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M. Ghazizadeh Ahsaie and H. Farajpour
requires image processing with compatible software to segment and reconstruct the
desired anatomy. The segmented anatomy should be conrmed by radiologist and
clinicians to ensure the precision of designs and models.
3 Image Processing
Today, different 3D software are available, being widely used in maxillofacial
reconstructions, namely: Mimics® (Materialise, Leuven, 3001, Belgium), CMF
ProPlan (Materialise, Leuven, 3001, Belgium), SurgiCase® (Materialise, Leuven,
3001, Belgium), SimPlant® (Dentsply, York, 17401, USA), Nobel GuideTM (Nobel
Biocare, Zürich-Flughafen, CH-8058, Switzerland), iPlan (BrainLab AG,
Feldkirchen, 85622, Germany), VoXim® (IVS Technology GmbH, Chemnitz,
09125, Germany), and Analyze (AnalyzeDirect, Inc., Overland Park, 66085, USA).
The CT or CBCT images can enter these software in both DICOM and STL format
for optical scanners. Mimics software is one of the most utilized tools for maxillofacial reconstructions. The software platform consists of three orthogonal MPR sections providing axial, coronal, and sagittal views (Fig.2). In addition, a 3D surface
rendered view of the image volume is also presented upon segmentation.
The imported data can be segmented to a specic region of interest (ROI). For
instance, to create a model of maxillary bone, gray values that represent the maxilla
should be separated from the rest of the image volume. Segmentation can be fully
automatic, semiautomatic, or manual. The most popular and well-known
a
b
d
c
Fig. 2 Main platform in Mimics® (Materialise, Leuven, 3001, Belgium) software. Coronal (a),
axial (b), and sagittal (c) CBCT images are shown from imported DICOM data set. (d) 3D view is
still vacant and is shown when segmentation is initiated
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segmentation method used to date is automatic thresholding [11]. CT images from
maxillofacial skeleton can dene structures primarily by Hounseld unit (HU)
thresholding; however, soft tissue and neurovascular anatomy needs additional segmentation methods. Some software provide inset default threshold range for various
tissues (Fig.3). When using this thresholding method, tissues with the same gray
value pixel intensity are shown simultaneously. To remove unrelated and unnecessary anatomy, one can use the option region growing tool. Region growing
c
d
e
Fig. 3 Selection of specic threshold range results in demonstration of corresponding gray values.
By sliding the graph on the bottom to the right and left, lower pixel values and higher pixel values
are demonstrated, respectively. Threshold selection is a subjective task. (a) Soft tissue, (b) soft
tissue and bone, (c) bone and teeth, (d) enamel and dentin, and (e) enamel (OnDemand3D,
Cybermed, Seoul, Korea)
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segmentation separates pixels that comprise an ROI.Initial seed points are inserted
in the desired ROI, and the computer algorithm compares the gray values of the
neighboring pixels and, when similar, adds those pixels to expand the seed. The
process is iterative and progresses until no more pixels can be added. Following
region growing segmentation, operator input is often needed to conrm the boundaries of the selected mask. Split mask technique can be performed when separation
of connected adjacent anatomies is required. The operator can also combine pixels,
modify boundaries, or erase regions manually (Figs.4 and 5). A binary keep or
discard is assigned to each voxel to modify boundaries [12]. Segmentation software
also allow understanding the anatomies to better extent, especially in case of bone
defects or dental trauma. The more accurate the segmentation, the more precise the
3D printed model. In case of maxillofacial unilateral defect reconstruction,
a
Fig. 4 Image processing requires multiple steps to calculate a 3D model. (a) Selection of a pre-
dened threshold set using inbuilt thresholds within Mimics. Note the difference in default threshold value for enamel, compact bone, and soft tissue in adult and child patient. For maxillofacial
bone, the threshold ranges from 226 HU to approximately 3071 HU.Thresholding is applied to the
entire dataset and delineates pixels based on their gray values but not on spatial location. (b)
Region growing segmentation is applied to separate pixels that comprise a region of interest (ROI).
One or more seeds are selected within the desired anatomy, and neighboring connected pixels are
added automatically by including same gray values. (c) Split mask technique aids in separation of
adjacent anatomical structures with the same pixel values. In this case, the mandible is separated
from other osseous structures. (d) If any errors in segmentation is detected, the selected mask can
be manually edited. (f) By conrming the segmented anatomy, the 3D model is calculated
automatically
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e
f
Fig. 4 (continued)
mirroring will improve the symmetry of the results. Therefore, after automatic
thresholding, manual threshold selection is necessary to acquire an optimal STL
model. Defects and deformities in the segmentation directly present as inaccuracies
in the 3D printed model (Fig.6). Eijnatten etal. indicated that manual thresholding
resulted in better STL model formation compared to default thresholding. Presence
of artifacts may distort the results of segmentation. Recent studies have shown that
most errors and imperfections in the nal printed 3D model are due to introduced
errors in the image acquisition and image processing phases, rather than during the
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a
Fig. 5 Segmentation of upper airway volume. (a) Seeding points are manually inserted within the
airway space on sagittal view. Iteration region growing is performed three-dimensionally on (b)
axial, (c) sagittal, and (e) coronal images by adding pixels with similar gray values to the seeding
points. (d) The 3D volumetric image is generated, and the volume can be calculated (ITK-SNAP
(http://itksnap.org/))
3D printing itself. Novel methods of segmentation like multi-thresholding, adaptive
thresholding, and machine learning algorithms can improve the results of segmentation. However, software cannot differentiate between MDCT and CBCT and therefore do not take the inherent differences into account [11] .
In some cases, reconstruction of vascular anatomy is performed using MRI
DICOM data. Choice of sequence and injection of contrast medium determines the
tissue contrast [12]. Although MRI is a nonionizing modality and can benet patient,
threshold-based segmentation is more complex than CT.MRI does not have pixel
values, and overlapping between adjacent tissues is detected [13]. When a 3D
printed model is required, the calculated surface volumetric dataset is broken down
into small triangles that tile the surface, and therefore, a STL le format is
constructed.
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Fig. 6 3D Slicer software (Brigham and Women’s Hospital, Boston, MA) platform for medical
image processing and 3D visualization of image data. (a) Axial, (b) coronal, and (c) sagittal CBCT
DICOM data of a patient with impacted maxillary canine with root resorption in adjacent lateral
tooth is imported to the software. Segmentation process on lateral incisor produces an individual
tooth mask (d) and shows no signs of root resorption
4 STL File Preparation
DICOM images are converted to another le format as printers do not accept
DICOM images. 3D printers understand individual objects. The most widely used
le format for 3D printing is Standard Tessellation Language also known as
STL.The STL format denes collection of triangle surfaces, known as “facets,” that
t together without any gap or overlap (Fig.7).
Only certain software packages currently can create STL le. Free software
such as 3D Slicer (Brigham and Women’s Hospital, Boston, MA), OSIRIX
(Pixmeo, Geneva, Switzerland), and ITK-SNAP (http://itksnap.org/) as well as
other software such as Mimics® (Materialise, Leuven, 3001, Belgium) provide
STL le formats.
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Fig. 7 Standard Tessellation Language (STL). (a) Schematic image of triangular surfaces matched
together, (b) STL le of maxillofacial skeleton of a fully edentulous patient represents the surface
as triangular facets, without any gaps or overlaps (Courtesy Dr. Mina Iranparvar Alamdari, Shahid
Beheshti University of Medical Sciences, Tehran, Iran)
M. Ghazizadeh Ahsaie and H. Farajpour
STL les should be veried to ensure anatomical accuracy prior to importation
to the 3D printers. The study of Visscher etal., 2016, demonstrated that the MRI
data converted to STL le introduced up to 1.5mm geometrical deviations in STL
le compared to 1.0mm for CT images [14].
5 Three-Dimensional (3D) Printing
Three-dimensional (3D) printing, also known as “rapid prototyping,” “additive
manufacturing,” “layer manufacturing,” and “additive layer manufacturing,” is
referred to several techniques that is used to create a 3D model based on the STL
le provided by computer-assisted design. Medical 3D printing comprises ve
types of techniques (vat photopolymerization, material jetting, binder jetting,
material extrusion, and powder bed fusion) to create models, implants, and
devices. In this emerging technique, a stack of layers is printed one by one forming the desired object.
One of the rst and most popular applications of 3D printing in the medical eld
is to reconstruct maxillofacial and craniofacial defects caused by pathologic conditions of the head and neck (RSNA). The STL le is usually obtained from MDCT
or CBCT data of maxillofacial region and is further imported to specic 3D printers
based on surgical needs and treatment plan (Fig.8). Thinner slice thickness and
isotropic voxels ensure good-quality 3D printed models from these cross-sectional
imaging. Custom- made implants, drill guides, surgical guides, splints, and
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Fig. 8 Various 3D printed models using different additive manufacturing materials and printers.
(a–c) Intermediate and nal splint in orthognathic surgery, (d–f) mandibular titanium prosthesis
for reconstruction of bony defects
f
prosthesis can be printed with various materials according to patients’ specic anatomy [15]. In addition, 3D printing provides a novel method in biomedical research
and medical education and enables improved education procedure for students and
patients [16].
A number of applications of 3D printing from MRI data have also been presented
in previous studies, such as models of blood vessels, nervous system, heart, brain,
kidney, and prostate. MRI can reduce the use of ionizing radiation especially in
developing children. The earliest endeavors were conducted by Markl etal., who 3D
printed a phantom of aortic vasculature using MRI DICOM data [17]. Current material jetting 3D printing technologies can be used to print anatomically accurate
phantoms that can be imaged using both CT and MRI [10]. Eley etal. 3D printed
model of the mandible and entire craniofacial skeleton using a rapid gradient-echo
acquisition technique also known as black bone MRI dataset [18]. The orbital and
maxillo-mandibular heights on the MRI images were compared to 3D printed
model, and the results suggested an average difference of less than 0.5mm. However,
in the study of Mitsouras etal., the dimensional accuracy of printed models from
MRI and CT was assessed and showed that MRI results in much larger differences
in the phantom’s dimensions compared with the CT [10, 19].
In vivo MRI has a resolution of approximately 1mm; therefore, if submillimeter
accuracy is needed for precision sizing, MDCT (resolution of approximately
0.3mm) or CBCT (resolution of approximately 0.1mm) should be considered as an
alternative [12].
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6 Clinical Cases
6.1 Craniofacial andMaxillofacial Defect Reconstruction
inTraumatic Cases (Fig.9)
Incorporation of three-dimensional imaging, notably MDCT and CBCT, in complex
trauma to head and face is essential as it provides accurate information on the location
and direction of fracture lines, displaced bones, and soft tissue complications [20]. CAD
designed models aid the surgeon to foresee the treatment plan, perform mock surgeries,
and anticipate any potential problem that may be encountered at the time of surgery
when restoring the traumatic defects. 3D printed prosthesis for maxillofacial defects can
effectively adjust to the defect site and decrease intraoperative time. If the structure is
porous, particulate bone graft may be used for further bone regeneration [21].
a
b
Fig. 9 Craniofacial reconstruction in a 49-year-old male referred with history of vehicle accident.
Right parietal and parts of frontal bone were destructed, and the right zygomatic bone was dislocated
distally. (a) DICOM data obtained from CBCT images were imported to Mimics® (Materialise,
Leuven, 3001, Belgium) software. (b) The right zygomatic bone was manually segmented and repositioned in its proper anatomical location. To construct the defect in right frontal and parietal bone, (c) the
skull was mirrored and (d) reconstructed from the normal left side. The frontal and parietal bone defects
were designed separately using porous prostheses. The STL le was further sent for 3D printing
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Fig. 9 (continued)
6.2 Maxillofacial Defect Reconstruction inTumor Cases
(Fig.10)
Maxillofacial tumors are often encountered by surgeons and requires thorough clinical and imaging assessments especially with volumetric imaging. The tumor usually needs to be respected, and defect should be reconstructed based on its size,
location, and remaining osseous walls at the periphery. Preoperative virtual planning helps the clinicians to better understand the resecting guiding plans, virtually
perform mandibulectomy or maxillectomy, and further assess the spatial relationship of the resection defect site with the designed prosthesis to obtain standard
occlusion, without compromising the beauty [22–24].
6.3 Careful Assessment ofMaxillofacial Vital Anatomic
Structures (Fig.11)
When performing maxillofacial surgeries, the clinician should assess the exact location of adjacent vital anatomic structures, namely, neurovascular bundles, and anticipate potential intraoperative limitations and risk to these structures. The
three-dimensional data obtained from CT can be astutely examined, and 3D models
can be provided to perform mock surgeries, considering crucial landmarks.
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