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Type: Review article
Published: 15-03-2019

 

Imaging and printing in plastic and reconstructive surgery part 1: established techniques

Michael P Chae MBBS BMedSc,1,2,3 David J Hunter-Smith MBBS MPH FRACS,1,2,3 Warren M Rozen MBBS PhD FRACS1,2,3

1 

Department of Plastic, Reconstructive and Hand Surgery
Peninsula Health
Frankston, Victoria
AUSTRALIA

 

2 

Peninsula Clinical School
Central Clinical School at Monash University
The Alfred Centre
Melbourne, Victoria
AUSTRALIA

3

Department of Surgery
School of Clinical Sciences at Monash University
Monash Medical Centre
Clayton, Victoria
AUSTRALIA

 

 

 

OPEN ACCESS

Correspondence

Name: Warren Rozen

Address: Monash University Plastic and Reconstructive Surgery Group (Peninsula)
Peninsula Health, Department of Surgery
2 Hastings Road
Frankston, Victoria, 3199
AUSTRALIA

Email: warrenrozen@hotmail.com

Telephone: +61 3 9784 8416

Citation: Chae MP, Hunter-Smith DJ, Rozen WM. Imaging and printing in plastic and reconstructive surgery part 1: established techniques. Aust J Plast Surg. 2019;2(1):55–68

Accepted for publication: 21 June 2018

Copyright © 2019. Authors retain their copyright in the article. This is an open access article distributed under the Creative Commons Attribution Licence which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.


Abstract

Background: An increasing number of reconstructive surgeons are using modern imaging technologies for preoperative planning and intraoperative surgical guidance. Conventional imaging modalities such as CT and MRI are relatively affordable and widely accessible and offer powerful functionalities. In the first of a two-part series, we evaluate established three-dimensional (3D) imaging and printing techniques based on CT and MRI used in plastic and reconstructive surgery.

Method: A review of the published English literature dating from 1950 to 2017 was taken using databases such as PubMed, MEDLINE®, Web of Science and EMBASE.

Result: In plastic and reconstructive surgery, the most commonly used, free software platforms are 3D Slicer (Surgical Planning Laboratory, Boston, MA, USA) and OsiriX (Pixmeo, Geneva, Switzerland). Perforator mapping using 3D-reconstructed images from computed tomography angiography (CTA) and magnetic resonance angiography (MRA) is commonly used for preoperative planning. Three-dimensional volumetric analysis using current software techniques remains labour-intensive and reliant on operator experience. Three-dimensional printing has been investigated extensively since its introduction. As more free open-source software suites and affordable 3D printers become available, 3D printing is becoming more accessible for clinicians.

Conclusion: Numerous studies have explored the application of 3D-rendered conventional imaging modalities for perforator mapping, volumetric analysis and printing. However, there is a lack of comprehensive review of all established 3D imaging and printing techniques in a language suitable for clinicians.

Key words: image processing, 3D printing, plastic and reconstructive surgery, CTA, MRA


Introduction

Performing perforator-based flap reconstruction requires careful selection of the perforator, flap design and donor site. A suitable perforator is ideally harvested from a donor site with minimal morbidity and is large enough to facilitate microsurgical anastomosis and adequately supply all portions of the flap.1 In recent times, an increasing number of plastic and reconstructive surgeons have begun using modern 3D imaging and printing technologies to aid preoperative planning, intraoperative guidance and medical education.2,3 However, there is a lack of comprehensive review of these techniques that provides a global understanding of this novel field in a language suitable for clinicians.

Currently, a plethora of imaging modalities is being used in plastic and reconstructive surgery, mainly computed tomography angiography (CTA) and magnetic resonance angiography (MRA).4–9 First reported for perforator-based flap planning in 2006,6,7 CTA is widely used in preoperative investigations by institutions around the world and is considered the gold standard due to its high accuracy and reliability.4,5,10–13 However, CTA poses the potential risk of additional radiation exposure, involves intravenous administration of iodinated contrast media and does not provide haemodynamic features such as flow velocity and direction.

Magnetic resonance angiography bypasses radiation exposure but is limited by only being able to detect vessels greater than 1 mm in diameter.14 It also has lower spatial resolution15 and poorer contrast differentiation from the surrounding soft tissue.16 As a result, MRA has a lower sensitivity (50%) for detecting abdominal wall perforators than CTA.9 Enhanced by recent advances in imaging techniques,17 contrast agents18 and increasing availability of higher field-strength scanners,19 more recent studies have reported improved sensitivity in identifying perforators (91.3–100%).8,20–24 As a result, MRA remains an investigation of choice for younger patients and for those with iodine allergy and impaired renal function.25

In this review, we evaluate the established 3D imaging and printing techniques based on CT and MRI.

Method

We reviewed the published English literature from 1950 to 2017 from well-established databases such as PubMed, MEDLINE®, Web of Science and EMBASE. We included all studies that analyse 3D imaging and printing techniques used in surgery, especially plastic and reconstructive surgery. We used search terms such as ‘3D imaging’, ‘CTA’, ‘MRA’, ‘3D image software’, ‘volumetric analysis’, ‘3D printing’, ‘preoperative planning’, ‘intraoperative guidance’, ‘education’, ‘training’ and ‘customised implant’. We also retrieved secondary references found through bibliographical linkages.

3D imaging rendering software

Through our literature review, we identified the most commonly used 3D image rendering software suites in medical application. We identified their specifications, such as the software language on which they are based, cost, open-source capability and function, by accessing the manufacturer’s website or from publications.

3D perforator mapping

We identified that CTA and MRA are the most commonly used imaging modalities for 3D perforator mapping. Hence, we evaluated the software suites based on these modalities.

3D volumetric analysis

We focused our analysis of 3D volumetric analysis based on conventional 3D imaging techniques, CT and MRI. We systematically identified a list of software suites used to analyse 3D volumetric data from CT or MRI and examined their application in plastic and reconstructive surgery.

3D printing

Studies using 3D printing for preoperative planning in plastic and reconstructive surgery were assessed using Oxford Centre for Evidence-Based Medicine levels of evidence.26 Given that the most common 3D printing application in plastic and reconstructive surgery is mandibular reconstruction with free fibular flap, we performed a focused further qualitative analysis of this application.

Results and discussion

Numerous studies have explored the application of conventional imaging modalities for 3D perforator mapping, 3D volumetric analysis and 3D printing.

3D image rendering

Proprietary software provided by manufacturers of CT and MRI scanners generally offers only two-dimensional image-viewing capabilities. As a result, numerous free, open-source software platforms have been developed that are capable of 3D image rendering. They are built on robust, but limited, open-source software libraries that provide the basic architecture. In plastic and reconstructive surgery, the most commonly used free software platforms are 3D Slicer (Surgical Planning Laboratory, Boston, MA, USA) and OsiriX (Pixmeo, Geneva, Switzerland) (see Table 1).

Table 1: Summary of 3D image rendering software
Product Manufacturer Software language Free Open-source Function
3D Slicer Surgical Planning Laboratory (Boston, MA, USA) C++ Yes Yes Built on ITK and VTK 
    Python     Easy-to-use graphical user interface
          Creates 3D images of regions of interest suitable for 3D printing
OsiriX Pixmeo (Geneva, Switzerland) Objective-C Yes No Built on ITK and VTK 
          Enables both viewing and 3D rendering of anatomical structures 
          Easy-to-use graphical user interface 
          Has both 3D rendering techniques: volume-rendered technique and maximum intensity projection

3D Slicer

3D Slicer27 is a well-supported, open-source platform built on Insight ToolKit (ITK) and Visualisation ToolKit (VTK) using C++ and Python.2 Developed to segment brain tumours from MRI scans28 3D Slicer is used in a variety of medical applications ranging from lung cancer diagnosis29 to cancer imaging.30 This software is adept at generating volumetric images for 3D printing through thresholding and segmentation techniques (see below).

OsiriX

The OsiriX31 image-viewing software platform is built on ITK and VTK, for Macintosh computers only. It has an intuitive graphical user interface and fast processing speed make it popular with clinicians worldwide.31 OsiriX enables viewing of multidimensional data such as positron emission tomography (PET)-CT32 and cardiac-CT as well as standard tomographic scans (CT and MRI).33 It is suitable for viewing 3D and 4D datasets but limited to 3D anatomical models of large organs such as long bones and the heart.

3D perforator mapping

In perforator based, free flap reconstruction, plastic surgeons commonly rely on CTA- or MRA-based 3D reconstructed images of the relevant perforators for preoperative planning (see Figure 1).

Figure 1. CTA-based 3D perforator mapping in DIEP flap planning performed using OsiriX software
(A) MIP reconstruction demonstrating the intramuscular and subcutaneous course of each perforator and (B) VRT reconstruction demonstrating the location of the perforators (blue arrows) as they emerge from the rectus sheath in reference to the umbilicus (marked). CTA: computed tomographic angiography 3D: three-dimensional DIEP: deep inferior epigastric artery perforator MIP: maximum intensity projection VRT: volume-rendered technique DIEA: deep inferior epigastric artery.

CTA

Computed tomography angiography is the most commonly used imaging modality for 3D perforator mapping, using maximum intensity projection (MIP) and volume-rendered technique (VRT) 3D software reconstruction techniques. Compared with Siemens Syngo InSpace 4D (Siemens, Erlangen, Germany) and VoNaviX (IVS Technology, Chemnitz, Germany), which are expensive, and virSSPA (University Hospitals Virgen del Rocio, Sevilla, Spain), which is not available outside the original institution, OsiriX software platform is free and has been demonstrated to be as accurate.

MRA

Modern magnetic resonance technology can provide superior 3D reconstructed images. However, they are expensive, time-consuming and relatively difficult to perform. Similar to CTA, free OsiriX software can be used for 3D perforator mapping from MRA. Recently, investigators have developed a semi-automated plugin tool for analysing MRA images using OsiriX, However, it remains to be validated in a large cohort.

3D volumetric analysis

Accurate assessment of tissue volume is an important aspect of preoperative planning in plastic surgery.34–38 Particularly in breast reconstructive surgery, volumetric analysis is paramount for achieving symmetrisation and a satisfactory outcome.39–44 However, an accurate, reliable and convenient method of objective breast volumetric analysis has remained elusive (see Figure 2 and Table 2).45

Figure 2. MRI-based 3D volumetric analysis in planning breast reconstructive surgery demonstrating 611 mL on the right breast and 635 mL on the left breast, performed using OsiriX software (Pixmeo).
Table 2: Summary of software platforms capable of performing 3D volumetric analysis from CT and MRI
Product Manufacturer Free Open-
source
Clinical application
CT
OsiriX Pixmeo, Geneva, Switzerland Yes No Breast
Aquarius Workstation TeraRecon Inc., San Mateo, CA, USA No No Breast, DIEP flap
Mimics Materialise NV, Leuven, Belgium No No DIEP flap
Leonardo Workstation Siemens AG, Munich, Germany No No DIEP flap
ImageJ NIH, Rockville, MD, USA Yes No Orbital volume
Vevo LAB Fujifilm ViewSonics, Toronto, Canada No No Autologous fat graft in mice
SkyScan CTan Bruker, Kontich, Belgium No No Limb lymphoedema in mice
MRI
OsiriX Pixmeo, Geneva, Switzerland Yes No Breast, Breast implant,
limb lymphoedema in mice
Volume Viewer Plus GE Healthcare, Waukesha, WI, USA No No Breast
BrainLAB BrainLAB AG, Feldkirchen, Germany No No Breast, Breast implant
Medis Suite MR Medis Medical Imaging Systems BV, Leiden,
The Netherlands
No No Breast, Breast implant
AW Server GE Healthcare, Waukesha, WI, USA No No Breast glandular tissue
Dextroscope Volume Interactions, Singapore No No Malar fat pad
ImageJ National Institutes of Health, Rockville, MD, USA Yes No Breast
DIEP: deep inferior epigastric artery perforator. Source: Eder et al,39 Rha et al,41 Rosson et al,43 Herold et al,44 Lee et al,46 Chae et al,51 Rha et al,52 Blackshear et al,131 and Corey et al135

CTA

Calculating the flap volume from CTA and comparing it with the intraoperative flap weight, Eder et al reported high correlation between the two measurements (r=0.998, p <0.001) demonstrating the high prediction accuracy of CTA (0.29%; –8.77 to 5.67%).39

In order to further improve its accuracy, Rosson et al placed fiducial markers on the surgical incision line before the CTA and achieved accuracy of up to 99.7 per cent (91–109%).43

Lee et al calculated a ratio using the volume of the breast and the potential deep inferior epigastric artery perforator (DIEP) flap from CTA and created a treatment algorithm.46 If more than 50 per cent of the harvested flap is required for reconstruction, surgeons can make modifications to the flap design by increasing its height, capturing more adipose tissue by bevelling superiorly from the flap’s upper margin, like Ramakrishnan’s extended DIEP technique,47 and incorporating multiple perforators if available. If more than 75 per cent of the flap is required, venous augmentation is performed with contralateral superficial inferior epigastric vein. Using this algorithm in 109 consecutive patients, the authors noted a significant reduction in perfusion-related complications (5.6 vs 22.9%, p=0.006) and fat necrosis (5.6 vs 19.1%, p=0.03).

MRI

In comparison to CT, MRI has superior soft-tissue resolution and is thus more accurate at measuring breast volumes (r=0.928 vs 0.782, p=0.001)48 and has a mean measurement deviation of only 4.3 per cent.49 Furthermore, Rha et al show that MRI-derived breast volume is more accurate than the traditional volumetric method using a plaster cast (r2=0.945 vs 0.625).

Using the manufacturer’s specifications as gold standard, Herold et al measured the volume of breast implants using MRI in patients with bilateral augmentation mammaplasty.44 Furthermore, they compared the accuracy of three commonly available 3D image processing software platforms: OsiriX, BrainLAB (BrainLAB AG, Feldkirchen, Germany) and Medis Suite MR (Medis Medical Imaging Systems BV, Leiden, The Netherlands). BrainLAB had the lowest mean deviation of 2.2 ± 1.7 per cent, followed by OsiriX at 2.8 ± 3.0 per cent and Medis Suite MR at 3.1 ± 3.0 per cent. However, all software platforms correlated highly accurately with the reference overall (r=0.99). Interestingly, software analysis is fastest using OsiriX at 30 seconds per implant, followed by BrainLAB and Medis Suite MR at 5 minutes.

To date, most software techniques remain manual, that is labour-intensive and reliant on operator experience, while validated evidence of commercially available automatic segmentation tool is scarce.50,51 Interestingly, Rha et al used ImageJ, a free NIH-developed image processing program, to successfully perform volumetric analysis of the orbit and breast from CT and MRI, respectively.52 However, ImageJ has yet to be investigated in clinical application

3D printing

In contrast to medical imaging modalities that are limited by being displayed on a 2D surface, such as a computer screen, a 3D-printed biomodel can additionally provide haptic feedback.2,53–56 Three-dimensional printing, also known as rapid prototyping or additive manufacturing, describes a process by which a product derived from computer-aided design (CAD) is built in a layer-by-layer manner.57–59 The main advantages of 3D printing are the ability to customise, cost-efficiency and convenience.60,61 Since its introduction, the use of 3D printing in surgery has been extensively investigated.

In clinical application, two types of software platforms are required for 3D printing: 3D modelling software that can convert standard Digital Imaging and Communications in Medicine (DICOM) files from CTA/MRA into a CAD file; and 3D slicing software that divides the CAD file into thin data slices suitable for printing.62 A range of modelling software is available but only the following are user-friendly and commonly reported: 3D Slicer,51,63 OsiriX64 and Mimics (Materialise NV, Leuven, Belgium).65 Three-dimensional slicing software usually accompanies 3D printers at no additional cost and has a simple user interface such as Cube software (3D Systems, Rock Hill, SC, USA), MakerBot Desktop (MakerBot Industries, New York, NY, USA) or Cura (Ultimaker BV, Geldermalsen, The Netherlands).

In clinical application, a host of 3D printer types have been used including fused filament fabrication (FFF), selective laser sintering (SLS), stereolithography (SLA), binder jetting and multijet modelling (MJM).2 Fused filament fabrication is the most common and most affordable 3D desktop printing technology available.66–68 In an FFF 3D printer, a melted filament of thermoplastic material is extruded from a nozzle moving in the x–y plane and solidifies upon deposition on a build plate.69 More recently, 3D metal printing using SLS has gained popularity in creating sterilisable surgical guides70,71 and customised dental implants.72

Encouraged by its potential, surgeons from a wide range of specialities have applied 3D printing to their practice such as neurosurgery,73–80 cranio-maxillofacial surgery,81–88 cardiothoracic surgery,89,90 orthopaedic surgery,91,92 transplantation,93–95 ear, nose and throat surgery96,97 and breast cancer surgery.98 Similarly, in reconstructive plastic surgery, 3D printing appears most useful for preoperative planning, intraoperative guidance, medical education and creating custom implants. 3D-printed bespoke implants overlap significantly with 3D bioprinting99–101 and are beyond the scope of this article.

Preoperative planning

Three-dimensional printing has been most commonly used in plastic and reconstructive surgery for preoperative planning (see Table 3).

Table 3: Use of CT/MRI-based 3D-printed haptic models for preoperative planning in plastic and reconstructive surgery
Clinical application 3D-printed model Imaging 3D modelling software 3D printer
DIEP Asymmetrical breast CTA Osirix (Pixmeo, Geneva, Switzerland) Cube 2 (3D Systems, Rock Hill, SC, USA)
Case report
DIEP Breast CTA AYRA (Virgen del Rocio University Hospital, Sevilla, Spain) FFF
Case series of 35 IMA perforator CT Mimics (Materialise NV, Leuven, Belgium) ProJet x60 (3D Systems, Rock Hill, SC, USA)
DIEP
Cadaver
DIEP DIEP flap CTA Mimics (Materialise NV, Leuven, Belgium) Objet500 Connex1 (Stratasys, Eden Prairie, MN, USA)
Case report
Lower limb soft-tissue defect Reverse model of the defect CTA Osirix (Pixmeo, Geneva, Switzerland) Cube 2 (3D Systems, Rock Hill, SC, USA)
Case report
Sacral soft-tissue defect Sacral defect CT/MRI Osirix (Pixmeo, Geneva, Switzerland) Cube 2 (3D Systems, Rock Hill, SC, USA)
Case series of five        
Hemi-mandibulectomy Mandible and giant invasive SCC CTA 3D Slicer (Surgical Planning Laboratory, Boston, MA, USA) MakerBot Z18 (MakerBot Industries, New York, NY, USA)
Case report        
Bony defect of the wrist Bony defect CT MeshMixer (Autodesk, San Rafael, CA, USA) Micro 3D Printer (M3D, Fulton, MD, USA)
Case series of three
4D printing of thumb movements Hand 4D CT Osirix (Pixmeo, Geneva, Switzerland) Cube 2 (3D Systems, Rock Hill, SC, USA)
Case report        
Nasal cartilaginous defect Nasal alar cartilage MRI GOM Inspect (GOM GmbH, Braunschweigh, Germany) ZPrinter 250 (3D Systems, Rock Hill, SC, USA)
Cadaver
Human volunteer
Augmentative rhinoplasty Individualised nasal implant CT Rhinoceros (McNeel, Seattle, WA, USA) Cubicon Single (Hyvision System, Seongnam, South Korea
Case series of seven        
CTA: computed tomographic angiography DIEP: deep inferior epigastric artery perforator SCC: squamous cell carcinoma FFF: fused filament fabrication IMA: internal mammary artery.
Source: Chae et al,5,63,136 Garcia-Tutor et al,64 Gillis et al,102 Mehta et al,103 Suarez-Mejias et al,104 Cabalag et al,105 Taylor et al,106 Visscher et al,107Choi et al108

Autologous breast reconstruction

In 2014, Gillis and Morris reported the first case of a 3D-printed internal mammary artery (IMA) and its perforators, a common recipient site in free flap breast reconstruction.102 Similarly, Mehta et al 3D-printed a multi-colour, multi-material model of a deep inferior epigastric artery (DIEA) and its perforators.103 Despite the benefits, both studies revealed the high cost of 3D printing (US$400–US$1200 per model), mainly due to having to outsource the manufacturing. In addition, outsourcing introduces delays of up to six to eight weeks that may not be appropriate in some clinical settings. As a result, Suarez-Mejias et al developed their own 3D modelling software called AYRA (Virgen del Rocio University Hospital, Sevilla, Spain).104 More recently, Chae et al described an affordable and convenient technique of 3D printing using free software platforms and desktop 3D printers (see Figure 3).51

Figure 3. 3D-printed biomodel of breasts in planning reconstruction using Cube 2 printer (3D Systems, Rock Hill, SC, USA). Reproduced with permission from Chae et al51

Soft-tissue modelling

In a case of lower limb reconstruction, Chae et al 3D-printed a model of the soft-tissue defect that aided in flap design.63 Similarly, Garcia-Tutor et al used 3D-printed models of large sacral defects to perform qualitative and quantitative volumetric assessment.64 Cabalag et al fabricated a model of a giant squamous cell carcinoma that was useful for planning hemi-mandibulectomy and determining the length of the free fibular flap required.105

Bony modelling

Taylor and Lorio 3D-printed, in-house, a negative mould of a scaphoid/lunate defect from avascular necrosis from which a silicone model was created, sterilised and used intraoperatively for flap planning.106 In an interesting application, Chae et al described their technique of four-dimensional (4D) printing whereby multiple models of the thumb and wrist bones were 3D printed from 4D CT scans to demonstrate their dynamic relationship.

Cartilage modelling

Three-dimensional assessment of nasal cartilaginous defect can be useful for planning reconstruction. Visscher et al demonstrated that 3D printing alar cartilages using MRI showed a mean error of 2.5 mm.107 Interestingly, most of the difference was found in 3D printing the medial crus but the lateral crus remained highly accurate, probably due to its more linear shape. Recently, Choi et al 3D-printed a patient-specific negative mould from CT to create silicone nasal implants for augmentative rhinoplasty using in-house software108 and demonstrated a mean accuracy of 0.07 mm (0.17%) with no complications.

Intraoperative guidance

Use of 3D-printed fibular osteotomy guides for mandibular reconstruction has been studied extensively (see Table 4).66,109–123 Investigators have demonstrated their accuracy of up to 0.1–0.4 mm.66,110–112,117 Moreover, they can significantly reduce flap ischaemia time (120 minutes vs 170 minutes, p=0.004)114 and total operating time (8.8 hours vs 10.5 hours, p=0.0006).117

Table 4: Summary of all studies investigating the use of an image-guided 3D-printed surgical guide in mandibular reconstruction with a free fibular flap
Year Patients Source of 3D printing Imaging 3D rendering software 3D printers
2017 18 In-house CT AYRA (Virgen del Rocio University Hospital, Sevilla, Spain) Objet30 Pro (Stratasys, Eden Prairie, MN, USA)
Osirix (Pixmeo, Geneva, Switzerland) Zortrax M200 (Zortrax, Olsztyn, Poland)
3D Slicer (Surgical Planning Laboratory, Boston, MA, USA)
MeshMixer (Autodesk, San Rafael, CA, USA)
Blender (Blender Foundation, Amsterdam, The Netherlands)
2017 3 Outsourced CT Osirix (Pixmeo, Geneva, Switzerland)
MeshLab (ISTI, Pisa, Italy)
Netfabb (Autodesk, San Rafael, CA, USA)
Blender (Blender Foundation, Amsterdam, The Netherlands) Formiga P 100 (EOS, Munich, Germany)
2017 7 Outsourced CT E3D Online (E3D Online, Oxfordshire, UK) ProJet 3510 HD (3D Systems, Rock Hill, SC, USA)
Amira (FEI Company, Hillsboro, OR, USA)
2016 1 In-house CT Blender (Blender Foundation, Amsterdam, The Netherlands) PolyJet (Stratasys, Eden Prairie, MN, USA)
2015 1 Outsourced CT SurgiCase CMF (Materialise NV, Leuven, Belgium) SLM
2013 68 Outsourced CT ProPlan CMF (Dupuy Synthes CMF, West Chester, PA, USA) SLA
2013 48 Outsourced CT VoXim (IVS Technology, Chemnitz, Germany) SLA
2013 10 Outsourced CT ProPlan CMF (Dupuy Synthes CMF, West Chester, PA, USA) SLA
2013 38 Outsourced CT SurgiCase CMF (Materialise NV, Leuven, Belgium) SLA
2012 1 Outsourced CT SurgiCase CMF (Materialise NV, Leuven, Belgium)
Rhinoceros (McNeel, Seattle, WA, USA) M 270 (EOS, Munich, Germany)
2012 1 In-house CTA AYRA (Virgen del Rocio University Hospital, Sevilla, Spain) FFF
2012 9 In-house CT Mimics (Materialise NV, Leuven, Belgium) SLA 3500 (3D Systems, Rock Hill, SC, USA)
2012 15 Outsourced CT Magics (Materialise NV, Leuven, Belgium) SLA
2011 5 Outsourced CT N/A SLS
2009 3 Outsourced CT Extended Brilliance Workspace (Philips Healthcare) Objet Eden 500V (Stratasys, Eden Prairie, MN, USA)
2009 1 Outsourced CT SurgiCase CMF (Materialise NV, Leuven, Belgium) SLS nylon

Medical education

Educating junior surgical trainees and medical students about 3D pathological defects such as cleft lip and palate without hands-on interaction and demonstration is notoriously difficult. As the supply of cadavers for medical education continues to dwindle due to rising maintenance costs124 and concerns regarding occupational health and safety,125 the use of 3D-printed biomodels has become popular.126,127 Zheng et al have used 3D-printed negative moulds to fabricate soft silicone models of cleft lip and palate on which students directly perform cheiloplasty.128 Subsequently, in a randomised clinical trial of 67 medical students, AlAli et al demonstrated that the knowledge gained using 3D-printed models of cleft lip and palate was significantly higher than when using standard slide presentations (44.65% vs 32.16%, p=0.038).129 Similarly, clinicians have 3D printed negative moulds of paediatric microtia for practical demonstration.130

Conclusion

Many studies have explored the application of 3D-rendered conventional imaging modalities for 3D perforator mapping, 3D volumetric analysis and 3D printing. There are numerous free, open-source software platforms that are capable of 3D image rendering, such as 3D Slicer and OsiriX. For perforator mapping, most plastic surgeons rely on CTA- or MRA-based 3D reconstructed images. Current 3D volumetric analysis technologies remain labour-intensive and are yet to be automatised. 3D printing has been most commonly used in plastic and reconstructive surgery for preoperative planning in mandibular reconstruction with a free fibular flap. The majority of these studies have a lower level of evidence, consisting of case series and reports. Furthermore, there is a lack of comprehensive review of all established 3D imaging and printing techniques in a language suitable for clinicians.

Disclosure

The authors have no financial or commercial conflicts of interest to disclose.

References

  1. Saint-Cyr M, Schaverien MV, Rohrich RJ. Perforator flaps: history, controversies, physiology, anatomy, and use in reconstruction. Plast Reconstr Surg. 2009;123(4):132e–45e.
  2. Chae MP, Rozen WM, McMenamin PG, Findlay MW, Spychal RT, Hunter-Smith DJ. Emerging applications of bedside 3D printing in plastic surgery. Front Surg. 2015;2:25.
  3. Pratt GF, Rozen WM, Chubb D, Ashton MW, Alonso-Burgos A, Whitaker IS. Preoperative imaging for perforator flaps in reconstructive surgery: a systematic review of the evidence for current techniques. Ann Plast Surg. 2012;69(1):3–9.
  4. Smit JM, Dimopoulou A, Liss AG, Zeebregts CJ, Kildal M, Whitaker IS, Magnusson A, Acosta R. Preoperative CT angiography reduces surgery time in perforator flap reconstruction. J Plast Reconstr Aesthet Surg. 2009;62(9):1112–117.
  5. Rozen WM, Anavekar NS, Ashton MW, Stella DL, Grinsell D, Bloom RJ, Taylor GI. Does the preoperative imaging of perforators with CT angiography improve operative outcomes in breast reconstruction? Microsurgery. 2008;28(7):516–23.
  6. Masia J, Clavero JA, Larranaga JR, Alomar X, Pons G, Serret P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg. 2006;59(6):594–99.
  7. Alonso-Burgos A, Garcia-Tutor E, Bastarrika G, Cano D, Martinez-Cuesta A, Pina LJ. Preoperative planning of deep inferior epigastric artery perforator flap reconstruction with multislice-CT angiography: imaging findings and initial experience. J Plast Reconstr Aesthet Surg. 2006;59(6):585–93.
  8. Masia J, Kosutic D, Cervelli D, Clavero JA, Monill JM, Pons G. In search of the ideal method in perforator mapping: noncontrast magnetic resonance imaging. J Reconstr Microsurg. 2010;26(1):29–35.
  9. Rozen WM, Stella DL, Bowden J, Taylor GI, Ashton MW. Advances in the pre-operative planning of deep inferior epigastric artery perforator flaps: magnetic resonance angiography. Microsurgery. 2009;29(2):119–23.
  10. Clavero JA, Masia J, Larranaga J, Monill JM, Pons G, Siurana S, Alomar X. MDCT in the preoperative planning of abdominal perforator surgery for postmastectomy breast reconstruction. Am J Roentgenol. 2008;191(3):670–76.
  11. Masia J, Larranaga J, Clavero JA, Vives L, Pons G, Pons JM. The value of the multidetector row computed tomography for the preoperative planning of deep inferior epigastric artery perforator flap: our experience in 162 cases. Ann Plast Surg. 2008;60(1):29–36.
  12. Rozen WM, Ashton MW, Grinsell D, Stella DL, Phillips TJ, Taylor GI. Establishing the case for CT angiography in the preoperative imaging of abdominal wall perforators. Microsurgery. 2008;28(5):306–13.
  13. Rosson GD, Williams CG, Fishman EK, Singh NK. 3D CT angiography of abdominal wall vascular perforators to plan DIEAP flaps. Microsurgery. 2007;27(8):641–46.
  14. Rozen WM, Ashton MW, Stella DL, Phillips TJ, Taylor GI. Magnetic resonance angiography and computed tomographic angiography for free fibular flap transfer. J Reconstr Microsurg. 2008;24(6):457–58.
  15. Cina A, Barone-Adesi L, Rinaldi P, Cipriani A, Salgarello M, Masetti R, Bonomo, L. Planning deep inferior epigastric perforator flaps for breast reconstruction: a comparison between multidetector computed tomography and magnetic resonance angiography. Eur Radiol. 2013;23(8):2333–343.
  16. Aubry S, Pauchot J, Kastler A, Laurent O, Tropet Y, Runge M. Preoperative imaging in the planning of deep inferior epigastric artery perforator flap surgery. Skeletal Radiol. 2013;42(3):319–27.
  17. Mathes DW, Neligan PC. Current techniques in preoperative imaging for abdomen-based perforator flap microsurgical breast reconstruction. J Reconstr Microsurg. 2010;26(1):3–10.
  18. Neil-Dwyer JG, Ludman CN, Schaverien M, McCulley SJ, Perks AG. Magnetic resonance angiography in preoperative planning of deep inferior epigastric artery perforator flaps. J Plast Reconstr Aesthet Surg. 2009;62(12):1661–665.
  19. Hartung MP, Grist TM, Francois CJ. Magnetic resonance angiography: current status and future directions. J Cardiovasc Magn Reson. 2011;13:19.
  20. Pauchot J, Aubry S, Kastler A, Laurent O, Kastler B, Tropet Y. Preoperative imaging for deep inferior epigastric perforator flaps: a comparative study of computed tomographic angiography and magnetic resonance angiography. Eur J Plast Surg. 2012;35(11):795–801.
  21. Greenspun D, Vasile J, Levine JL, Erhard H, Studinger R, Chernyak V, Newman T, Prince M, Allen RJ. Anatomic imaging of abdominal perforator flaps without ionizing radiation: seeing is believing with magnetic resonance imaging angiography. J Reconstr Microsurg. 2010;26(1):37–44.
  22. Newman TM, Vasile J, Levine JL, Greenspun DT, Allen RJ, Chao MT, Winchester PA, Prince MR. Perforator flap magnetic resonance angiography for reconstructive breast surgery: a review of 25 deep inferior epigastric and gluteal perforator artery flap patients. J Magn Reson Imaging. 2010;31(5):1176–184.
  23. Alonso-Burgos A, Garcia-Tutor E, Bastarrika G, Benito A, Dominguez PD, Zubieta JL. Preoperative planning of DIEP and SGAP flaps: preliminary experience with magnetic resonance angiography using 3-tesla equipment and blood-pool contrast medium. J Plast Reconstr Aesthet Surg. 2010;63(2):298–304.
  24. Chernyak V, Rozenblit AM, Greenspun DT, Levine JL, Milikow DL, Chia FA, Erhard, HA. Breast reconstruction with deep inferior epigastric artery perforator flap: 3.0-T gadolinium-enhanced MR imaging for preoperative localization of abdominal wall perforators. Radiology. 2009;250(2):417–24.
  25. Chae MP, Hunter-Smith DJ, Rozen WM. Comparative analysis of fluorescent angiography, computed tomographic angiography and magnetic resonance angiography for planning autologous breast reconstruction. Gland Surg. 2015;4(2):164–78.
  26. Centre for Evidence-Based Medicine. OCEBM levels of evidence [PDF on internet] Oxford, United Kingdom: CEBM [Updated 1 May 2016; cited 1 October 2014]. Available from: https://www.cebm.net/2016/05/ocebm-levels-of-evidence/.
  27. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging. 2012;30(9):1323–341.
  28. Gering DT, Nabavi A, Kikinis R, Hata N, O’Donnell LJ, Grimson WE, Jolesz FA, Black PM, Wells WM III. An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging. 2001;13(6):967–75.
  29. Yip SSF, Parmar C, Blezek D, Estepar RSJ, Pieper S, Kim J, Aerts H. Application of the 3D Slicer chest imaging platform segmentation algorithm for large lung nodule delineation. PLoS One. 2017;12(6):e0178944.
  30. Hassanzadeh E, Alessandrino F, Olubiyi OI, Glazer DI, Mulkern RV, Fedorov A, Tempany CM, Fennessy FM. Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer. Abdom Radiol (NY). 2018;43(5):1237–244.
  31. Rosset A, Spadola L, Ratib O. OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging. 2004;17(3):205–16.
  32. Vogel WV, Oyen WJ, Barentsz JO, Kaanders JH, Corstens FH. PET/CT: panacea, redundancy, or something in between? J Nucl Med. 2004;45 Suppl 1:15S–24S.
  33. Flohr T, Ohnesorge B, Bruder H, Stierstorfer K, Simon J, Suess C et al. Image reconstruction and performance evaluation for ECG-gated spiral scanning with a 16-slice CT system. Med Phys. 2003;30(10):2650–662.
  34. Tepper OM, Karp NS, Small K, Unger J, Rudolph L, Pritchard, Choi M. Three-dimensional imaging provides valuable clinical data to aid in unilateral tissue expander-implant breast reconstruction. Breast J. 2008;14(6):543–50.
  35. Hudson DA. Factors determining shape and symmetry in immediate breast reconstruction. Ann Plast Surg. 2004;52(1):15–21.
  36. Kovacs L, Zimmermann A, Papadopulos NA, Biemer E. Re: factors determining shape and symmetry in immediate breast reconstruction. Ann Plast Surg. 2004;53(2):192–94.
  37. Lee HY, Hong K, Kim EA. Measurement protocol of women’s nude breasts using a 3D scanning technique. Appl Ergon. 2004;35(4):353–59.
  38. Galdino GM, Nahabedian M, Chiaramonte M, Geng JZ, Klatsky S, Manson P. Clinical applications of three-dimensional photography in breast surgery. Plast Reconstr Surg. 2002;110(1):58–70.
  39. Eder M, Raith S, Jalali J, Muller D, Harder Y, Dobritz M, Papadopulos NA, Machens HG, Kovacs L. Three-dimensional prediction of free-flap volume in autologous breast reconstruction by CT angiography imaging. Int J Comput Assist Radiol Surg. 2014;9(4):541–49.
  40. Eric M, Anderla A, Stefanovic D, Drapsin M. Breast volume estimation from systematic series of CT scans using the Cavalieri principle and 3D reconstruction. Int J Surg. 2014;12(9):912–17.
  41. Rha EY, Choi IK, Yoo G. Accuracy of the method for estimating breast volume on three-dimensional simulated magnetic resonance imaging scans in breast reconstruction. Plast Reconstr Surg. 2014;133(1):14–20.
  42. Kim H, Lim SY, Pyon JK, Bang SI, Oh KS, Mun GH. Preoperative computed tomographic angiography of both donor and recipient sites for microsurgical breast reconstruction. Plast Reconstr Surg. 2012;130(1):11e–20e.
  43. Rosson GD, Shridharani SM, Magarakis M, Manahan MA, Stapleton SM, Gilson MM, Flores JI, Basdag B, Fishman E. Three-dimensional computed tomographic angiography to predict weight and volume of deep inferior epigastric artery perforator flap for breast reconstruction. Microsurgery. 2011;31(7):510–16.
  44. Herold C, Reichelt A, Stieglitz LH, Dettmer S, Knobloch K, Lotz J, Vogt PM. MRI-based breast volumetry-evaluation of three different software solutions. J Digit Imaging. 2010;23(5):603–10.
  45. Bulstrode N, Bellamy E, Shrotria S. Breast volume assessment: comparing five different techniques. Breast. 2001;10(2):117–23.
  46. Lee KT, Mun GH. Volumetric planning using computed tomographic angiography improves clinical outcomes in DIEP flap breast reconstruction. Plast Reconstr Surg. 2016;137(5):771e–780e.
  47. Chae MP, Ramakrishnan V, Hunter-Smith DJ, Rozen WM. The extended DIEP flap. In: Shiffman M, editor. Breast Reconstruction: Art, Science, and New Clinical Techniques. Heidelberg, Germany: Springer, 2016.
  48. Kim H, Mun GH, Wiraatmadja ES, Lim SY, Pyon JK, Oh KS, Lee JE, Nam SJ, Bang SI. Preoperative magnetic resonance imaging-based breast volumetry for immediate breast reconstruction. Aesthetic Plast Surg. 2015;39(3):369–76.
  49. Fowler PA, Casey CE, Cameron GG, Foster MA, Knight CH. Cyclic changes in composition and volume of the breast during the menstrual cycle, measured by magnetic resonance imaging. Br J Obstet Gynaecol. 1990;97(7):595–02.
  50. Chae MP, Hunter-Smith DJ, Spychal RT, Rozen WM. 3D volumetric analysis and haptic modeling for preoperative planning in breast reconstruction. Anaplastology. 2015;4(1):1–4.
  51. Chae MP, Hunter-Smith DJ, Spychal RT, Rozen WM. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat. 2014;146(2):457–60.
  52. Rha EY, Kim JM, Yoo G. Volume measurement of various tissues using the Image J software. J Craniofac Surg. 2015;26(6):e505–06.
  53. Kamali P, Dean D, Skoracki R, Koolen PG, Paul MA, Ibrahim AM, Lin SJ. The current role of three-dimensional (3D) printing in plastic surgery. Plast Reconstr Surg. 2016 Jan 21 (Epub ahead of print].
  54. Bauermeister AJ, Zuriarrain A, Newman MI. Three-dimensional printing in plastic and reconstructive surgery: a systematic review. Ann Plast Surg. 2016;77(5):569–76.
  55. Ibrahim AM, Jose RR, Rabie AN, Gerstle TL, Lee BT, Lin SJ. Three-dimensional printing in developing countries. Plast Reconstr Surg Glob Open. 2015;3(7):e443.
  56. Gerstle TL, Ibrahim AM, Kim PS, Lee BT, Lin SJ. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg. 2014;133(2):446–51.
  57. Sachs EM, Haggerty JS, Cima MJ, Williams PA, inventors. Three-dimensional printing techniques. United States patent US 5,204,055. 1993 April 20.
  58. Hull CW, inventor. Method for production of three-dimensional objects by stereolithography. United States patent US 4,929,402. 1990 May 29.
  59. Hull CW, inventor. Apparatus for production of three-dimensional objects by stereolithography. United States patent US 4,575,330. 1986 Mar 11.
  60. Hoy MB. 3D printing: making things at the library. Med Ref Serv Q. 2013;32(1):93–99.
  61. Levy GN, Schindel R, Kruth JP. Rapid manufacturing and rapid tooling with layer manufacturing (LM) technologies, state of the art and future perspectives. CIRP Ann-Manuf Techn. 2003;52(2):589–609.
  62. Hieu LC, Zlatov N, Vander Sloten J, Bohez E, Khanh L, Binh PH, Oris P, Toshev Y. Medical rapid prototyping applications and methods. Assembly Autom. 2005;25(4):284–92.
  63. Chae MP, Lin F, Spychal RT, Hunter-Smith DJ, Rozen WM. 3D-printed haptic ‘reverse’ models for preoperative planning in soft tissue reconstruction: a case report. Microsurgery. 2015;35(2):148–53.
  64. Garcia-Tutor E, Romeo M, Chae MP, Hunter-Smith DJ, Rozen WM. 3D Volumetric modeling and microvascular reconstruction of irradiated lumbosacral defects after oncologic resection. Front Surg. 2016;3:66.
  65. Mavili ME, Canter HI, Saglam-Aydinatay B, Kamaci S, Kocadereli I. Use of three-dimensional medical modeling methods for precise planning of orthognathic surgery. J Craniofac Surg. 2007;18(4):740–47.
  66. Cohen A, Laviv A, Berman P, Nashef R, Abu-Tair J. Mandibular reconstruction using stereolithographic 3-dimensional printing modeling technology. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2009;108(5):661–66.
  67. Watson RA. A low-cost surgical application of additive fabrication. J Surg Educ. 2014;71(1):14–17.
  68. Olszewski R, Szymor P, Kozakiewicz M. Accuracy of three-dimensional, paper-based models generated using a low-cost, three-dimensional printer. J Craniomaxillofac Surg. 2014;42(8):1847–52
  69. Crump SS, inventor. Apparatus and method for creating three-dimensional objects. United States patent US 5,121,329. 1992 Jun 9.
  70. Wang YT, Yu JH, Lo LJ, Hsu PH, Lin CL. Developing customized dental miniscrew surgical template from thermoplastic polymer material using image superimposition, CAD system, and 3D printing. Biomed Res Int. 2017;1906197.
  71. Wang D, Wang Y, Wang J, Song C, Yang Y, Zhang Z, Lin H, Zhen Y, Liao S. Design and fabrication of a precision template for spine surgery using selective laser melting (SLM). Materials (Basel). 2016;9(7):E608.
  72. Yang F, Chen C, Zhou Q, Gong Y, Li R, Li C, Klampfl F, Freund S, Wu X, Sun Y, Li X, Schmidt M, Ma D, Yu Y. Laser beam melting 3D printing of Ti6Al4V based porous structured dental implants: fabrication, biocompatibility analysis and photoelastic study. Sci Rep. 2017;7:45360.
  73. Coelho G, Chaves TMF, Goes AF, Del Massa EC, Moraes O, Yoshida M. Multimaterial 3D printing preoperative planning for frontoethmoidal meningoencephalocele surgery. Childs Nerv Syst. 2017;34(4):749–56.
  74. Chen X, Possel JK, Wacongne C, van Ham AF, Klink PC, Roelfsema PR. 3D printing and modelling of customized implants and surgical guides for non-human primates. J Neurosci Methods. 2017;286:38–55.
  75. Egger J, Gall M, Tax A, Ucal M, Zefferer U, Li X et al. Interactive reconstructions of cranial 3D implants under MeVisLab as an alternative to commercial planning software. PLoS One. 2017;12(3):e0172694.
  76. Skrzat J, Spulber A, Walocha J. Three-dimensional model of the skull and the cranial bones reconstructed from CT scans designed for rapid prototyping process. Folia Med Cracov. 2016;56(2):45–52.
  77. Anderson JR, Thompson WL, Alkattan AK, Diaz O, Klucznik R, Zhang YJ et al. Three-dimensional printing of anatomically accurate, patient specific intracranial aneurysm models. J Neurointerv Surg. 2016;8(5):517–20.
  78. Ploch CC, Mansi C, Jayamohan J, Kuhl E. Using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. World Neurosurg. 2016;90:668–74.
  79. Park EK, Lim JY, Yun IS, Kim JS, Woo SH, Kim DS, Shim KW. Cranioplasty enhanced by three-dimensional printing: custom-made three-dimensional-printed titanium implants for skull defects. J Craniofac Surg. 2016;27(4):943–49.
  80. Kimura T, Morita A, Nishimura K, Aiyama H, Itoh H, Fukaya S, Sora S, Ochiai C. Simulation of and training for cerebral aneurysm clipping with 3-dimensional models. Neurosurgery. 2009;65(4):719–25; discussion 25–26.
  81. Wu TY, Lin HH, Lo LJ, Ho CT. Postoperative outcomes of two- and three-dimensional planning in orthognathic surgery: a comparative study. J Plast Reconstr Aesthet Surg. 2017;70(8):1101–111.
  82. Callahan AB, Campbell AA, Petris C, Kazim M. Low-cost 3D printing orbital implant templates in secondary orbital reconstructions. Ophthal Plast Reconstr Surg. 2017;33(5):376–80.
  83. LoPresti M, Daniels B, Buchanan EP, Monson L, Lam S. Virtual surgical planning and 3D printing in repeat calvarial vault reconstruction for craniosynostosis: technical note. J Neurosurg Pediatr. 2017;19(4):490–94.
  84. Kim YC, Jeong WS, Park TK, Choi JW, Koh KS, Oh TS. The accuracy of patient specific implant prebented with 3D-printed rapid prototype model for orbital wall reconstruction. J Craniomaxillofac Surg. 2017;45(6):928–36.
  85. Huang YH, Seelaus R, Zhao L, Patel PK, Cohen M. Virtual surgical planning and 3D printing in prosthetic orbital reconstruction with percutaneous implants: a technical case report. Int Med Case Rep J. 2016;9:341–45.
  86. Sutradhar A, Park J, Carrau D, Nguyen TH, Miller MJ, Paulino GH. Designing patient-specific 3D printed craniofacial implants using a novel topology optimization method. Med Biol Eng Comput. 2016;54(7):1123–135.
  87. Park SW, Choi JW, Koh KS, Oh TS. Mirror-imaged rapid prototype skull model and pre-molded synthetic scaffold to achieve optimal orbital cavity reconstruction. J Oral Maxillofac Surg. 2015;73(8):1540–553.
  88. Mendez BM, Chiodo MV, Patel PA. Customized ‘in-office’ three-dimensional printing for virtual surgical planning in craniofacial surgery. J Craniofac Surg. 2015;26(5):1584–586.
  89. Al Jabbari O, Abu Saleh WK, Patel AP, Igo SR, Reardon MJ. Use of three-dimensional models to assist in the resection of malignant cardiac tumors. J Card Surg. 2016;31(9):581–83.
  90. Olivieri LJ, Su L, Hynes CF, Krieger A, Alfares FA, Ramakrishnan K, Zurakowski D, Marshall MB, Kim PC, Jonas RA, Nath DS. ‘Just-in-time’ simulation training using 3-D printed cardiac models after congenital cardiac surgery. World J Pediatr Congenit Heart Surg. 2016;7(2):164–68.
  91. Osagie L, Shaunak S, Murtaza A, Cerovac S, Umarji S. Advances in 3D modeling: preoperative templating for revision wrist surgery. Hand (NY). 2017;12(5):NP68–72.
  92. Spottiswoode BS, van den Heever DJ, Chang Y, Engelhardt S, Du Plessis S, Nicolls F, Hartzenberg HB, Gretschel A. Preoperative three-dimensional model creation of magnetic resonance brain images as a tool to assist neurosurgical planning. Stereotact Funct Neurosurg. 2013;91(3):162–69.
  93. Igami T, Nakamura Y, Hirose T, Ebata T, Yokoyama Y, Sugawara G, Mizuno T, Mori K, Nagino M. Application of a three-dimensional print of a liver in hepatectomy for small tumors invisible by intraoperative ultrasonography: preliminary experience. World J Surg. . 2014;38(12):3163–6.
  94. Ikegami T, Maehara Y. Transplantation: 3D printing of the liver in living donor liver transplantation. Nat Rev Gastroenterol Hepatol. 2013;10(12):697–98.
  95. Zein NN, Hanouneh IA, Bishop PD, Samaan M, Eghtesad B, Quintini C, Miller C, Yerian L, Klatte R. Three-dimensional print of a liver for preoperative planning in living donor liver transplantation. Liver Transpl. 2013;19(12):1304–310.
  96. Chan HH, Siewerdsen JH, Vescan A, Daly MJ, Prisman E, Irish JC. 3D rapid prototyping for otolaryngology-head and neck surgery: applications in image-guidance, surgical simulation and patient-specific modeling. PLoS One. 2015;10(9):e0136370.
  97. Mowry SE, Jammal H, Myer Ct, Solares CA, Weinberger P. A novel temporal bone simulation model using 3d printing techniques. Otol Neurotol. 2015;36(9):1562–565.
  98. Barth RJ Jr, Krishnaswamy V, Paulsen KD, Rooney TB, Wells WA, Rizzo E, Angeles CV, Marotti JD, Zuurbier RA, Black CC. A patient-specific 3D-printed form accurately transfers supine MRI-derived tumor localization information to guide breast-conserving surgery. Ann Surg Oncol. 2017;24(10):2950–956.
  99. Murphy SV, Atala A. 3D bioprinting of tissues and organs. Nat Biotechnol. 2014;32(8):773–85.
  100. Chae MP, Hunter-Smith DJ, Murphy SV, Findlay M. 3D bioprinting adipose tissue for breast reconstruction. In: Thomas DJ, Jessop ZM, Whitaker IS, editors. 3D Bioprinting for Reconstructive Surgery: Techniques and Applications. Sawston, Cambridge, UK: Woodhead Publishing, 2017.
  101. Chae MP, Hunter-Smith DJ, Murphy SV, Atala A, Rozen WM. 3D bioprinting in nipple-areolar complex reconstruction. In: Shiffman MA, editor. Nipple-Areolar Complex Reconstruction: Principles and Clinical Techniques. Heidelberg, Germany: Springer, 2016.
  102. Gillis JA, Morris SF. Three-dimensional printing of perforator vascular anatomy. Plast Reconstr Surg. 2014;133(1):80e–82e.
  103. Mehta S, Byrne N, Karunanithy N, Farhadi J. 3D printing provides unrivalled bespoke teaching tools for autologous free flap breast reconstruction. J Plast Reconstr Aesthet Surg. 2016;69(4):578–80.
  104. Suarez-Mejias C, Gomez-Ciriza G, Valverde I, Parra Calderon C, Gomez-Cia T. New technologies applied to surgical processes: virtual reality and rapid prototyping. Stud Health Technol Inform. 2015;210:669–71.
  105. Cabalag MS, Chae MP, Miller GS, Rozen WM, Hunter-Smith DJ. Use of three-dimensional printed ‘haptic’ models for preoperative planning in an Australian plastic surgery unit. ANZ J Surg. 2017;87(12):1057–059.
  106. Taylor EM, Iorio ML. Surgeon-based 3D printing for microvascular bone flaps. J Reconstr Microsurg. 2017;33(6):441–45.
  107. Visscher DO, van Eijnatten M, Liberton N, Wolff J, Hofman MBM, Helder MN , Don Griot JPW, Zuijlen PPMV. MRI and additive manufacturing of nasal alar constructs for patient-specific reconstruction. Sci Rep. 2017;7(1):10021.
  108. Choi YD, Kim Y, Park E. Patient-specific augmentation rhinoplasty using a three-dimensional simulation program and three-dimensional printing. Aesthet Surg J. 2017;37(9):988–98.
  109. Bosc R, Hersant B, Carloni R, Niddam J, Bouhassira J, De Kermadec H, Bequignon E, Wojcik T, Julieron M, Meningaud JP. Mandibular reconstruction after cancer: an in-house approach to manufacturing cutting guides. Int J Oral Maxillofac Surg. 2017;46(1):24–31.
  110. Ganry L, Quilichini J, Bandini CM, Leyder P, Hersant B, Meningaud JP. Three-dimensional surgical modelling with an open-source software protocol: study of precision and reproducibility in mandibular reconstruction with the fibula free flap. Int J Oral Maxillofac Surg. 2017;46(8):946–57.
  111. Liang Y, Jiang C, Wu L, Wang W, Liu Y, Jian X. Application of combined osteotomy and reconstruction pre-bent plate position (CORPPP) technology to assist in the precise reconstruction of segmental mandibular defects. J Oral Maxillofac Surg. 2017;75(9):2026 e1–10.
  112. Mottini M, Seyed Jafari SM, Shafighi M, Schaller B. New approach for virtual surgical planning and mandibular reconstruction using a fibula free flap. Oral Oncol. 2016;59:e6–9.
  113. Schouman T, Khonsari RH, Goudot P. Shaping the fibula without fumbling: the SynpliciTi customised guide-plate. Br J Oral Maxillofac Surg. 2015;53(5):472–73.
  114. Seruya M, Fisher M, Rodriguez ED. Computer-assisted versus conventional free fibula flap technique for craniofacial reconstruction: an outcomes comparison. Plast Reconstr Surg. 2013;132(5):1219–228.
  115. Rohner D, Bucher P, Hammer B. Prefabricated fibular flaps for reconstruction of defects of the maxillofacial skeleton: planning, technique, and long-term experience. Int J Oral Maxillofac Implants. 2013;28(5):e221–29.
  116. Saad A, Winters R, Wise MW, Dupin CL, St Hilaire H. Virtual surgical planning in complex composite maxillofacial reconstruction. Plast Reconstr Surg. 2013;132(3):626–33.
  117. Hanasono MM, Skoracki RJ. Computer-assisted design and rapid prototype modeling in microvascular mandible reconstruction. Laryngoscope. 2013;123(3):597–04.
  118. 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):743–49.
  119. Infante-Cossio P, Gacto-Sanchez P, Gomez-Cia T, Gomez-Ciriza G. Stereolithographic cutting guide for fibula osteotomy. Oral Surg Oral Med Oral Pathol Oral Radiol. 2012;113(6):712–13.
  120. Zheng GS, Su YX, Liao GQ, Chen ZF, Wang L, Jiao PF, Liu HC, Zhong YQ, Zhang TH, Liang YJ. Mandible reconstruction assisted by preoperative virtual surgical simulation. Oral Surg Oral Med Oral Pathol Oral Radiol. 2012;113(5):604–11.
  121. Hou JS, Chen M, Pan CB, Wang M, Wang JG, Zhang B, Tao Q, Wang C, Huang HZ. Application of CAD/CAM-assisted technique with surgical treatment in reconstruction of the mandible. J Craniomaxillofac Surg. 2012;40(8):e432–37.
  122. Antony AK, Chen WF, Kolokythas A, Weimer KA, Cohen MN. Use of virtual surgery and stereolithography-guided osteotomy for mandibular reconstruction with the free fibula. Plast Reconstr Surg. 2011;128(5):1080–084.
  123. Leiggener C, Messo E, Thor A, Zeilhofer HF, Hirsch JM. A selective laser sintering guide for transferring a virtual plan to real time surgery in composite mandibular reconstruction with free fibula osseous flaps. Int J Oral Maxillofac Surg. 2009;38(2):187–92.
  124. Greene JR. Design and development of a new facility for teaching and research in clinical anatomy. Anat Sci Educ. 2009;2(1):34–40.
  125. Raja DS, Sultana B. Potential health hazards for students exposed to formaldehyde in the gross anatomy laboratory. J Environ Health. 2012;74(6):36–40.
  126. Mogali SR, Yeong WY, Tan HKJ, Tan GJS, Abrahams PH, Zary N, Low-Beer N, Ferenczi MA. Evaluation by medical students of the educational value of multi-material and multi-colored three-dimensional printed models of the upper limb for anatomical education. Anat Sci Educ. 2018;11(1):54-64
  127. Lioufas PA, Quayle MR, Leong JC, McMenamin PG. 3D printed models of cleft palate pathology for surgical education. Plast Reconstr Surg Glob Open. 2016;4(9):e1029.
  128. Zheng Y, Lu B, Zhang J, Wu G. CAD/CAM silicone simulator for teaching cheiloplasty: description of the technique. Br J Oral Maxillofac Surg. 2015;53(2):194–96.
  129. AlAli AB, Griffin MF, Calonge WM, Butler PE. Evaluating the use of cleft lip and palate 3D-printed models as a teaching aid. J Surg Educ. 2018;75(1):200–08.
  130. Berens AM, Newman S, Bhrany AD, Murakami C, Sie KC, Zopf DA. Computer-aided design and 3D printing to produce a costal cartilage model for simulation of auricular reconstruction. Otolaryngol Head Neck Surg. 2016;155(2):356–59.
  131. Blackshear CP, Rector MA, Chung NN, Irizarry DM, Flacco JS, Brett EA, Momeni A, Lee GK, Longaker MT, Wan DC. Three-dimensional ultrasound versus computerized tomography in fat graft volumetric analysis. Ann Plast Surg. 2018;80(3):293–96.
  132. Frueh FS, Korbel C, Gassert L, Muller A, Gousopoulos E, Lindenblatt N, Giovanoli P, Laschke MW, Menger MD. High-resolution 3D volumetry versus conventional measuring techniques for the assessment of experimental lymphedema in the mouse hindlimb. Sci Rep. 2016;6:34673.
  133. Roxo AC, Nahas FX, Bazi F, de Castro CC, Aboudib JH, Marques RG. Evaluation of the effects of silicone implants on the breast parenchyma. Aesthet Surg J. 2015;35(8):929–35.
  134. Roxo AC, Nahas FX, Pinheiro Rodrigues NC, Salles JI, Amaral Cossich VR, de Castro CC, Aboudib JH, Marques RG. Functional and volumetric analysis of the pectoralis major muscle after submuscular breast augmentation. Aesthet Surg J. 2017;37(6):654–61.
  135. Corey CL, Popelka GR, Barrera JE, Most SP. An analysis of malar fat volume in two age groups: implications for craniofacial surgery. Trauma Reconstr. 2012;5(4):231–34.
  136. Chae MP, Hunter-Smith DJ, De-Silva I, Tham S, Spychal RT, Rozen WM. Four-dimensional (4D) printing: a new evolution in computed tomography-guided stereolithographic modeling. Principles and application. J Reconstr Microsurg. 2015;31(6):458–63.