Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): guidelines for medical 3D printing and appropriateness for clinical scenarios
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.
Keywords3D printing Appropriateness Guideline Quality Radiology Additive manufacturing Anatomic model
American College of Radiology
Atrial septal defect
Computer aided design
The center for devices and radiological health
Congenital heart disease
Contrast to noise ratio
Digital imaging and communications in medicine
Double-outlet left ventricle
Double-outlet right ventricle
European Association for Cardio-Thoracic Surgery / Society of Thoracic Surgery
The United States Food and Drug Administration
Field of view
Health Insurance Portability and Accountability Act
International Classification of Diseases, Tenth Revision
International Pediatric and Congenital Cardiac Code
Magnetic resonance imaging
Not otherwise specified
Picture archiving and communication system
Partial anomalous pulmonary venous return
Region of interest
Radiological Society of North America
Right ventricular outflow tract
Special Interest Group
Signal to noise ratio
Standard tessellation language
Total anomalous pulmonary venous return
Transposition of the great arteries
Virtual reality markup language
Ventricular septal defect
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 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576] 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.
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 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576] 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.
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.
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 .
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.
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.
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 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 , 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
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.
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” . Third, in December 2017, the FDA published “Technical Considerations for Additive Manufactured Devices” . 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” . 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  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 [584, 585, 586]. 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” 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
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 [1, 2, 3, 4, 5, 6, 7]. 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.
The International Classification of Diseases, Tenth Revision (ICD-10)  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 . 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.
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 . High kidney tumor complexity can also be correlated to the risk of major postoperative complications requiring a secondary intervention .
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.
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.
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 [563, 564, 565, 566]. 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 . 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 . 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 . 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  and testing [568, 576] endovascular devices like catheters, coils, balloons, and stents.
Breast cancer is the most common solid malignancy in women in the United States . 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 . 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 . The most common of these entities, fibrocystic change, is clinically observed in up to 50% of women and found histologically in 90% of women . Fibroadenomas are the next most common benign breast disease occurring in 15–23% of women . 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 . Therefore, careful understanding of the anatomy may minimize the deleterious effects of surgery in benign breast disease.
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.
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
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.
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 7.Yang F, Zheng H, Lyu J, Yang X, Yang Y, Pang Y, Liang F, Zhang G, Xu Z, Jiang S, Lyu B, Meng F, Hao B. A case of transcatheter closure of inferior vena cava type atrial septal defect with patent ductus arteriosus occlusion device guided by 3D printing technology. Zhonghua Xin Xue Guan Bing Za Zhi. 2015;43(7):631–3.PubMedGoogle Scholar
- 9.Bhatla P, Tretter JT, Ludomirsky A, Argilla M, Latson LA, Chakravarti S, Barker PC, Yoo S-J, McElhinney DB, Wake N, Mosca RS. Utility and scope of rapid prototyping in patients with complex muscular ventricular septal defects or double-outlet right ventricle: does it Alter Management decisions? Pediatr Cardiol. 2017;38(1):103–14.PubMedCrossRefGoogle Scholar
- 10.Farooqi KM, Uppu SC, Nguyen K, Srivastava S, Ko HH, Choueiter N, Wollstein A, Parness IA, Narula J, Sanz J, Nielsen JC. Application of virtual three-dimensional models for simultaneous visualization of Intracardiac anatomic relationships in double outlet right ventricle. Pediatr Cardiol. 2015;37(1):90–8.PubMedCrossRefGoogle Scholar
- 11.Garekar S, Bharati A, Chokhandre M, Mali S, Trivedi B, Changela VP, Solanki N, Gaikwad S, Agarwal V. Clinical application and multidisciplinary assessment of three dimensional printing in double outlet right ventricle with remote ventricular septal defect. World J Pediatr Congenit Heart Surg. 2016;7(3):344–50.PubMedCrossRefGoogle Scholar
- 17.Schievano S, Migliavacca F, Coats L, Khambadkone S, Carminati M, Wilson N, Deanfield JE, Bonhoeffer P, Taylor AM. Percutaneous pulmonary valve implantation based on rapid prototyping of right ventricular outflow tract and pulmonary trunk from MR data. Radiology. 2007;242(2):490–7.PubMedCrossRefGoogle Scholar
- 21.Farooqi KM, Nielsen JC, Uppu SC, Srivastava S, Parness IA, Sanz J, Nguyen K. Use of 3-dimensional printing to demonstrate complex intracardiac relationships in double-outlet right ventricle for surgical planning. Circ Cardiovasc Imaging. 2015;8(5):e003043. https://doi.org/10.1161/CIRCIMAGING.114.003043. CrossRefPubMedGoogle Scholar
- 22.Dydynski PB, Kiper C, Kozik D, Keller BB, Austin E, Holland B. Three-dimensional reconstruction of Intracardiac anatomy using CTA and surgical planning for double outlet right ventricle: early experience at a tertiary care congenital heart center. World J Pediatr Congenit Heart Surg. 2016;7(4):467–74. https://doi.org/10.1177/2150135116651399.CrossRefPubMedGoogle Scholar
- 23.Kappanayil M, Koneti NR, Kannan RR, Kottayil BP, Kumar K. Three-dimensional-printed cardiac prototypes aid surgical decision-making and preoperative planning in selected cases of complex congenital heart diseases: early experience and proof of concept in a resource-limited environment. Ann Pediatr Cardiol. 2017;10(2):117–25.PubMedPubMedCentralCrossRefGoogle Scholar
- 30.Mottl-Link S, Hübler M, Kühne T, Rietdorf U, Krueger JJ, Schnackenburg B, De Simone R, Berger F, Juraszek A, Meinzer HP, Karck M, Hetzer R, Wolf I. Physical models aiding in complex congenital heart surgery. Ann Thorac Surg. 2007;86:273–7. https://doi.org/10.1016/j.athoracsur.2007.06.001. CrossRefGoogle Scholar
- 73.Yamada H, Nakaoka K, Horiuchi T, Kumagai K, Ikawa T, Shigeta Y, Imamura E, Iino M, Ogawa T, Hamada Y. Mandibular reconstruction using custom-made titanium mesh tray and particulate cancellous bone and marrow harvested from bilateral posterior ilia. J Plast Surg Hand Surg. 2014;48(3):183–90.PubMedCrossRefGoogle Scholar
- 82.Yamada H, Nakaoka K, Sonoyama T, Kumagai K, Ikawa T, Shigeta Y, Harada N, Kawamura N, Ogawa T, Hamada Y. Clinical usefulness of mandibular reconstruction using custom-made titanium mesh tray and autogenous particulate cancellous bone and marrow harvested from tibia and/or Ilia. J Craniofac Surg. 2016;27(3):586–92.PubMedCrossRefGoogle Scholar
- 87.Ciocca L, Mazzoni S, Fantini M, Persiani F, Baldissara P, Marchetti C, Scotti R. A CAD/CAM-prototyped anatomical condylar prosthesis connected to a custom-made bone plate to support a fibula free flap. Med Biol Eng Comput. 2012;50(7):743–9. https://doi.org/10.1007/s11517-012-0898-4.CrossRefPubMedGoogle Scholar
- 93.Robiony M, Salvo I, Costa F, Zerman N, Bazzocchi M, Toso F, Bandera C, Filippi S, Felice M, Politi M. Virtual reality surgical planning for maxillofacial distraction osteogenesis: the role of reverse engineering rapid prototyping and cooperative work. J Oral Maxillofac Surg. 2007;65(6):1198–208.PubMedCrossRefGoogle Scholar
- 108.Ying B, Ye N, Jiang Y, Liu Y, Hu J, Zhu S. Correction of facial asymmetry associated with vertical maxillary excess and mandibular prognathism by combined orthognathic surgery and guiding templates and splints fabricated by rapid prototyping technique. Int J Oral Maxillofac Surg. 2015;44(11):1330–6.PubMedCrossRefGoogle Scholar
- 112.Fiaschi P, Pavanello M, Imperato A, Dallolio V, Accogli A, Capra V, Consales A, Cama A, Piatelli G. Surgical results of cranioplasty with a polymethylmethacrylate customized cranial implant in pediatric patients: a single-center experience. J Neurosurg Pediatr. 2016;17(6):705–10.PubMedCrossRefGoogle Scholar
- 115.Lee UL, Kwon JS, Woo SH, Choi YJ. Simultaneous Bimaxillary surgery and mandibular reconstruction with a 3-dimensional printed titanium implant fabricated by Electron beam melting: a preliminary mechanical testing of the printed mandible. J Oral Maxillofac Surg. 2016;74(7):1501.e1–1501.e15.CrossRefGoogle Scholar
- 130.Ritschl LM, Rau A, Güll FD, diBora B, Wolff KD, Schönberger M, Bauer FX, Wintermantel E, Loeffelbein DJ. Pitfalls and solutions in virtual design of nasoalveolar molding plates by using CAD/CAM technology--a preliminary clinical study. J Craniomaxillofac Surg. 2016;44(4):453–9.PubMedCrossRefGoogle Scholar
- 162.Ibrahim D, Broilo TL, Heitz C, de Oliveira MG, de Oliveira HW, Nobre SM, Dos Santos Filho JH, Silva DN. Dimensional error of selective laser sintering, three-dimensional printing and PolyJet models in the reproduction of mandibular anatomy. J Craniomaxillofac Surg. 2009;37(3):167–73.PubMedCrossRefGoogle Scholar
- 197.Stokbro K, Aagaard E, Torkov P, Bell RB, Thygesen T. Surgical accuracy of three-dimensional virtual planning: a pilot study of bimaxillary orthognathic procedures including maxillary segmentation. Int J Oral Maxillofac Surg. 2016;45(1):8–18. https://doi.org/10.1016/j.ijom.2015.07.010.CrossRefPubMedGoogle Scholar
- 202.Hou JS, Chen M, Pan CB, Tao Q, Wang JG, Wang C, Zhang B, Huang HZ. Immediate reconstruction of bilateral mandible defects: management based on computer-aided design/computer-aided manufacturing rapid prototyping technology in combination with vascularized fibular osteomyocutaneous flap. J Oral Maxillofac Surg. 2011;69(6):1792–7.PubMedCrossRefGoogle Scholar
- 205.Man QW, Jia J, Liu K, Chen G, Liu B. Secondary reconstruction for mandibular osteoradionecrosis defect with fibula osteomyocutaneous flap flowthrough from radial forearm flap using stereolithographic 3-dimensional printing modeling technology. J Craniofac Surg. 2015;26(2):e190–3.PubMedCrossRefGoogle Scholar
- 221.Zizelmann C, Bucher P, Rohner D, Gellrich NC, Kokemueller H, Hammer B. Virtual restoration of anatomic jaw relationship to obtain a precise 3D model for total joint prosthesis construction for treatment of TMJ ankylosis with open bite. Int J Oral Maxillofac Surg. 2010;39(10):1012–5.PubMedCrossRefGoogle Scholar
- 229.Gonçalves JR, Gomes LC, Vianna AP, Rodrigues DB, Gonçalves DA, Wolford LM. Airway space changes after maxillomandibular counterclockwise rotation and mandibular advancement with TMJ concepts total joint prostheses: three-dimensional assessment. Int J Oral Maxillofac Surg. 2013;42(8):1014–22.PubMedCrossRefGoogle Scholar
- 238.Tarsitano A, Battaglia S, Ramieri V, Cascone P, Ciocca L, Scotti R, Marchetti C. Short-term outcomes of mandibular reconstruction in oncological patients using a CAD/CAM prosthesis including a condyle supporting a fibular free flap. J Craniomaxillofac Surg. 2017;45(2):330–7.PubMedCrossRefGoogle Scholar
- 239.Ritschl LM, Mücke T, Fichter A, Güll FD, Schmid C, Duc JMP, Kesting MR, Wolff KD, Loeffelbein DJ. Functional outcome of CAD/CAM-assisted versus conventional microvascular, fibular free flap reconstruction of the mandible: a retrospective study of 30 cases. J Reconstr Microsurg. 2017;33(4):281–91.PubMedCrossRefGoogle Scholar
- 244.Ciocca L, Marchetti C, Mazzoni S, Baldissara P, Gatto MR, Cipriani R, Scotti R, Tarsitano A. Accuracy of fibular sectioning and insertion into a rapid-prototyped bone plate, for mandibular reconstruction using CAD-CAM technology. J Craniomaxillofac Surg. 2015;43(1):28–33.PubMedCrossRefGoogle Scholar
- 252.Wu CT, Lee ST, Chen JF, Lin KL, Yen SH. Computer-aided design for three-dimensional titanium mesh used for repairing skull base bone defect in pediatric neurofibromatosis type 1. A novel approach combining biomodeling and neuronavigation. Pediatr Neurosurg. 2008;44(2):133–9.PubMedCrossRefGoogle Scholar
- 257.Cheng HT, Wu CI, Tseng CS, Chen HC, Lee WS, Chen PK, Chang SC. The occlusion-adjusted prefabricated 3D mirror image templates by computer simulation: the image-guided navigation system application in difficult cases of head and neck reconstruction. Ann Plast Surg. 2009;63(5):517–21.PubMedCrossRefGoogle Scholar
- 258.Zhou LB, Shang HT, He LS, Bo B, Liu GC, Liu YP, Zhao JL. Accurate reconstruction of discontinuous mandible using a reverse engineering/computer-aided design/rapid prototyping technique: a preliminary clinical study. J Oral Maxillofac Surg. 2010;68(9):2115–21.PubMedCrossRefPubMedCentralGoogle Scholar
- 281.Azuma M, Yanagawa T, Ishibashi-Kanno N, Uchida F, Ito T, Yamagata K, Hasegawa S, Sasaki K, Adachi K, Tabuchi K, Sekido M, Bukawa H. Mandibular reconstruction using plates prebent to fit rapid prototyping 3-dimensional printing models ameliorates contour deformity. Head Face Med. 2014;10:45.PubMedPubMedCentralCrossRefGoogle Scholar
- 284.Kääriäinen M, Kuuskeri M, Gremoutis G, Kuokkanen H, Miettinen A, Laranne J. Utilization of three-dimensional computer-aided preoperative virtual planning and manufacturing in maxillary and mandibular reconstruction with a microvascular fibula flap. J Reconstr Microsurg. 2016;32(2):137–41.PubMedGoogle Scholar
- 289.Anchieta MV, Salles FA, Cassaro BD, Quaresma MM, Santos BF. Skull reconstruction after resection of bone tumors in a single surgical time by the association of the techniques of rapid prototyping and surgical navigation. Int J Comput Assist Radiol Surg. 2016;11(10):1919–25.PubMedCrossRefGoogle Scholar
- 297.Ayoub N, Ghassemi A, Rana M, Gerressen M, Riediger D, Hölzle F, Modabber A. Evaluation of computer-assisted mandibular reconstruction with vascularized iliac crest bone graft compared to conventional surgery: a randomized prospective clinical trial. Trials. 2014;15:114.PubMedPubMedCentralCrossRefGoogle Scholar
- 299.Sieira Gil R, Roig AM, Obispo CA, Morla A, Pagès CM, Perez JL. Surgical planning and microvascular reconstruction of the mandible with a fibular flap using computer-aided design, rapid prototype modelling, and precontoured titanium reconstruction plates: a prospective study. Br J Oral Maxillofac Surg. 2015;53(1):49–53.PubMedCrossRefGoogle Scholar
- 304.Atalay HA, Canat HL, Ülker V, Alkan İ, Özkuvanci Ü, Altunrende F. Impact of personalized three-dimensional -3D- printed pelvicalyceal system models on patient information in percutaneous nephrolithotripsy surgery: a pilot study. Int Braz J Urol. 2017;43:470–5. https://doi.org/10.1590/S1677-5538.IBJU.2016.0441.CrossRefPubMedPubMedCentralGoogle Scholar
- 305.Atalay HA, Ülker V, Alkan İ, Canat HL, Özkuvancı Ü, Altunrende F. Impact of three-dimensional printed Pelvicaliceal system models on residents’ understanding of Pelvicaliceal system anatomy before percutaneous Nephrolithotripsy surgery: a pilot study. J Endourol. 2016;30:1132–7. https://doi.org/10.1089/end.2016.0307.CrossRefPubMedGoogle Scholar
- 312.Golab A, Smektala T, Kaczmarek K, Stamirowski R, Hrab M, Slojewski M. Laparoscopic partial nephrectomy supported by training involving personalized silicone replica poured in three-dimensional printed casting Mold. J Laparoendosc Adv Surg Tech A. 2017;27:420–2. https://doi.org/10.1089/lap.2016.0596.CrossRefPubMedGoogle Scholar
- 313.Von Rundstedt F-C, Scovell JM, Agrawal S, Zaneveld J, Link RE. Utility of patient-specific silicone renal models for planning and rehearsal of complex tumour resections prior to robot-assisted laparoscopic partial nephrectomy. BJU Int. 2017;119:598–604. https://doi.org/10.1111/bju.13712.CrossRefPubMedGoogle Scholar
- 317.Komai Y, Sugimoto M, Gotohda N, Matsubara N, Kobayashi T, Sakai Y, Shiga Y, Saito N. Patient-specific 3-dimensional printed kidney designed for “4D” surgical navigation: a novel aid to facilitate minimally invasive off-clamp partial nephrectomy in complex tumor cases. Urology. 2016;91:226–33. https://doi.org/10.1016/j.urology.2015.11.060.CrossRefPubMedGoogle Scholar
- 318.Bernhard J-C, Isotani S, Matsugasumi T, Duddalwar V, Hung AJ, Suer E, Baco E, Satkunasivam R, Djaladat H, Metcalfe C, Hu B, Wong K, Park D, Nguyen M, Hwang D, Bazargani ST, de Castro Abreu AL, Aron M, Ukimura O, Gill IS. Personalized 3D printed model of kidney and tumor anatomy: a useful tool for patient education. World J Urol. 2016;34:337–45. https://doi.org/10.1007/s00345-015-1632-2.CrossRefPubMedGoogle Scholar
- 319.Knoedler M, Feibus AH, Lange A, Maddox MM, Ledet E, Thomas R, Silberstein JL. Individualized physical 3-dimensional kidney tumor models constructed from 3-dimensional printers result in improved trainee anatomic understanding. Urology. 2015;85:1257–61. https://doi.org/10.1016/j.urology.2015.02.053.CrossRefPubMedGoogle Scholar
- 326.Wendler JJ, Klink F, Seifert S, Fischbach F, Jandrig B, Porsch M, Pech M, Baumunk D, Ricke J, Schostak M, Liehr U-B. Irreversible electroporation of prostate Cancer: patient-specific pretreatment simulation by electric field measurement in a 3D bioprinted textured prostate Cancer model to achieve optimal electroporation parameters for image-guided focal ablation. Cardiovasc Intervent Radiol. 2016;39:1668–71. https://doi.org/10.1007/s00270-016-1390-6.CrossRefPubMedGoogle Scholar
- 329.Fitzgerald KA, Guo J, Tierney EG, Curtin CM, Malhotra M, Darcy R, O’Brien FJ, O’Driscoll CM. The use of collagen-based scaffolds to simulate prostate cancer bone metastases with potential for evaluating delivery of nanoparticulate gene therapeutics. Biomaterials. 2015;66:53–66. https://doi.org/10.1016/j.biomaterials.2015.07.019. CrossRefPubMedGoogle Scholar
- 334.Henry OY, Fragoso A, Beni V, Laboria N, Sánchez JLA, Latta D, Von Germar F, Drese K, Katakis I, O’Sullivan CK. Design and testing of a packaged microfluidic cell for the multiplexed electrochemical detection of cancer markers. Electrophoresis. 2009;30:3398–405. https://doi.org/10.1002/elps.200900368.CrossRefPubMedGoogle Scholar
- 335.Sayed Aluwee SAZB, Zhou X, Kato H, Makino H, Muramatsu C, Hara T, Matsuo M, Fujita H. Evaluation of pre-surgical models for uterine surgery by use of three-dimensional printing and mold casting. Radiol Phys Technol. 2017;10:279–85. https://doi.org/10.1007/s12194-017-0397-2.CrossRefPubMedGoogle Scholar
- 337.Kadoya N, Miyasaka Y, Nakajima Y, Kuroda Y, Ito K, Chiba M, Sato K, Dobashi S, Yamamoto T, Takahashi N, Kubozono M, Takeda K, Jingu K. Evaluation of deformable image registration between external beam radiotherapy and HDR brachytherapy for cervical cancer with a 3D-printed deformable pelvis phantom. Med Phys. 2017;44:1445–55. https://doi.org/10.1002/mp.12168.CrossRefPubMedGoogle Scholar
- 339.Lindegaard JC, Madsen ML, Traberg A, Meisner B, Nielsen SK, Tanderup K, Spejlborg H, Fokdal LU, Nørrevang O. Individualised 3D printed vaginal template for MRI guided brachytherapy in locally advanced cervical cancer. Radiother Oncol. 2016;118:173–5. https://doi.org/10.1016/j.radonc.2015.12.012.CrossRefPubMedGoogle Scholar
- 343.Sethi R, Cunha A, Mellis K, Siauw T, Diederich C, Pouliot J, Hsu I-C. Clinical applications of custom-made vaginal cylinders constructed using three-dimensional printing technology. J Contemp Brachytherapy. 2016;8:208–14. https://doi.org/10.5114/jcb.2016.60679. CrossRefPubMedPubMedCentralGoogle Scholar
- 352.Chen X, Chen X, Zhang G, Lin H, Yu Z, Wu C, Li X, Lin Y, Huang W. Accurate fixation of plates and screws for the treatment of acetabular fractures using 3D-printed guiding templates: an experimental study. Injury. 2017;48:1147–54. https://doi.org/10.1016/j.injury.2017.03.009.CrossRefPubMedGoogle Scholar
- 355.Chung KJ, Hong DY, Kim YT, Yang I, Park YW, Kim HN. Preshaping plates for minimally invasive fixation of calcaneal fractures using a real-size 3D-printed model as a preoperative and intraoperative tool. Foot Ankle Int. 2014;35:1231–6. https://doi.org/10.1177/1071100714544522.CrossRefPubMedGoogle Scholar
- 356.De Muinck Keizer RJO, Lechner KM, Mulders M a M, Schep NWL, Eygendaal D, Goslings JC. Three-dimensional virtual planning of corrective osteotomies of distal radius malunions: a systematic review and meta-analysis. Strategies Trauma Limb Reconstr. 2017;12:77–89. https://doi.org/10.1007/s11751-017-0284-8.CrossRefPubMedPubMedCentralGoogle Scholar
- 365.Hamada Y, Gotani H, Sasaki K, Tanaka Y, Egawa H, Kanchanathepsak T. Corrective osteotomy of Malunited Diaphyseal fractures of the forearm simplified using 3-dimensional CT data: proposal of our simple strategy through case presentation. Hand (N Y). 2017;12:NP95–8. https://doi.org/10.1177/1558944717692087.CrossRefGoogle Scholar
- 370.Huang H, Hsieh M-F, Zhang G, Ouyang H, Zeng C, Yan B, Xu J, Yang Y, Wu Z, Huang W. Improved accuracy of 3D-printed navigational template during complicated tibial plateau fracture surgery. Australas Phys Eng Sci Med. 2015;38:109–17. https://doi.org/10.1007/s13246-015-0330-0.CrossRefPubMedGoogle Scholar
- 377.Kataoka T, Oka K, Miyake J, Omori S, Tanaka H, Murase T. 3-dimensional prebent plate fixation in corrective osteotomy of malunited upper extremity fractures using a real-sized plastic bone model prepared by preoperative computer simulation. J Hand Surg Am. 2013;38:909–19. https://doi.org/10.1016/j.jhsa.2013.02.024.CrossRefPubMedPubMedCentralGoogle Scholar
- 380.Lazarus P, Pire E, Sapa C, Ruffenach L, Saur M, Liverneaux P, Hidalgo Diaz JJ. Design and evaluation of a new synthetic wrist procedural simulator (Wristsim®) for training of distal radius fracture fixation by volar plating. Hand Surg Rehabil. 2017;36:275–80. https://doi.org/10.1016/j.hansur.2017.03.002.CrossRefPubMedGoogle Scholar
- 381.Li B, Chen B, Zhang Y, Wang X, Wang F, Xia H, Yin Q. Comparative use of the computer-aided angiography and rapid prototyping technology versus conventional imaging in the management of the tile C pelvic fractures. Int Orthop. 2016;40:161–6. https://doi.org/10.1007/s00264-015-2800-0.CrossRefPubMedGoogle Scholar
- 393.Sanghavi PS, Jankharia BG. Holding versus seeing pathology. Three-dimensional printing of the bony pelvis for preoperative planning of a complex pelvis fracture: a case report. Indian J Radiol Imaging. 2016;26:397–401. https://doi.org/10.4103/0971-3026.190414.CrossRefPubMedPubMedCentralGoogle Scholar
- 399.Wu X-B, Wang J-Q, Zhao C-P, Sun X, Shi Y, Zhang Z-A, Li Y-N, Wang M-Y. Printed three-dimensional anatomic templates for virtual preoperative planning before reconstruction of old pelvic injuries: initial results. Chin Med J. 2015;128:477–82. https://doi.org/10.4103/0366-6999.151088.CrossRefPubMedPubMedCentralGoogle Scholar
- 403.You W, Liu LJ, Chen HX, Xiong JY, Wang DM, Huang JH, Ding JL, Wang DP. Application of 3D printing technology on the treatment of complex proximal humeral fractures (Neer3-part and 4-part) in old people. Orthop Traumatol Surg Res. 2016;102:897–903. https://doi.org/10.1016/j.otsr.2016.06.009.CrossRefPubMedGoogle Scholar
- 413.Zhang YZ, Chen B, Lu S, Yang Y, Zhao JM, Liu R, Li YB, Pei GX. Preliminary application of computer-assisted patient-specific acetabular navigational template for total hip arthroplasty in adult single development dysplasia of the hip. Int J Med Robot. 2011;7:469–74. https://doi.org/10.1002/rcs.423.CrossRefPubMedGoogle Scholar
- 414.Zheng P, Yao Q, Xu P, Wang L. Application of computer-aided design and 3D-printed navigation template in locking compression pediatric hip PlateΤΜ placement for pediatric hip disease. Int J Comput Assist Radiol Surg. 2017;12:865–71. https://doi.org/10.1007/s11548-017-1535-3.CrossRefPubMedGoogle Scholar
- 417.Bastian L, Hüfner T, Mössinger E, Geerling J, Goesling T, Busche M, Kendoff D, Bading S, Rosenthal H, Krettek C. Integration of modern technologies in therapy of sarcomas of the pelvis. Computer-assisted hemipelvectomy and implantation of a “custom-made” Bonit gentamycin coated partial pelvic prosthesis. Unfallchirurg. 2003;106:956–62. https://doi.org/10.1007/s00113-003-0680-z. CrossRefPubMedGoogle Scholar
- 422.Guenette JP, Himes N, Giannopoulos AA, Kelil T, Mitsouras D, Lee TC. Computer-based vertebral tumor Cryoablation planning and procedure simulation involving two cases using MRI-visible 3D printing and advanced visualization. AJR Am J Roentgenol. 2016;207:1128–31. https://doi.org/10.2214/AJR.16.16059.CrossRefPubMedPubMedCentralGoogle Scholar
- 426.Kim D, Lim JY, Shim KW, Han JW, Yi S, Yoon DH, Kim KN, Ha Y, Ji GY, Shin DA. Sacral reconstruction with a 3D-printed implant after Hemisacrectomy in a patient with sacral osteosarcoma: 1-year follow-up result. Yonsei Med J. 2017;58:453–7. https://doi.org/10.3349/ymj.2017.58.2.453.CrossRefPubMedPubMedCentralGoogle Scholar
- 427.Liang H, Ji T, Zhang Y, Wang Y, Guo W. Reconstruction with 3D-printed pelvic endoprostheses after resection of a pelvic tumour. Bone Joint J. 2017;99-B:267–75. https://doi.org/10.1302/0301-620X.99B2.BJJ-2016-0654.R1.CrossRefPubMedGoogle Scholar
- 429.Luo W, Huang L, Liu H, Qu W, Zhao X, Wang C, Li C, Yu T, Han Q, Wang J, Qin Y. Customized knee prosthesis in treatment of Giant cell tumors of the proximal tibia: application of 3-dimensional printing Technology in Surgical Design. Med Sci Monit. 2017;23:1691–700.PubMedPubMedCentralCrossRefGoogle Scholar
- 436.Tam MD, Laycock SD, Bell D, Chojnowski A. 3-D printout of a DICOM file to aid surgical planning in a 6 year old patient with a large scapular osteochondroma complicating congenital diaphyseal aclasia. J Radiol Case Rep. 2012;6:31–7. https://doi.org/10.3941/jrcr.v6i1.889.CrossRefPubMedPubMedCentralGoogle Scholar
- 447.Hafez MA, Chelule KL, Seedhom BB, Sherman KP. Computer-assisted total knee arthroplasty using patient-specific templating. Clin Orthop Relat Res. 2006;444:184–92. https://doi.org/10.1097/01.blo.0000201148.06454.ef. CrossRefPubMedGoogle Scholar
- 449.Kleyer A, Beyer L, Simon C, Stemmler F, Englbrecht M, Beyer C, Rech J, Manger B, Krönke G, Schett G, Hueber AJ. Development of three-dimensional prints of arthritic joints for supporting patients’ awareness to structural damage. Arthritis Res Ther. 2017;19:34. https://doi.org/10.1186/s13075-017-1234-z.CrossRefPubMedPubMedCentralGoogle Scholar
- 452.Taller S, Srám J, Lukáš R, Endrych L, Džupa V. Fixation of acetabular fractures. A novel method of pre-operative omega plate contouring. Acta Chir Orthop Traumatol Cechoslov. 2014;81:212–20.Google Scholar
- 457.Mao K, Wang Y, Xiao S, Liu Z, Zhang Y, Zhang X, Wang Z, Lu N, Shourong Z, Xifeng Z, Geng C, Baowei L. Clinical application of computer-designed polystyrene models in complex severe spinal deformities: a pilot study. Eur Spine J. 2010;19:797–802. https://doi.org/10.1007/s00586-010-1359-0.CrossRefPubMedPubMedCentralGoogle Scholar
- 459.Takemoto M, Fujibayashi S, Ota E, Otsuki B, Kimura H, Sakamoto T, Kawai T, Futami T, Sasaki K, Matsushita T, Nakamura T, Neo M, Matsuda S. Additive-manufactured patient-specific titanium templates for thoracic pedicle screw placement: novel design with reduced contact area. Eur Spine J. 2016;25:1698–705. https://doi.org/10.1007/s00586-015-3908-z.CrossRefPubMedGoogle Scholar
- 462.Yang M, Li C, Li Y, Zhao Y, Wei X, Zhang G, Fan J, Ni H, Chen Z, Bai Y, Li M. Application of 3D rapid prototyping technology in posterior corrective surgery for Lenke 1 adolescent idiopathic scoliosis patients. Medicine (Baltimore). 2015;94:e582. https://doi.org/10.1097/MD.0000000000000582.CrossRefGoogle Scholar
- 469.Chung M, Radacsi N, Robert C, McCarthy ED, Callanan A, Conlisk N, Hoskins PR, Koutsos V. On the optimization of low-cost FDM 3D printers for accurate replication of patient-specific abdominal aortic aneurysm geometry. 3D Print Med. 2018;4:2. https://doi.org/10.1186/s41205-017-0023-2.CrossRefPubMedPubMedCentralGoogle Scholar
- 482.Meess KM, Izzo RL, Dryjski ML, Curl RE, Harris LM, Springer M, Siddiqui AH, Rudin S, Ionita CN. 3D printed abdominal aortic aneurysm phantom for image guided surgical planning with a patient specific fenestrated endovascular graft system. Proc SPIE Int Soc Opt Eng. 2017. https://doi.org/10.1117/12.2253902.
- 486.Sodian R, Schmauss D, Schmitz C, Bigdeli A, Haeberle S, Schmoeckel M, Markert M, Lueth T, Freudenthal F, Reichart B, Kozlik-Feldmann R. 3-dimensional printing of models to create custom-made devices for coil embolization of an anastomotic leak after aortic arch replacement. Ann Thorac Surg. 2009;88:974–8. https://doi.org/10.1016/j.athoracsur.2009.03.014.CrossRefPubMedGoogle Scholar
- 494.Treasure T, Takkenberg JJM, Golesworthy T, Rega F, Petrou M, Rosendahl U, Mohiaddin R, Rubens M, Thornton W, Lees B, Pepper J. Personalised external aortic root support (PEARS) in Marfan syndrome: analysis of 1-9 year outcomes by intention-to-treat in a cohort of the first 30 consecutive patients to receive a novel tissue and valve-conserving procedure, compared with the published results of aortic root replacement. Heart. 2014;100:969–75. https://doi.org/10.1136/heartjnl-2013-304913. CrossRefPubMedPubMedCentralGoogle Scholar
- 495.Valverde I, Gomez G, Coserria JF, Suarez-Mejias C, Uribe S, Sotelo J, Velasco MN, Santos De Soto J, Hosseinpour A-R, Gomez-Cia T. 3D printed models for planning endovascular stenting in transverse aortic arch hypoplasia. Catheter Cardiovasc Interv. 2015;85:1006–12. https://doi.org/10.1002/ccd.25810.CrossRef