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Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): guidelines for medical 3D printing and appropriateness for clinical scenarios

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Contributed equally
3D Printing in Medicine20184:11

https://doi.org/10.1186/s41205-018-0030-y

  • Received: 7 August 2018
  • Accepted: 19 September 2018
  • Published:

Abstract

Medical three-dimensional (3D) printing has expanded dramatically over the past three decades with growth in both facility adoption and the variety of medical applications. Consideration for each step required to create accurate 3D printed models from medical imaging data impacts patient care and management. In this paper, a writing group representing the Radiological Society of North America Special Interest Group on 3D Printing (SIG) provides recommendations that have been vetted and voted on by the SIG active membership. This body of work includes appropriate clinical use of anatomic models 3D printed for diagnostic use in the care of patients with specific medical conditions. The recommendations provide guidance for approaches and tools in medical 3D printing, from image acquisition, segmentation of the desired anatomy intended for 3D printing, creation of a 3D-printable model, and post-processing of 3D printed anatomic models for patient care.

Keywords

  • 3D printing
  • Appropriateness
  • Guideline
  • Quality
  • Radiology
  • Additive manufacturing
  • Anatomic model

Background

In 2016, the Radiological Society of North America (RSNA) approved a proposal to create the Special Interest Group on 3D Printing (SIG). This document fulfills two of the original SIG goals: to provide recommendations towards consistent and safe production of 3D printed models derived from medical images, and to describe a set of clinical scenarios for 3D printing is appropriate for the intended use of caring for patients with those medical conditions. This project also fills a previously unmet need for practice parameters/guidelines regarding the clinical service of anatomic modeling (3D Printing) described for proposed new billing codes, including those for the American Medical Association. These practice parameters and recommendations are not intended as comprehensive standards but do reflect several salient aspects of clinical anatomic modeling and appropriateness. The guidelines subcommittee of the SIG will maintain and devote the time and effort necessary to continually develop and update these recommendations. This subcommittee is comprised of volunteer members of the SIG who form the writing group of this document.

In its current state, medical 3D printing [1576] has been performed for a variety of patients, but without evidence-based appropriateness guidelines. For many body parts, this document includes a comprehensive assessment of appropriateness from the medical literature, supplemented by expert opinion (SIG members) when there is a paucity of peer-review data. After the clinical decision to use 3D printing for patient care, there are many subsequent steps, as reviewed in prior literature [563, 566, 577]. These include image acquisition, image segmentation (demarcation of the desired 3D anatomy), creating 3D-printable file types for each segmented part, printing, and post processing of 3D medical models. This document differs from existing works, including case reports, small and larger studies, and 3D printing review articles in the literature. This is not a review article; instead of reviewing the literature or providing data regarding the clinical utility of medical 3D printing, the RSNA SIG has assembled a group of experts to begin to provide consensus recommendations on the practice of medical modeling and 3D printing, particularly for practice within healthcare facilities. 3D printing of anatomical models within a hospital has recently become recognized as point-of-care manufacturing. These recommendations create a foundational outline to provide practice recommendations for those steps required for medical 3D printing, including image acquisition, segmentation, printing, post-processing, and model verification.

Methods

Consensus methodology recommendations

The recommendations regarding medical image acquisition, image data preparation and manipulation, generation of 3D printed models, quality control, communication with referring physicians, preoperative planning using 3D printed models, and considerations regarding materials were discussed and summarized by members of the RSNA Special Interest Group for 3D Printing during several meetings, including on August 31 (Silver Spring, MD) and December 1, 2017 (Chicago, IL) after review of the relevant medical 3D printing literature [1576] and the local clinical practice of representative members of the Special Interest Group. Relevant recommendations were further exposed to internal online discussion and summarized by a focused taskforce. The final recommendations were reviewed and vetted by all RSNA 3D printing SIG members.

Appropriateness consensus guideline generation

The Special Interest Group has initiated the quality and safety scholarship to identify those clinical situations for which 3D Printing is considered an appropriate, and not appropriate, representation of the data contained in a medical imaging examination. These documents highlight appropriateness of medical 3D printing for clinical utilization, research, scientific, and informational purposes. This work is loosely modeled after the American College of Radiology Appropriateness Criteria® [553, 554] in that the guidelines committee uses an evidence-based approach at scoring. Consensus among members is used when there is a paucity of evidence.

Each category was led by a separate writing group, composed of a small group of experts in that domain of medical imaging and 3D printing. The SIG Executive committee, led by the Guidelines Chairperson, formed the review panel. Ratings were generated via by a vote of Special Interest Group members at in-person meetings. The results of the ratings follow the following 1–9 format (with 9 being the most appropriate):
  • 1–3, red, rarely appropriate: There is a lack of a clear benefit or experience that shows an advantage over usual practice.

  • 4–6, yellow, maybe appropriate: There may be times when there is an advantage, but the data is lacking, or the benefits have not been fully defined.

  • 7–9, green, usually appropriate: Data and experience shows an advantage to 3D printing as a method to represent and/or extend the value of data contained in the medical imaging examination.

The supporting evidence was obtained through structured PubMed searches, as detailed in the Appendix. In rare circumstances, supporting literature was recommended directly by the members of the committee and was explicitly identified outside of the structured PubMed search results.

A subset of applications of 3D printing, including in congenital heart, vascular, craniomaxillofacial, musculoskeletal, genitounirary, and breast pathologies was selected for detailed review. All final components of this section were vetted and approved by vote of Special Interest Group members at several face-to-face meetings including on August 31 (Silver Spring, MD) and December 1, 2017 (Chicago, IL) as well as via internal posting on the SIG member intranet.

Results

Consensus methodology recommendations

Medical image acquisition

The most common medical imaging modalities for 3D printing are computed tomography (CT) and magnetic resonance imaging (MRI); however, any 3D imaging dataset including sonography (e.g., echocardiography) may be utilized as input data for segmentation. The international standard format for these imaging files is Digital Imaging and Communications in Medicine (DICOM). At this time, DICOM images are not routinely sent directly to a 3D printer for printing, so medical images are segmented and converted to a file type that is recognized by 3D printers. Common file types include Standard Tessellation Language (STL), OBJ, VRML/WRL, AMF, 3MF, and X3D. Once this functionality is implemented by 3D printing vendors, picture archiving and communication system (PACS) vendors, and at the point of care facility, it will allow 3D files in the form of STLs, for example, to be stored in a patient’s medical record.

Spatial resolution and slice thickness

Medical imaging data should have sufficient spatial resolution to accurately represent the anatomy to be modeled. The spatial resolution of an imaging method refers to the smallest resolvable distance between two different objects or two different features of the same object. Low spatial resolution techniques will be unable to differentiate two adjacent structures that are close together and have similar tissue properties. When the intent to produce a 3D model is known prior to a medical imaging procedure, the image acquisition should be tailored so that the anatomy in the intended 3D model can be adequately visualized. The optimal spatial resolution will depend on the anatomy being imaged.

Slice thickness, which influences the spatial resolution and image noise (discussed in the next section), can also be optimized depending on the intended use. In general, this means that the smallest anatomy of interest should be captured on multiple sequential DICOM images of a particular series. For example, if the anatomy of interest measures 3 mm, it would be desirable for this anatomy to be captured on at least 3 sequential image slices; therefore, the slice thickness should be no greater than 1 mm, and preferably smaller. If images are acquired with a large slice thickness, stair-step boundaries may be seen in the 3D model.

For CT, in combination with scan distance, consideration may be given regarding collimation (the thickness of the X-ray beam) and overlap. Typically, the scan distance and collimation are the same; however, if the slice distance is smaller than the collimation, there will be an overlap which may lead to improved results. Cone Beam CT has technical differences with conventional CT, and often results in a lower patient radiation exposure and subsequently less image contrast that typical clinical CT images. Image artifacts and consistency of SNR throughout the scan can also limit studies. For MRI, voxels may be isotropic or rectangular solids and the resolution may be different in the three dimensions. The size of the voxel depends on the matrix size, the field of view (FOV), and the slice thickness.

In some clinical scenarios, there are patients for which suboptimal imaging data is available, but a separate acquisition is contraindicated. If superior spatial resolution is preferred and CT data is required, that benefit should be weighed against the risk of delivering more radiation to the patient.

Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR)

The SNR is a metric of image quality. A higher SNR, all else being equal, implies more trustworthy data for 3D printing. The CNR is the relationship of the signal intensity differences (the contrast) between two regions, scaled to noise. High contrast between various organs in the body is an important feature of medical imaging and is necessary to delineate structures for 3D printing. The SNR and CNR of images used for 3D printing should be comparable to, or superior to, those for “3D visualization”, defined as the comprehensive ensemble of manipulation of a volumetric data set for viewing on a 2D surface such as a computer monitor [563].

If the SNR and/or CNR are inconsistent, or suboptimal, the risks of inaccurate segmentation must be weighed against those of rescanning the patient. Regarding high noise data, a judgment call must be made to determine whether the segmentation operator is capable of delineating the data (e.g. in the case of a cone beam CT image series).

In CT, the X-ray tube voltage may also be adjusted to maximize the signal. A lower kV can be used to increase the enhancement of iodine contrast when building vascular models. In addition, the raw data reconstruction parameters selected may affect the appearance of specific anatomical structures. For example, the reconstruction kernel (image filter) impacts both the spatial resolution and image noise, which must be balanced, based on the application. Typically, kernel options range from “sharp” to “smooth.” Sharpening filters increase edge sharpness at a cost of increasing noise while smoothing filters reduce noise content in images by also decrease edge sharpness. For models with fine structures, such as the temporal bone, a sharp kernel is preferred; and for larger, low contrast models, a smooth kernel is more appropriate. CT is considered the imaging modality of choice for bone imaging and is often used to produce 3D anatomical models of hard tissue structures such as bone. In MRI, the SNR may be improved by performing a volume acquisition (at the expense of time), decreasing noise by reducing the bandwidth, altering the echo time or repeat time, increasing the FOV, decreasing the matrix size, or increasing the slice thickness.

Image artifact

The sub-volume of the imaging dataset that will be 3D printed is defined in this document as the printing Region of Interest (ROI). All medical images contain artifact, and image processing steps should be taken to minimize artifact. The ROI should be small enough to enable confident segmentation for 3D printing. There are cases for which medical interpretation is possible (see Image interpretation Section), but 3D printing can be limited by the presence of artifact, motion, or other spatial or noise limitations in DICOM images. When this is the case, we recommend that the model be annotated with documentation of those parts of the ROI where segmentation quality may be limited.

Image interpretation

Medical images acquired for a clinical indication should be interpreted with the interpretation being incorporated into the patient medical record. The interpretation should include the ROI being considered for printing. Often, interpretation of the ROI incorporates 3D visualization to enable or enhance diagnosis. Examples of 3D visualization include multi-planar reformatting, maximum intensity projections, and volume rendering. Such interpretations are currently billable in the United States under CPT codes 76376 and 76377.

Image data preparation and manipulation

Image segmentation

Image segmentation is necessary to create 3D printable files from medical images. The segmentation process, which subdivides medical images into anatomical regions, typically begins by importing a set of DICOM images into dedicated image post-processing software. Anatomical regions are selected using a combination of automated and semi-automated tools. Once the desired ROI for 3D printing has been selected, data is interpolated and a surface-based 3D model which describes the 3D geometry of that volume is calculated. To date, the most common, widely used, and accepted file format for medical 3D printed objects is the STL file.

STL files are composed of triangular faces, and the number of these faces can affect anatomical accuracy of a model. Each lab should determine the appropriate number of faces/triangles for their medical models to adequately represent anatomy. Operators should be aware of any reduction, smoothing, or further file manipulation or optimization within the segmentation software when creating and exporting the STL file.

The contours of the STL file should be routinely checked against the source medical imaging data; typical segmentation software packages allow the final STL to be re-imported and its contours displayed over the original DICOM images. This option can be used to verify the surface accuracy of an anatomical model STL file. Additional file formats noted above should also meet the same criteria.

Segmentation and Computer Aided Design (CAD) software

Medical image processing software is required to generate a file format amenable to 3D printing. The RSNA 3D printing SIG concurs with the FDA that software that has been favorably evaluated by the FDA be used to translate medical images into formats amenable to 3D printing for all aspects of patient care, defined by the SIG as all interactions with healthcare professionals, or patients and their families, related to medical care. The SIG recommends that software used for segmentation is FDA cleared to produce 3D Printed models suitable for diagnostic use, specifically using the FDA definition of diagnostic use and noting that FDA cleared software for 3D printed models will also include machines and materials validated for this intended use. At the time of manuscript submission, the FDA has approved one complete system, consisting of software through the printing process, for medical model production.

File storage and descriptors

Files stored within a repository should contain or be linked to a set of corresponding descriptors, including those pertaining to image acquisition and further imaging processing. Descriptors should be supported by standardized terminology from a consensus vocabulary; the SIG acknowledges that this vocabulary represents a current, unmet need. If the descriptors are not within digital files, this information should be otherwise archived.

Reference to file manipulation and alteration

Data from medical images undergo alterations in the design of the physical model. These changes have been categorized into Minor and Major alterations [578], with the latter generally representing changes that could impact clinical care. When modifications include major changes, the operator should verify that both the digital file and 3D printed model is labeled/identified appropriately.

Generation of 3D printed model

3D printing

There are many different 3D printing technologies, each differing in the way that the final 3D printed model is created. When 3D printed models are generated from medical images, the resolution of the 3D printer should be equal to, or superior to that of the clinical images used to segment the model. Similar to the DICOM acquisition stage, it is preferable that printed layers be a multiplier of the smallest geometry of interest. For example, if the smallest anatomical object of interest on the 3D model is 1 mm, this object should be printed on at least 3 layers of the model. Due to the nature of medical models, and the need for sub-millimeter accuracy, a layer thickness of no more than one-third of a millimeter is recommended, and preferably less than or equal to one-eighth of a millimeter. In addition to the layer thickness of the 3D printing hardware, the in-plane (x-y) resolution should be known, with a target of less than one-quarter of a millimeter. The values above are global recommendations may not be applicable in all cases. If a model requires a higher or lower accuracy, these parameters should be modified accordingly.

The medical model should include a patient identifier or an internal unique identification number that can be tracked back to the patient and date of the image acquisition. Labels can be incorporated (3D printed) into the model itself. Labels should be externally attached to the model if size or location does not allow for printed labeling. Printed models are assumed to be of anatomic size (1:1) unless a scaling factor is otherwise noted. Additional identifiers such as model sidedness (left, right) should be noted, as appropriate. Institutional guidelines should be used to verify models are free of protected health information, or models are handled appropriately in accordance to Health Insurance Portability and Accountability Act (HIPAA) guidelines.

Post-processing printed models

Post-processing steps should not alter the intended morphology and desired accuracy of the part, but instead should only enhance the utility (including clarity and transparency) and/or durability of the model. It should be noted that finishing may slightly alter the dimensional accuracy of a part, but this variation should be minimal (or within the desired global accuracy of the part) and the benefits (for example: strength and clarity) should outweigh the dimensional change. All support materials and residual manufacturing materials and/or substances should be removed as completely as possible. If all supporting material is not capable of being removed, this should be noted and presented to the requesting provider. Should the model be damaged either during or after post-processing and cleaning, repairs should be performed in a manner that reconstitutes the quality to which the original model adhered. If these repairs are not possible, the model should be reprinted. Any damage should be noted to the provider and the option to reprint should be presented. Cleaning solution concentration and saturation levels should be monitored and maintained in accordance to manufacture recommendations.

Model inspection

The model should be inspected by the 3D printing laboratory before clinical use. For cases where the model may be limited by a known image artifact, the model will be noted with any areas of concern. Qualitative and/or quantitative measures to confirm that the 3D printed model matches the desired input data will be taken, including but not limited to expert subjective assessment and objective fitting to the original volume submitted for printing. This can be done on a per part basis, per build basis, or in accordance with an additional internal protocol of the 3D lab. Some examples of qualitative assessments could include comparing the model to a digital representation or printed picture of the model and inspecting the model for printing imperfections or inaccuracies. Some examples of quantitative inspections could include measurements of a test specimen, measurements of the model, or scanning and comparing the model back to the original DICOM data sets.

The U.S. Food and Drug Administration (FDA)

The U.S. Food and Drug Administration (FDA) ensures the safety and efficacy of personalized devices in the United States of America. 3D Printing falls under the auspices of The Center for Devices and Radiological Health (CDRH). There have been four FDA benchmarks related to 3D printing and medical devices from 2014 to 2018.

First, in October 2014, the FDA held a public workshop entitled “Additive Manufacturing of Medical Devices: An Interactive Discussion on the Technical Considerations of 3D Printing”. Second, the FDA published “Additively Manufactured Medical Products – The FDA Perspective” [579]. Third, in December 2017, the FDA published “Technical Considerations for Additive Manufactured Devices” [580]. This perspective included insights regarding 3D printing data manipulation and hardware for modeling patient-specific anatomy. Fourth, the FDA commented on the publication “Maintaining Safety and Efficacy for 3D Printing in Medicine” [578]. This paper uses a similar, logical 3-step format of these consensus recommendations, and then develops different suggestions for regulatory models that depend on how much, if at all, the anatomical data is modified before 3D printing. On August 31st, 2017, the RSNA SIG and the FDA engaged in a joint meeting to discuss 3D printed anatomic models. The intended output of this meeting is a co-published white paper that will form the next benchmark.

Quality control program

Due to environmental factors and material properties, model morphology is expected to change over time. As part of a complete quality control program, 3D printers should undergo regular accuracy testing, including test prints, preventative maintenance, and recalibration [581, 582]. Laboratories may develop a process using a phantom to ensure regular quality standards for their printers. If the reference standard is known or assumed, mathematical operations [583] can be applied equally to those volumes in the ROI to determine the overall accuracy of the model, including not only potential manual errors from segmentation, but also generation of the final data set including digital post-processing steps such as smoothing.

Delivery and discussion with referring physicians

3D printed models represent an advanced form of communication of the data in medical images, and may include the summation of data from multiple sources. Extensive multidisciplinary teaching opportunities for 3D printing have been realized [584586]. Physicians should have an opportunity to discuss the salient features and intended use of all models. Any concerns about the model or segmentation process, if not discussed previously, should be noted to the provider at the time of delivery. Where possible, annotations detailing critical points of model anatomy should be stored both within the digital record of the model, and physically placed on the 3D printed model. One example is annotation of a subtle fracture that may not otherwise be represented in either or both, the segmented, or the 3D printed model.

Pre-operative planning

“Pre-operative planning” with 3D printing refers to virtual surgical planning (also called digital templating, digital surgical planning, virtual planning, computerized planning, computer-assisted surgical simulation). This detailed planning of the intervention occurs in the digital space. There are times when the simulation itself is the end product, and the interventionist acquires valuable information regarding patient anatomy and medical devices to be used to increase confidence and knowledge before surgery. For these cases the digital plan is transferred to patient care by way of 3D printed templates, guides, or models. This type of planning usually involves major changes to the digital model while utilizing original patient contours. This necessitates the systematic application of the 3D printing recommendations outlined above to the models used for virtual surgical planning as a minimum requirement.

Material biocompatibility, cleaning, and sterilization

For anatomical models and surgical guides/templates/jigs potentially entering a surgical field, material biocompatibility, cleaning, and sterilization are vitally important. The details are beyond the scope of this document. However, biocompatibility of materials depends on several factors including base material, the 3D printing process (and any variations), any post-processing techniques, and hospital cleaning and sterilization methods and requirements. Manufacturers should provide cleaning recommendations and specifications for materials which have been formally tested for biocompatibility and sterility, and these specifications should be followed by the facility. Additional internal sterilization policies may exist depending on the hospital.

Appropriateness of 3D printing (anatomic modeling) for selected clinical scenarios

This section provides evidence-based guidelines, supplemented by expert opinion when there is a paucity of peer-review data, to define and support the use of 3D printing for patients with a variety of conditions, including congenital heart, vascular, craniomaxillofacial, musculoskeletal, genitounirary, and breast pathologies (Table 1).
Table 1

Ratings Summary: Appropriateness Guidelines (scoring system defined in Methods) for patients who present with a variety of medical conditions, and for whom 3D Printing is often considered

Discussion

Reviews that include the types of 3D printers commonly used in medicine have been published [563, 584]. Regarding image post-processing and software, several tutorials are available for step-by-step training. The following discussion includes the specific descriptions from the SIG writing group for each clinical group of clinical scenarios considered for appropriateness.

Congenital heart disease

Congenital heart diseases (CHD) are the most common significant birth defects. Substantial literature supports the benefit of 3D printing for patients with congenital heart disease [17]. Regarding improved outcomes, precise preoperative understanding of the complex anatomy from a printed model may obviate or shorten lengthy exploration, and therefore operation and cardiopulmonary bypass time can be reduced.

These recommendations utilize and conform to the CHD nomenclature defined by the European Association for Cardio-Thoracic Surgery / Society of Thoracic Surgery (EACTS-STS) version of the International Pediatric and Congenital Cardiac Code (IPCCC), except as where noted otherwise. The clinical scenarios defined by the IPCCC include the following: Septal Defects, Pulmonary Venous Anomalies, Cor Triatriatum, Pulmonary Venous Stenosis, Right Heart Lesions, Left Heart Lesions, Single Ventricle, Transposition of the Great Arteries, DORV, DOLV.

Structured searches were performed using the US National Library of Medicine (PubMed), which enabled the querying and retrieval of appropriate clinical documents supporting the appropriateness of 3D printing-enabled technologies for each specific diagnosis. The search results were reviewed by experts and some references were removed and some were relocated to different categories. As noted above, references outside of the structured searches were added but noted and approved by the writing group. As a general rule, the benefits of 3D printing to define and rehearse an intervention increase with the overall degree of complexity of disease.

Craniomaxillofacial pathologies

The International Classification of Diseases, Tenth Revision (ICD-10) [555] descriptions and categorization were used to categorize the clinical scenarios for rating craniomaxillofacial conditions. Four major groups were used as the starting point; 1) Craniomaxillofacial Trauma, 2) Congenital Malformations, 3) Acquired/Developmental Deformities and 4) Neoplasms. Further sub-groups were formed underneath the major groupings. Additional clarification for “simple” versus “complex” diagnoses within a particular group was given based on inherent differences in appropriateness ratings between subgroups of patients in these groups. Further language describing each diagnostic grouping helps describe the difference between a simple and a complex case in each subcategory.

Structured searches were performed using the US National Library of Medicine (PubMed), which enabled the querying and retrieval of appropriate clinical documents supporting the appropriateness of 3D printing-enabled technologies for a specific condition. The search results were reviewed by experts and some references were removed because they were not relevant. A small number of references were added because they were found to be relevant, but not appearing using the stated search string. As noted above, these were vetted by the writing group before inclusion. Clinical scenarios that were only dental or only brain have not been included. The authors recognize that these include many important clinical scenarios of for 3D printing, and the goal is to include them in upcoming documents.

Craniomaxillofacial (CMF) conditions for the purposes of this document encompass several different surgical specialties all working in the head and neck area with both pediatric and adult patients. These include oral and maxillofacial surgery, craniofacial surgery, plastic surgery, microvascular surgery, pediatric neurosurgery and otolaryngology. Use of 3D printing-enabled technologies to aid clinical care in the craniomaxillofacial area has been seen from the very advent of 3D printing in the late 1980s [556, 557]. Even before the commercialization of stereolithography there were surgeons, engineers and researchers figuring out more manual ways of converting medical imaging datasets into 3D models [558]. The fit seems clear, CMF surgery has both a functional component, and for most cases an aesthetic component, where the form carries importance along with the functional restoration. In the CMF arena, the use of anatomical models of anatomy is primarily derived from CT and MRI datasets, and also from an increasing volume of cone beam CT datasets. Patient-specific anatomical models are the baseline, but for many of these applications the value of these technologies has been found in either, a) patient-matched implants (for instance temporomandibular joint reconstruction), or b) virtual surgery combined with templates and guides (for instance orthognathic surgery). The scenarios to follow were thought of in this way, with some of them relying heavily on anatomical models alone and some of them relying with increasing importance on the role that digital planning combined with patient-matched implants or templating is playing.

Genitourinary pathologies

The genitourinary conditions have been organized anatomically, recognizing that common genitourinary interventions are largely based on anatomic considerations. The complication rate after major genitourinary surgeries is reflected in the complexity of the lesion. For example, more complex kidney tumors are associated with longer operative times, warm ischemia times, and greater blood loss [559]. High kidney tumor complexity can also be correlated to the risk of major postoperative complications requiring a secondary intervention [560].

There is a growing body of literature that supports the benefits to patients from 3D printed models. Specifically, 3D printed models may improve comprehension of anatomy and facilitate pre-surgical planning for complex surgical cases, ultimately reducing operation times and improving patient outcomes.

This document describes and provides rating for the clinical scenarios related to 3D printing of genitourinary pathology [561, 562]. Structured searches were performed using the US National Library of Medicine (PubMed), which enabled the querying and retrieval of appropriated clinical documents supporting the appropriateness of 3D printing for a specific diagnosis. As a general rule, the benefits of 3D printing to define and rehearse a genitourinary intervention increases with the overall degree of complexity of the pathology that is represented by the physical model based on a medical imaging study performed in a radiology department.

Musculoskeletal pathologies

The role of 3D printed models in addressing musculoskeletal pathologies can vary depending on a specific clinical scenario, ranging from aiding in informed consent to use in preoperative planning. Custom fixation plates, surgical osteotomy guides and implants can also be generated from 3D data, allowing for virtual surgery and design of a custom implant that is modeled after the contralateral healthy side. In addition, mock surgeries can be performed on the physical 3D models, allowing for more intuitive problem solving and measurements preoperatively. Such planning alters surgical management for some patients, either by delaying intervention, or by suggesting an alternative approach. Pre-surgical planning can also decrease operating room time and the number of devices and tools that need to be tried and subsequently wasted and/or re-sterilized. In this sense, 3D printing has proven useful for demonstrating musculoskeletal pathology and for planning interventions.

Based on the accumulating evidence, the use of 3D printed models can positively impact numerous metrics associated with musculoskeletal interventions, including patient and physician satisfaction, operative time, blood loss, and the various direct and indirect costs associated with patient-centered decision making regarding management of complex disease. At present, the musculoskeletal pathologies with potential and established 3D printing-enabled management have been broadly categorized into fractures, chronic osseous abnormalities, degenerative disorders, neoplastic pathologies, scoliosis, and miscellaneous specific applications including ligamentous injury and heterotopic ossification.

Vascular pathologies

3D printing has been shown to be useful for understanding the vascular anatomy, evaluation of hemodynamics, treatment planning (surgical and endovascular) as well as preclinical testing of devices. It has also been used for medical education and procedural training on vascular models [563566]. There are several clinical scenarios for which 3D printing has been used in the care of patients with vascular disease. Because of the nature of vascular pathology, dissection, aneurysm, and stenosis are often treated with medical management and “watchful waiting”; most patients follow this algorithm, and there is little to no role for 3D printing. However, some patients have a clinical presentation and non-invasive tests that warrant intervention, while others progress from watchful waiting to planned intervention. For many of these patients, 3D printing is appropriate. Of note, coronary 3D printing, and cardiac printing in general falls outside the scope of this document. These clinical scenarios will be discussed in future documents.

Most aortic dissections are treated medically, and for these patients there is no indication for 3D printing. However, 3D printing may be appropriate for planning intervention in complex dissections, and in particular dissections that also have enlargement. Models have been used for planning and simulation of stent deployment [495]. Simulation on models can help in identifying the best projections for angiography, best catheter and wire combinations to navigate the anatomy, in for determining appropriate balloon and stent size as well as position.

Endovascular repair of complex aortic aneurysm involving the origin of branches, extreme angulations, complex neck anatomy, and short landing zones can be quite challenging. Use of 3D printed models can aid understanding of complex anatomy, device selection, and design of prosthesis best suited for patient’s anatomy. These models have shown to be useful in planning procedures and increase operator confidence [491]. 3D printed models have also been used to precisely place fenestrations on stent grafts to treat complex aneurysms [479, 567]. In addition, graft replicas can be tested on patient specific 3D model for suitability before being deployed in patients.

Aortic surgeries, especially in the region of aortic arch and upper abdominal aorta can be quite challenging due to origin of branches, angulation and complex aneurysm neck anatomy. 3D printed models have shown to improve surgeons’ understanding of anatomy and help preoperative planning [485]. Further, 3D printed models can potentially also be used to plan and simulate surgical and endovascular interventions on visceral aneurysms [502, 503]. These models can also be used for designing [486] and testing [568, 576] endovascular devices like catheters, coils, balloons, and stents.

Breast pathologies

Breast cancer is the most common solid malignancy in women in the United States [570]. The overall lifetime risk of developing breast cancer for women in the United States is 12.4%. Advancements in diagnostic tests and treatments have led to decreasing death rates of 1.8% per year from 2005 to 2014 [570, 571]. Understanding the extent of disease at the time of diagnosis allows appropriate staging and determination of prognosis and survival, in addition to selection of suitable surgical options [572]. Benefits from 3D printed models and its role as an aid to clinical care has been increasingly described in the literature. 3D printed models have the ability of depicting the extent of disease and relationships of sensitive anatomy, thereby possibly reducing operating time, enhancing utilization of new oncoplastic techniques, and improving patient outcomes.

Benign breast diseases are common and include a wide range of entities [573]. The most common of these entities, fibrocystic change, is clinically observed in up to 50% of women and found histologically in 90% of women [573]. Fibroadenomas are the next most common benign breast disease occurring in 15–23% of women [574]. Surgical management of these entities may be needed in cases where cosmesis is altered or when symptom relief is needed. Surgical management may impact developing breast tissue in young women leading to alterations in its proper development [575]. Therefore, careful understanding of the anatomy may minimize the deleterious effects of surgery in benign breast disease.

Conclusions

3D printing will play an increasingly important role in enabling precision medicine. This document addresses the clinical scenarios where pathology complexity necessitates a transformation of clinical imaging data into a physical model. Adoption of common clinical standards regarding appropriate use, information and material management, and quality control are needed to ensure the greatest possible clinical benefit from3D printing.

This work provides the first comprehensive literature-based guideline document regarding the implementation of 3D printing in clinical practice and details the appropriate scenarios for numerous clinical applications of 3D printing. It is anticipated that this consensus guideline document, created by the members of the RSNA 3D printing special group, will provide the initial reference for method and clinical application standardization. The document and will be substantially expanded and refined, based on expanding clinical applications.

Notes

Abbreviations

3D: 

Three-dimensional

ACR: 

American College of Radiology

ASD: 

Atrial septal defect

AV: 

Atrioventricular

CAD: 

Computer aided design

CDRH: 

The center for devices and radiological health

CHD: 

Congenital heart disease

CMF: 

Craniomaxillofacial

CNR: 

Contrast to noise ratio

CT: 

Computed tomography

DICOM: 

Digital imaging and communications in medicine

DOLV: 

Double-outlet left ventricle

DORV: 

Double-outlet right ventricle

EACTS-STS: 

European Association for Cardio-Thoracic Surgery / Society of Thoracic Surgery

FDA: 

The United States Food and Drug Administration

FOV: 

Field of view

HIPAA: 

Health Insurance Portability and Accountability Act

ICD-10: 

International Classification of Diseases, Tenth Revision

IPCCC: 

International Pediatric and Congenital Cardiac Code

MRI: 

Magnetic resonance imaging

NOS: 

Not otherwise specified

PACS: 

Picture archiving and communication system

PAPVR: 

Partial anomalous pulmonary venous return

ROI: 

Region of interest

RSNA: 

Radiological Society of North America

RVOT: 

Right ventricular outflow tract

SIG: 

Special Interest Group

SNR: 

Signal to noise ratio

STL: 

Standard tessellation language

TAPVR: 

Total anomalous pulmonary venous return

TGA: 

Transposition of the great arteries

VRML: 

Virtual reality markup language

VSD: 

Ventricular septal defect

Declarations

Acknowledgements

RSNA SIG Faculty Members (July 15, 2018)

Abraham Levitin, MD, Beachwood, OH, United States

Adam C. Zoga, MD, Dept of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, United States

Alejandro A. Espinoza, PhD, Dept of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, United States

Alexander J. Chien, MD, Chino Hills, CA, United States

Amar B. Shah, MD, New York, NY, United States

Ambroise Mathurin Dzogang Temdemno, MD, CARIM, Yaounde, Cameroon

Amin S. Chaoui, MD, Wellesley, MA, United States

Amy E. Alexander, MS, Dept of Radiology, Mayo Clinic, Rochester, MN, United States

Anand V. Rao, MD, Brookfield, WI, United States

Anne Garcia, Opheart, Houston, TX, United States

Angel R Colon, MD, Mayaguez, PR, United States

Antoine Leimgruber, MD, MS, Pully, VD, Switzerland

Antoine M. Vanderhofstadt, MD, Brussels, Belgium

Asra Khan-Bonenberger, MD, Orlando, FL, United States

Attilio A. Guazzoni, MD, Dept of Radiology, San Biagio Hospital, Domodossola, VB, Italy

Barbara L. McComb, MD, Ponte Vedra, FL, United States

Benjamin E. Tubb, MD, PhD, San Antonio, TX, United States

Benjamin Johnson, 3DSystems, Littleton, CO, United States

Benjamin M. Howe, MD, Dept of Radiology, Mayo Clinic, Rochester, MN, United States

Berdoudi Rabah, MD, Dept of Radiology, Imagerie Medicale du Charollais, Paray-le-Monial, France

Bernadette M. Greenwood, BS, RT, Desert Medical Imaging, Indian Wells, CA, United States

Beth A. Ripley, MD, PhD, Dept of Radiology, University of Washington, Seattle, WA, United States

Beth M. Kline-Fath, MD, Dept of Radiology (MLC 5031), Children’s Hospital Medical Center, Cincinnati, OH, United States

Brent Chanin, BEng, Mediprint.us, Chester, NY, United States

Brian A. Tweddale, MD, Doylestown, PA, United States

Brian McNamee, MD, Coeur D Alene, ID, United States

Bruce M. Barack, MD, Los Angeles, CA, United States

Bruce M. Shuckett, MD, Toronto, ON, Canada

Bryan Crutchfield, Materialise, Plymouth, MI, United States

Carina L. Butler, MD, Lexington, KY, United States

Carlin A. Ridpath, MD, Springfield, MO, United States

Carlos I. Hernandez Rojas, MD, Lima 27, Peru

Carlos Torres, MD, Ottawa, ON, Canada

Carolina A. Souza, MD, Ottawa, ON, Canada

Chen C. Hoffmann, MD, Dept of Diagnostic Radiology, Ramat-Gan, Israel

Cheryl L. Kirby, MD, Cherry Hill, NJ, United States

Ching-Lan Wu, MD, Dept of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan

Chris Letrong, RT, ARRT, San Jose, CA, United States

Christina Kotsarini, MD, PhD, Glasgow, United Kingdom

Christine J. Kim, MD, Dept of Neuroradiology, Brigham and Women’s Hospital, Boston, MA, United States

Christopher A. Swingle, DO, Saint Louis, MO, United States

Christopher E. Smith, MD, Rch Palos Vrd, CA, United States

Christopher Wilke, MD, Dept of Radiation Oncology, Univ of Minnesota School of Medicine, Minneapolis, MN, United States

Christopher Yurko, MD, Vallejo, CA, United States

Claudio Silva, MD, Radiology Department, Clinica Alemana, Facultad de Medicina Clinica Alemana Universidad del Desarrollo, Santiago, Chile

Colin M. Wilson, MA, Dept of Radiology, University of New Mexico, Albuquerque, NM, United States

Craig S. Howard, MD, Hattiesburg, MS, United States

Damodaran Arul Selvam, MD, Dept of Radiology, Malcolm Randall VA Medical Center, Gainesville, FL, United States

Dana A. Fuller, MD, Dallas, TX, United States

Daniel A. Crawford, MSc, BSc, Dept of Medical 3D Printing, Axial3D, Belfast, Antrim, United Kingdom

Daniel Davis, RT, BS, Denver, CO, United States

Daniel LaRussa, PhD, Department of Radiology, The University of Ottawa Faculty of Medicine, Ottawa, ON, Canada

Daniel S. Madsen, MD, Dept of Interventional Radiology, San Antonio Military Med Ctr, Fort Sam Houston, TX, United States

Daniele Marin, MD, Cary, NC, United States

Darshit Thakrar, MD, Dept of Pediatric Radiology, Childrens Hospital of Pittsburgh of UPMC, Pittsburgh, PA, United States

Dave Nuthals, Vital Images, Minnetonka, MN, United States

David Dreizin, MD, Dept of Radiology & Nuclear Medicine, University of Maryland Medical Center, Baltimore, MD, United States

David M. Hough, MD, Rochester, MN, United States

David MacCutcheon, TeraRecon, Foster City, MA, United States

Daya Vora, MD, Troy, MI, United States

Deborah E. Starkey, RT, Medical Radiation Sciences, Queensland University of Technology, Brisbane, QLD, Australia

Denis Samama, MD, Service d’Imagerie Medicale, Neuilly-sur-Seine, France

Derek L. West, MD, Dept of Radiology, Emory University, Atlanta, GA, United States

Diane M. Twickler, MD, Dept of Radiology, Univ of Texas Southwestern Medical Ctr, Dallas, TX, United States

Donald S. Emerson, MD, Memphis, TN, United States

Dong Xu, MD, PhD, Dept of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China

Dorothy J. Shum, MD, Dept of Radiology, University of California San Francisco, San Francisco, CA, United States

Eddy D. Lucas, MD, Wichita, KS, United States

Eduardo M. Rosa, MD, Radiologica Los Volcanes, Puerto Montt, Decima Region, Chile

Edward A. Del Grosso, MD, Granville, OH, United States

Edward P. Quigley, III, MD, PhD, Salt Lake City, UT, United States

Edward Stefanowicz, MBA, RT, Dept of Radiology, Geisinger Health System, Danville, PA, United States

Enrique R. Escobar, MD, Melilla, Spain

Eric M. Baumel, MD, Digital Imaging Diagnostics PLC, Wellington, FL, United States

Eric Teil, MD, Tresserve, France

Erik W. Stromeyer, MD, Miami Beach, FL, United States

Ernest J. Ferris, MD, Little Rock, AR, United States

Fabrizio D’Alessandro, MD, Massa, Ron, Italy

Fadi Toonsi, MBBS, FRCPC, Jeddah, Saudi Arabia

Faisal M. Shah, MD, Scotch Plains, NJ, United States

Fernando A. Alvarado Sr, MD, Dept of Radiology, Diagnos, Machala, El Oro, Ecuador

Francesco Potito, MD, Dept of MRI CT, Centro Radiologico Potito, Campobasso, CB, Italy

Frank S. Bonelli, MD, PhD, Rockford, IL, United States

Freddy Drews, MD, Avon Lake, OH, United States

Gaetano T. Pastena, MD, MBA, Glenmont, NY, United States

Gary W. Kerber, MD, Urbana, IL, United States

Gene Kitamura, MD, Dept of Radiology, UPMC, Pittsburgh, PA, United States

George Antaki, MD, Riverview, FL, United States

Georgina A. Viyella, MD, Santo Domingo, Dominican Republic

Gerard P. Farrar, MD, Hemlock, MI, United States

Gloria M. Rapoport, MD, Forest Hills, NY, United States

Gul Moonis, MD, South Orange, NJ, United States

H. Henry Guo, MD, Fremont, CA, United States

Halemane S. Ganesh, MD, Lexington, KY, United States

Han N. Ta, MD, Newport Coast, CA, United States

Haraldur Bjarnason, MD, Dept of Vascular & Interventional Radiology, Mayo Clinic, Rochester, MN, United States

Hemant T. Patel, MD, Samved Hospital, Ahmedabad, India

Hongju Son, MD, Dept of Radiology, Einstein Healthcare Network, Philadelphia, PA, United States

Hui J. Chen, MD, San Francisco, CA, United States

Hyun-Ju Lee, MD, PhD, Dept of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of

Irini M. Youssef, MD, MPH, Hollidaysburg, PA, United States

Jack M. Drew, MD, Littleton, CO, United States

Jaime Ribeiro Barbosa, MD, Instituto de Radiologia Pres Prudente, Presidente Prudente, SP, Brazil

James B. Allison, MD, Richmond, VT, United States

James Shin, MD, MSc, New York, NY, United States

Jared V. Grice, DMP, Nashville, TN, United States

Jaroslaw Ast, MD, Poznan, Wielkopolska, Poland

Jayanthi Parthasarathy BDS, PhD, Nationwide Children’s Hospital, Columbus, OH, United States

Jeffrey A. Haithcock, MD, Colleyville, TX, United States

Jeffrey A. Sodergren, MD, Mountain Top, PA, United States

Jeffrey D. Hirsch, MD, Lutherville, MD, United States

Jesus D. Buonomo, MD, Gurabo, PR, United States

Joaquim M. Farinhas, MD, Tampa, FL, United States

Joel M. Stein, MD, PhD, Div of Neuroradiology, Hospital of the University of Pennsylvania, Philadelphia, PA, United States

Johannes Goerich, MD, Eberbach, Baden, Germany

John A. Skinner, MD, Dept of Radiology, Mayo Clinic, Rochester, MN, United States

John G. O’Rourke, MBBS, Sydney, NSW, Australia

John Oh, MD, Las Vegas, NV, United States

John P. Knoedler Jr., MD, North Oaks, MN, United States

Jonathan A. Aziza, MD, Thornhill, ON, Canada

Jonathan M. Ford, PhD, Dept of Radiology, University of South Florida College of Medicine, Tampa, FL, United States

Jorge E. Salazar, MD, UT Medical Group Inc., Memphis, TN, United States

Jose A. Barriocanal, MD, PhD, Chattanooga, TN, United States

Jose A. Maldonado, MD, San Juan, PR, United States

Joseph Johnnie, MS, BEng, Medivators, Conroe, TX, United States

Joseph M. Aulino, MD, Brentwood, TN, United States

Josephine Pressacco, MD, PhD, Dept of Diagnostic Radiology (D5–113), MUHC/Montreal General Hospital, Montreal, QC, Canada

Judy H. Song, MD, Medstar Georgetown University Hospital, Washington, DC, United States

Juergen Brandt, MD, Arnsberg, Germany

Julie S. Lee, MD, Seattle, WA, United States

Juling Ong I, MBBS, Dept of Plastic Surgery/3D Facility, Great Ormond Street Hospital for Children, London, United Kingdom

Justin Sutherland, PhD, Department of Radiology, The University of Ottawa Faculty of Medicine, Ottawa, ON, Canada

Karen K. Moeller, MD, Louisville, KY, United States

Katherine Weimer, 3D Systems - Healthcare, Littleton, CO, United States

Kathleen G. Oxner, MD, Greenville, SC, United States

Kathryn E. Pflug, MD, Dept of Radiology, Lakeview Regional Medical Center, Covington, LA, United States

Kelly D. Smith, MD, Mitchell, SD, United States

Kelly Oppe, RT, Dept of Radiology, Carle Foundation Hospital, Urbana, IL, United States

Kenneth A. Buckwalter, MD, Indiana University, Indianapolis, IN, United States

Kenneth L. Sandock, MD, Tucson, AZ, United States

Kent R. Thielen, MD, Department of Radiology, Mayo Clinic, Rochester, MN, United States

Kevin A. Lugo, MBA, ARRT, Raleigh, NC, United States

Kevin J. Roche, MD, New Hope, PA, United States

Kevin L. Pope, MD, Breast Center of Northwest Arkansas, Fayetteville, AR, United States

Keyur Mehta, MD, Montefiore Medical Center, Bronx, NY, United States

Kimberly Torluemke, 3D Systems, Healthcare, Littleton, CO, United States

Kirby K. Wong, MBBS, MPH, Sydney, NSW, Australia

Klaus Kubin, MD, CT/MR Institutes, Medical Center Schallmoos, Salzburg, Austria

Kranthi K. Kolli, PhD, MS, New York, NY, United States

Kristi B. Oatis, MD, Lexington, KY, United States

Kwok-chung Lai, MBChB, FRCR, Dept of Radiology & Imaging, Queen Elizabeth Hospital, Kowloon, Hong Kong

Lance E. Reinsmith, MD, San Antonio, TX, United States

Lauralyn McDaniel, MBA, SME, Dearborn, MI, United States

Leizle E. Talangbayan, MD, Long Branch, NJ, United States

Leszek J. Jaszczak, MD, Williston, ND, United States

Ligia Cardona, MD, Santo Domingo, Distrito Nacional, Dominican Republic

Lincoln Wong, MD, Omaha, NE, United States

Liza Nellyta, MD, Department of Radiology, RS Awal Bros Pekanbaru, Pekanbaru, Riau, Indonesia

Louis T. Kircos, DDS, PhD, San Rafael, CA, United States

Luc Lacoursiere, MD, FRCPC, Quebec, QC, Canada

Luca Remonda, MD, Aarau, Aargau, Switzerland

Lucas M. Sheldon, MD, Niceville, FL, United States

Luigi Grazioli, MD, Servizio di Radiologia, University of Brescia/Spedali Civili Brescia, Brescia, BS, Italy

Luis A. Campos, MD, Lima 33, Lima, Peru

Luis A. Rodriguez Palomares, MD, Delegacion: Benito Juarez, Mexico City, Mexico

Mamdouh E. Rayan, MD, MSc, Chicago, IL, United States

Marc J. Gollub, MD, New York, NY, United States

Margaret O. Brown, MD, Walton, KY, United States

Mariah N. Geritano, MSc, Brookline, MA, United States

Mariam Thomas, MD, Los Angeles, CA, United States

Mariano Sturla, MD, Castelar, Buenos Aires, Argentina

Mark A. Smith, MS, ARRT, Columbus, OH, United States

Mark D. Alson, MD, Fresno, CA, United States

Mark E. Sharafinski Jr., MD, Madison, WI, United States

Marshall B. Hay, MD, Portage, MI, United States

Mary Ellen Wickum, MS, Cambridge, MA, United States

Mary Hu, MD, MS, Flushing, NY, United States

Mary L. Christie, Rockland, MA, United States

Mashael K. Alrujaib, FRCR, FRCPC, Dept of Radiology (MBC-28), King Faisal Specialist Hospital, Riyadh, Central Region, Saudi Arabia

Matthew Allen, MD, Redding Cancer Treatment Center, Redding, CA, United States

Mayola C. Boykin, MD, Ashland, KY, United States

Melanie Gillies, BSc, Coolangatta, QLD, Australia

Michael D. Maloney, MD, Yreka, CA, United States

Michael Gaisford, Stratasys, Cambridge, MA, United States

Michael L. Richardson, MD, Dept of Radiology, University of Washington, Seattle, WA, United States

Michael T. McGuire, MD, Jersey City, NJ, United States

Michael T. Miller, MD, Pittsford, NY, United States

Michael W. Itagaki, MD, MBA, Bellevue, WA, United States

Michel Berube, MD, Chicoutimi, QC, Canada

Michel D. Dumas, MD, Abilene, TX, United States

Michelle L. Walker, MS, Clearwater, FL, United States

Mohammad Eghtedari, MD, PhD, San Diego, CA, United States

Muge Ozhabes, MD, Marina Del Rey, CA, United States

Nathaniel Reichek, MD, Fort Salonga, NY, United States

Naveen K. Gowda, MD, Dept of Radiology, St. Lukes Hospital, Duluth, MN, United States

Nicholas C. Fraley, MD, Oro Valley, AZ, United States

Nicholas G. Rhodes, MD, Rochester, MN, United States

Nopporn Beokhaimook, MD, Nonthaburi, Thailand

Pamela A. Rowntree, RT, Medical Radiation Sciences, Queensland University of Technology, Brisbane, Qld, Australia

Pascal Fontaine, DVM, MSc, Montreal, QC, Canada

Patricia A. Rhyner, MD, Atlanta, GA, United States

Patrick Chang, MD, Dept of Radiology, Kaiser South San Francisco Medical Ctr, San Francisco, CA, United States

Paul E. Lizotte, DO, MA, Valley Center, CA, United States

Paulo M. Bernardes, MD, Rio de Janeiro, RJ, Brazil

Pedro E. Diaz, MD, Guaynabo, PR, United States

Pen-An Liao, MD, Taipei City, Taiwan

Perla M. Salgado, MD, Cuernavaca, Morelos, Mexico

Peter M. Van Ooijen, MSc, PhD, Dept of Radiology, University Medical Center of Groningen, Groningen, Netherlands

Peter Piechocniski, Memorial Sloan Kettering Cancer Center, New York City, NY, United States

Philip S. Lim, MD, Dept of Radiology, Abington Memorial Hospital, Abington, PA, United States

Philipp Brantner, MD, Binningen, Switzerland

Philippe Grouwels, MD, Hasselt, Belgium

Phillip D. Baker, MD, PhD, Dept of Radiology, Legacy Good Samaritan Hospital, Portland, OR, United States

Prasad S. Dalvie, MD, Dept of Radiology, University of Wisconsin, Madison, WI, United States

Qurashi M. Ali Fadlelseed, MD, PhD, National College for Med & Technical Studies, Kitarfoum, Sudan

R. Scott Rader, PhD, GE Healthcare, Marlborough, MA, United States

Rajaram E. Reddy, MD, St Catherines, ON, Canada

Rami M. Shorti, PhD, Intermountain Healthcare, South Jordan, UT, United States

Ramin Javan, MD, Washington, DC, United States

Randolph K. Otto, MD, Edmonds, WA, United States

Raphael J. Alcuri, MD, Whitesboro, NY, United States

Rasim C. Oz, MD, Baltimore, MD, United States

Richard A. Levy, MD, Saugerties, NY, United States

Richard E. Barlow, MD, Sandy Springs, GA, United States

Richard K. Brown, MD, Dept of Nuclear Medicine, University of Michigan, Ann Arbor, MI, United States

Richard Shoenfeld, MD, Mountain Lks, NJ, United States

Rikesh J. Makanji, MD, Tampa, FL, United States

Robert A. Posniak, MD, Windermere, FL, United States

Robert L. Falk, MD, Prospect, KY, United States

Robert M. DeWitt, MD, APO, AE, United States

Robert S. Redlich, MD, Hudson, OH, United States

Robyn A. Pugash, MD, Dept of Medical Imaging, Sunnybrook HSC, Toronto, ON, Canada

Roy G. Bryan Jr., MD, MBA, Radiology, Santa Barbara Cottage Hospital, Santa Barbara, CA, United States

Salim S. Merchant, FRANZCR, Melbourne, VIC, Australia

Sang Joon Park, PhD, Seoul National University Hospital, Seoul, Korea, Republic of

Sang-Sun Han, MD, Dept of Oral & Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea, Republic of

Sanjay M. Mallya, DDS, PhD, Dept of Oral & Maxillofacial Radiology, UCLA School of Dentistry, Los Angeles, CA, United States

Sanjay P. Prabhu, MBBS, FRCR, Dept of Pediatric Neuroradiology, Childrens Hospital Boston, Boston, MA, United States

Sankar P. Sinha, MBBS, FRCR, Nuneaton, Warwickshire, United Kingdom

Sanket Chauhan, MD, Dept of Surgery, Baylor University Medical Center, Dallas, TX, United States

Satinder S. Rekhi Jr., MD, Manorville, NY, United States

Scott H. Faro, MD, Haddonfield, NJ, United States

Scott T. Williams, MD, San Juan Capo, CA, United States

Sepideh Sefidbakht, MD, Powel, OH, United States

Sergio A. Gonzalez, MD, El Paso, TX, United States

Seth J. Berkowitz, MD, Brookline, MA, United States

Shannon N. Zingula, MD, Dept of Radiology, Mayo Clinic, Rochester, MN, United States

Shannon R. Kirk, MD, Loma Linda University, Loma Linda, CA, United States

Sharon W. Gould, MD, Kemblesville, PA, United States

Shuai Leng, PhD, Dept of Radiology, Mayo Clinic, Rochester, MN, United States

Sidney D. Machefsky, MD, University Cy, MO, United States

Sofiane Derrouis, MD, Neuchatel, Switzerland

Srini Malini, MD, Womens Specialists of Houston at TCH, Texas Childrens Hospital Pavilion for Women, Houston, TX, United States

Stephane Khazoom, MD, Chambly, QC, Canada

Stephen E. Russek, PhD, NIST, Boulder, CO, United States

Steven C. Horii, MD, Dept of Radiology, University of Pennsylvania Medical Center, Philadelphia, PA, United States

Steven R. Parmett, MD, Teaneck, NJ, United States

Sumit Pruthi, MBBS, Dept of Radiology, Vanderbilt Childrens Hospital, Nashville, TN, United States

Summer J. Decker, PhD, Dept of Radiology, University of South Florida College of Medicine, Tampa, FL, United States

Tan M. Nguyen, MD, Dept of Radiology, Sacramento, CA, United States

Terence J. O’Loughlin, MD, Provincetown, MA, United States

Terry C. Lynch, MD, Dept of Radiology, San Francisco General Hospital, San Francisco, CA, United States

Timothy L. Auran, MD, San Luis Obispo, CA, United States

Todd Goldstein, PhD, Northwell Health, USA, NY, United States

Todd Pietila, Materialise, Plymouth, MI, United States

Tone Lindgren, MD, Pelham, NY, United States

Tracy S. Chen, DO, MPH, Carmel, CA, United States

Vartan M. Malian, MD, Roseville, CA, United States

Vicente Gilsanz, MD, PhD, Dept of Radiology, Childrens Hospital Los Angeles, Los Angeles, CA, United States

Victor A. McCoy, MD, Prairieville, LA, United States

Vijay Jayaram, MBBS, PhD, Enfield, Middlesex, United Kingdom

Vinicius V. Alves, Niteroi, RJ, Brazil

W. Brian Hyslop, MD, PhD, Dept of Radiology, University of North Carolina, Chapel Hill, NC, United States

Wael M. Abdalla, MD, Presby South Tower, Henderson, NV, United States

Walter A. Carpenter, MD, PhD, Atlanta, GA, United States

Wellington Eddy Reynaldo Paez Zumarraga, MD, Quito, Ecuador

William D. Boswell Jr., MD, Dept of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States

William Prows, Intermountain Healthcare, Murray, UT, United States

Xing-Jun Gao, MD, Department of Radiology, Xinyang Central Hospital, Xinyang, Henan, China

Yeong Shyan Lee, MBBCh, Department of Diagnostic Radiology (Basement 1), Singapore, Singapore

Yiwen Chen, PhD, Dept of 3D Printing Med Research, China Medical University Hospital, Taichung City, Taiwan

Yoshimi Anzai, MD, Dept of Radiology, University of Utah, Salt Lake Cty, UT, United States

Zheng Jin, MS, New York, NY, United States

Trainee Members (July 15, 2018)

Adrian A. Negreros-Osuna, MD, Massachusetts General Hospital, Boston, MA, United States

Andreas Giannopoulos, MD, University Hospital Zurich, Zurich, Switzerland

Andres Vasquez, MD, MSc, New York, NY, United States

Boris Kumaev, DO, University of Florida, Jacksonville, FL, United States

Carissa M. White, MD, Venice, CA, United States

Eduardo Hernandez-Rangel, MD, University of California, Santa Ana, CA, United States

Elias Kikano, MD, Mayfield Heights, OH, United States

Elisa Spoldi, DVM, University of Florida College of Veterinary Med, Gainesville, FL, United States

Jessica D. Shand Smith, MBChB, Edinburgh, United Kingdom

Justin Kerby, II, MD, MS, Wichita, KS, United States

Kirk P. Langheinz, MD, Lafayette General Medical Center - Cancer Center of Acadiana, Lafayette, LA, United States

Luis G. Ricardez, MD, Hospital Civil de Culiacan, Culiacan, Sinaloa, Mexico

Michael Bartellas, MS, St Johns, NL, Canada

Narayana Vamyanmane Dhananjaya Kotebagilu, MBBS, MBA, Abhayahasta Multispeciality Hospital, Bengaluru, Karnataka, India

Sadia R. Qamar, MBBS, Vancouver General Hospital, UBC, Vancouver, BC, Canada

Sherazad Islam, MD, Glenview, IL, United States

Vasanthakumar Venugopal, MD, New Delhi, Delhi, India

Vjekoslav Kopacin, MD, Osijek, Croatia

Yu-hui Huang, MS, Chicago, IL, United States

Affiliated Contributors (non-members of the Special Interest Group)

Jeffrey P Jacobs, MD, Division of Cardiovascular Surgery and Director of the Andrews/Daicoff Cardiovascular Program, Johns Hopkins All Children’s Heart Institute, St Petersburg, FL

Kenneth E. Salyer, MD, World Craniofacial Foundation, Dallas, TX, US and Texas A&M University, Dallas, TX, US

R. Bryan Bell, MD, DDS, Providence Head and Neck Cancer Program, Providence Cancer Institute, Portland, OR, US and Head & Neck Surgical Associates, Portland, OR, US

Authors’ contributions

All primary authors edited, reviewed, and approved this manuscript. All included special interest group member coauthors, listed in the Acknowledgements section, were provided with the final manuscript for review and approved its publication.

Competing interests

The primary authors declare no competing interests. RSNA Special Interest Group for 3D Printing includes a variety of industry representatives with voting privileges, including representatives from Materialise Inc., Stratasys, 3D Systems, and TeraRecon, as detailed in the Acknowledgements section.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Radiology and The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
(2)
Center for Advanced Imaging Innovation and Research (CAI2R), Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, USA
(3)
Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, USA
(4)
Rady Children’s Hospital, San Diego, CA, USA
(5)
Department of Diagnostic Radiology, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
(6)
Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
(7)
Baltimore VA Medical Center, University of Maryland Medical Center, Baltimore, MD, USA
(8)
Department of Radiology and Frankel Cardiovascular Center, University of Michigan, Ann Arbor, MI, USA
(9)
Department of Neurosurgery, State University of New York Buffalo, Buffalo, NY, USA
(10)
Department of Radiology, Mayo Clinic, Rochester, MN, USA
(11)
3D Medical Applications Center, Walter Reed National Military Medical Center, Washington, DC, USA

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