Establishing 3D Printing at the Point of Care: Basic Principles and Tools for Success

Accuracy and feasibility in building a personalized 3D printed femoral pseudoaneurysm model for endovascular training

BACKGROUND

The use of three-dimensional(3D) printing is broadly across many medical specialties. It is an innovative, and rapidly growing technology to produce custom anatomical models and medical conditions models for medical teaching, surgical planning, and patient education. This study aimed to evaluate the accuracy and feasibility of 3D printing in creating a superficial femoral artery pseudoaneurysm model based on CT scans for endovascular training.

METHODS

A case of a left superficial femoral artery pseudoaneurysm was selected, and the 3D model was created using DICOM files imported into Materialise Mimics 22.0 and Materialise 3-Matic software, then printed using vat polymerization technology. Two 3D-printed models were created, and a series of comparisons were conducted between the 3D segmented images from CT scans and these two 3D-printed models. Ten comparisons involving internal diameters and angles of the specific anatomical location were measured.

RESULTS

The study found that the absolute mean difference in diameter between the 3D segmented images and the 3D printed models was 0.179±0.145 mm and 0.216±0.143mm, respectively, with no significant difference between the two sets of models. Additionally, the absolute mean difference in angle was 0.99±0.65° and 1.00±0.91°, respectively, and the absolute mean difference in angle between the two sets of data was not significant. Bland-Altman analysis confirmed a high correlation in dimension measurements between the 3D-printed models and segmented images. Furthermore, the accuracy of a 3D-printed femoral pseudoaneurysm model was further tested through the simulation of a superficial femoral artery pseudoaneurysm coiling procedure using the Philips Azurion7 in the angiography room.

CONCLUSIONS

3D printing is a reliable technique for producing a high accuracy 3D anatomical model that closely resemble a patient's anatomy based on CT images. Additionally, 3D printing is a feasible and viable option for use in endovascular training and medical education. In general, 3D printing is an encouraging technology with diverse possibilities in medicine, including surgical planning, medical education, and medical device advancement.

Multimodality Imaging for 3D Printing and Surgical Rehearsal in Complex Spine Surgery

Surgery is the mainstay treatment of symptomatic spinal tumors. It aids in restoring functionality, managing pain and tumor growth, and improving overall quality of life. Over the past decade, advancements in medical imaging techniques combined with the use of three-dimensional (3D) printing technology have enabled improvements in the surgical management of spine tumors by significantly increasing the precision, accuracy, and safety of the surgical procedures. For complex spine surgical cases, the use of multimodality imaging is necessary to fully visualize the extent of disease, including both soft-tissue and bone involvement. Integrating the information provided by these examinations in a cohesive manner to facilitate surgical planning can be challenging, particularly when multiple surgical specialties work in concert. The digital 3-dimensional (3D) model or 3D rendering and the 3D printed model created from imaging examinations such as CT and MRI not only facilitate surgical planning but also allow the placement of virtual and physical surgical or osteotomy planes, further enhancing surgical planning and rehearsal. The authors provide practical information about the 3D printing workflow, from image acquisition to postprocessing of a 3D printed model, as well as optimal material selection and incorporation of quality management systems, to help surgeons utilize 3D printing for surgical planning. The authors also highlight the process of surgical rehearsal, how to prescribe digital osteotomy planes, and integration with intraoperative surgical navigation systems through a case-based discussion. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Medical 3D Printing Using Desktop Inverted Vat Photopolymerization: Background, Clinical Applications, and Challenges

Medical 3D printing is a complex, highly interdisciplinary, and revolutionary technology that is positively transforming the care of patients. The technology is being increasingly adopted at the Point of Care (PoC) as a consequence of the strong value offered to medical practitioners. One of the key technologies within the medical 3D printing portfolio enabling this transition is desktop inverted Vat Photopolymerization (VP) owing to its accessibility, high quality, and versatility of materials. Several reports in the peer-reviewed literature have detailed the medical impact of 3D printing technologies as a whole. This review focuses on the multitude of clinical applications of desktop inverted VP 3D printing which have grown substantially in the last decade. The principles, advantages, and challenges of this technology are reviewed from a medical standpoint. This review serves as a primer for the continually growing exciting applications of desktop-inverted VP 3D printing in healthcare.

3D Printing in a hospital: Centralized clinical implementation and applications for comprehensive care

This educational article discusses the use of 3D printing or additive manufacturing in hospitals, not just for rapid prototyping but also for creating end-use products, such as clinical, diagnostic, and educational tools. The flexibility of 3D printing is valuable for creating patient-specific medical devices, custom surgical tools, anatomical models, implants, research tools and on-demand parts, among others. The advantages of and requirements for implementing a clinical 3D printing service in a hospital environment are discussed, including centralized 3D printing management, technology, example use cases, and considerations for implementation. The article provides an overview for other institutions to reference in setting up or organizing their clinical 3D printing services and is applicable to general hospitals or various sub-specialty practices.

Implementation of an In-House 3D Manufacturing Unit in a Public Hospital’s Radiology Department

Objective: Three-dimensional printing has become a leading manufacturing technique in healthcare in recent years. Doubts in published studies regarding the methodological rigor and cost-effectiveness and stricter regulations have stopped the transfer of this technology in many healthcare organizations. The aim of this study was the evaluation and implementation of a 3D printing technology service in a radiology department. Methods: This work describes a methodology to implement a 3D printing service in a radiology department of a Spanish public hospital, considering leadership, training, workflow, clinical integration, quality processes and usability. Results: The results correspond to a 6-year period, during which we performed up to 352 cases, requested by 85 different clinicians. The training, quality control and processes required for the scaled implementation of an in-house 3D printing service are also reported. Conclusions: Despite the maturity of the technology and its impact on the clinic, it is necessary to establish new workflows to correctly implement them into the strategy of the health organization, adjusting it to the needs of clinicians and to their specific resources. Significance: This work allows hospitals to bridge the gap between research and 3D printing, setting up its transfer to clinical practice and using implementation methodology for decision support.

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.