=0.001) and reduced total operative time in 22.25 minutes in favor of the 3D printing group (95%CI 49.95; 5.80 min, I2=0%, p=0.817) but without statistical significance. Albeit in a smaller sample, other endpoints (mechanical ventilation and ICU time) demonstrated some benefit from the technology but without statistical significance.

CONCLUSIONS

By providing personalized anatomical models, 3D printing may facilitate surgical planning and execution. More studies are needed to investigate the effects of 3D printing on reducing intervention, hospitalization, and mechanical ventilation times.

SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. After training with a dataset containing the cardiac anatomies of 67 patients spanning 6 CHD types and 14 combinations of CHD types, our method successfully captures divergent anatomical variations across different types and the meaningful intermediate CHD states across the spectrum of related CHD diagnoses. Additionally, our method demonstrates superior performance in CHD anatomy generation in terms of CHD-type correctness and shape plausibility. It also exhibits comparable generalization performance when reconstructing unseen cardiac anatomies. Moreover, our approach shows potential in augmenting image-segmentation pairs for rarer CHD types to significantly enhance cardiac segmentation accuracy for CHDs. Furthermore, it enables the generation of CHD cardiac meshes for computational simulation, facilitating a systematic examination of the impact of CHDs on cardiac functions.

SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects

Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis, which conveniently captures divergent anatomical variations across different types and represents meaningful intermediate CHD states. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.

Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations

Three-dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients' needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.

Three-dimensional printed moulds to obtain silicone hearts with congenital defects for paediatric heart-surgeon training

OBJECTIVES

Many types of congenital heart disease are amenable to surgical repair or palliation. The procedures are often challenging and require specific surgical training, with limited real-life exposure and often costly simulation options. Our objective was to create realistic and affordable 3D simulation models of the heart and vessels to improve training.

METHODS

We created moulded vessel models using several materials, to identify the material that best replicated human vascular tissue. This material was then used to make more vessels to train residents in cannulation procedures. Magnetic resonance imaging views of a 23-month-old patient with double-outlet right ventricle were segmented using free open-source software. Re-usable moulds produced by 3D printing served to create a silicone model of the heart, with the same material as the vessels, which was used by a heart surgeon to simulate a Rastelli procedure.

RESULTS

The best material was a soft elastic silicone (Shore A hardness 8). Training on the vessel models decreased the residents' procedural time and improved their grades on a performance rating scale. The surgeon evaluated the moulded heart model as realistic and was able to perform the Rastelli procedure on it. Even if the valves were poorly represented, it was found to be useful for preintervention training.

CONCLUSIONS

By using free segmentation software, a relatively low-cost silicone and a technique based on re-usable moulds, the cost of obtaining heart models suitable for training in congenital heart defect surgery can be substantially decreased.

Role of Three-Dimensional Visualization Modalities in Medical Education

For the past two decades, slide-based presentation has been the method of content delivery in medical education. In recent years, other teaching modalities involving three-dimensional (3D) visualization such as 3D printed anatomical models, virtual reality (VR), and augmented reality (AR) have been explored to augment the education experience. This review article will analyze the use of slide-based presentation, 3D printed anatomical models, AR, and VR technologies in medical education, including their benefits and limitations.