Patient-specific 3D Printing : A Novel Technique for Complex Pediatric Renal Transplantation

3D-printed models fabricated from CT, MRI, or echocardiography data provide the advantage of haptic feedback, direct manipulation, and enhanced understanding of cardiovascular anatomy and underlying pathologies. Reported applications of cardiovascular 3D printing span from diagnostic assistance and optimization of management algorithms in complex cardiovascular diseases, to planning and simulating surgical and interventional procedures. The technology has been used in practically the entire range of structural, valvular, and congenital heart diseases, and the added-value of 3D printing is established. Patient-specific implants and custom-made devices can be designed, produced, and tested, thus opening new horizons in personalized patient care and cardiovascular research. Physicians and trainees can better elucidate anatomical abnormalities with the use of 3D-printed models, and communication with patients is markedly improved. Cardiovascular 3D bioprinting and molecular 3D printing, although currently not translated into clinical practice, hold revolutionary potential. 3D printing is expected to have a broad influence in cardiovascular care, and will prove pivotal for the future generation of cardiovascular imagers and care providers. In this Review, we summarize the cardiovascular 3D printing workflow, from image acquisition to the generation of a hand-held model, and discuss the cardiovascular applications and the current status and future perspectives of cardiovascular 3D printing.

Modern imaging techniques are an essential component of preoperative planning in plastic and reconstructive surgery. However, conventional modalities, including three-dimensional (3D) reconstructions, are limited by their representation on 2D workstations. 3D printing, also known as rapid prototyping or additive manufacturing, was once the province of industry to fabricate models from a computer-aided design (CAD) in a layer-by-layer manner. The early adopters in clinical practice have embraced the medical imaging-guided 3D-printed biomodels for their ability to provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. With increasing accessibility, investigators are able to convert standard imaging data into a CAD file using various 3D reconstruction softwares and ultimately fabricate 3D models using 3D printing techniques, such as stereolithography, multijet modeling, selective laser sintering, binder jet technique, and fused deposition modeling. However, many clinicians have questioned whether the cost-to-benefit ratio justifies its ongoing use. The cost and size of 3D printers have rapidly decreased over the past decade in parallel with the expiration of key 3D printing patents. Significant improvements in clinical imaging and user-friendly 3D software have permitted computer-aided 3D modeling of anatomical structures and implants without outsourcing in many cases. These developments offer immense potential for the application of 3D printing at the bedside for a variety of clinical applications. In this review, existing uses of 3D printing in plastic surgery practice spanning the spectrum from templates for facial transplantation surgery through to the formation of bespoke craniofacial implants to optimize post-operative esthetics are described. Furthermore, we discuss the potential of 3D printing to become an essential office-based tool in plastic surgery to assist in preoperative planning, developing intraoperative guidance tools, teaching patients and surgical trainees, and producing patient-specific prosthetics in everyday surgical practice.

Background Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded. Results Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.