|Year : 2017 | Volume
| Issue : 2 | Page : 86-92
Patient-specific 3D printed model in delineating brain glioma and surrounding structures in a pediatric patient
Ivan Lau1, Andrew Squelch2, Yung Liang Wan3, Alex Mun-Chung Wong3, Werner Ducke4, Zhonghua Sun1
1 Department of Medical Radiation Sciences, Curtin University, Kensington, Western Australia
2 Department of Exploration Geophysics, Western Australian School of Mines, Curtin University; Pawsey Supercomputing Centre, Kensington, Western Australia
3 Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan
4 Research and Development, Cook Medical, Brisbane, Australia
|Date of Web Publication||18-Sep-2017|
Department of Medical Radiation Sciences, Curtin University, GPO Box, U1987, Perth, Western Australia 6845
Source of Support: None, Conflict of Interest: None
Background and Objectives: Three-dimensional (3D) printing has been increasingly used in medicine with applications in the diagnostic assessment of disease extent, medical education and training, preoperative planning, and surgical simulation. The use of 3D printing in brain tumors is very limited. In this study, we presented our preliminary experience of creating patient-specific 3D printed model of a brain tumor in a pediatric patient and demonstrated the feasibility of using 3D printing in delineating brain anatomy and tumor. Materials and Methods: A life-size 3D printed brain model of a 6-year-old girl, who was diagnosed with pilocytic astrocytoma, was generated. The model was created using high-resolution magnetic resonance images which were postprocessed and segmented to demonstrate normal anatomical structures and the tumor. The tumor was confirmed to be Grade I pilocytic astrocytoma after neurosurgery. Results: 3D printed model was found to provide realistic visualization of brain anatomical structures and tumor, and enhance understanding of pathology in relation to the surrounding structures. The mean difference in diameter measurements of the brain tumor was 0.53 mm (0.98%) between the 3D printed model and computerized model. Conclusions: This study shows it is feasible to generate a 3D printed model of brain tumor with encouraging results achieved to replicate brain anatomy and tumor. 3D printed model of brain tumor could serve as an excellent tool for preoperative planning and simulation of surgical procedures, which deserve to be investigated in further studies.
Keywords: Diagnosis, glioma, model, three-dimensional printing
|How to cite this article:|
Lau I, Squelch A, Wan YL, Wong AM, Ducke W, Sun Z. Patient-specific 3D printed model in delineating brain glioma and surrounding structures in a pediatric patient. Digit Med 2017;3:86-92
|How to cite this URL:|
Lau I, Squelch A, Wan YL, Wong AM, Ducke W, Sun Z. Patient-specific 3D printed model in delineating brain glioma and surrounding structures in a pediatric patient. Digit Med [serial online] 2017 [cited 2023 Jan 28];3:86-92. Available from: http://www.digitmedicine.com/text.asp?2017/3/2/86/215030
| Introduction|| |
Three-dimensional (3D) printing technology has shown increasing use in medicine in recent years, such as creating customized prosthetics, implants, fixtures, and surgical tools as well as reproducing patient-specific 3D printed models for surgical preparation.,, Previous studies have shown the value of 3D printing in neurosurgery for both surgical and nonsurgical purposes.,, From a study carried out by Müller et al., the 3D printed brain models were shown to enhance understanding of the anatomy, allow presurgical simulation, increase intraoperative accuracy in localization of lesions, enable accurate fabrication of implants, and improve education of trainees.
However, most of the studies available in the literature are focused on the use of 3D printing models in patient-specific prosthetics, cardiovascular disease, and maxillofacial surgery as well as aneurysm surgery,,, while reports on 3D printing of brain tumor are scarce. A recent systematic review analyzed 48 studies about the applications of 3D printed models in cardiovascular and cerebral vascular diseases. In addition to the high accuracy of replicating complex cardiovascular anatomy and pathology, patient-specific 3D printed models were shown to serve as a useful tool for presurgical planning and simulation according to the review. Two representative studies reported the 3D printed models in planning surgical repair of cerebral aneurysms., Mashiko et al. created twenty hollow elastic models of cerebral aneurysms with simulated clipping surgery performed in 12 cases. Responses from 12 experienced surgeons and six junior surgeons who participated in the questionnaire were favorable with regard to the usefulness of 3D printed models in presurgical planning, their ability of understanding the structure of aneurysm in relation to surrounding structures, and the training tool for inexperienced operators to clip an aneurysm. Namba et al. also used ten hollow 3D printed models of cerebral aneurysms to determine the preplanned shape of microcatheter for assisting design of catheter devices. Their results demonstrated the value of 3D printed models in accurate and stable catheter designing with optimal microcatheter shape determined in all of the 10 models prior to preoperative coiling of the aneurysm.
Vakharia et al. conducted a systematic review of 3D printing on cranial neurosurgery simulation. Of 31 studies related to 3D printing applications in neurosurgery, 16 were eligible for analysis with regard to the impact of 3D printing on simulation and training. Their analysis confirmed the beneficial effects of using 3D printed models in preoperative simulation and training of cerebral vascular diseases, in particular in the cerebral aneurysms. There is an increasing evidence to prove that 3D printing is a powerful tool to significantly impact the neurosurgical practice and training, although more studies are needed to verify its clinical outcomes in the vascular neurosurgery.
There are only a few studies reporting the usefulness of 3D printing in brain tumors, and they are mainly performed by the same research group.,,, Thus, the aim of this study was to investigate the feasibility and clinical value of using 3D printed brain model as a tool in facilitating the neurosurgical planning of glioma in pediatric patients. We present our experience of fabricating a life-size 3D model based on imaging data from a 6-year-old girl with pilocytic astrocytoma and demonstrate the feasibility of depicting anatomical structures and the tumor.
| Materials and Methods|| |
Patient history and clinical diagnosis
A girl aged 6 years, 3 months had a history of epiphora and torticollis. She had chief complaint of bilateral lower leg pain for 2 months with subsequent unsteady gait. No nausea, vomiting, diplopia, dizziness, dysphagia, or dysarthria was noted. Neurological examination revealed that the patient had clear consciousness. The deep tendon reflex, muscle power, test on cranial nerves, finger to nose test and Romberg test was negative or unremarkable, but the patient had wide gaiting and was unable to walk on a line.
Ultrasonography of the brain revealed hydrocephalus and suspicious cerebellar lesion. Computed tomography (CT) of brain without and with contrast medium enhancement revealed a 5.1 cm lobulated mass with cystic and solid components in the right cerebellum. The mass compressed the fourth ventricle resulting in obstructive hydrocephalus. Magnetic resonance imaging (MRI) using various pulse sequences, multi-planar projections, without and with intravenous administration of Gadolinium showed that the solid part of tumor measured 5.1 cm × 3.3 cm × 3.8 cm, extending from the fourth ventricle floor through right foramen of Luschka into the cerebello-medullary cistern, premedullary cistern, cisterna magna and downward to the proximal spinal cord through foramen magnum [Figure 1]. Differential diagnoses of hemangioblastoma, pilocytic astrocytoma, medulloblastoma, and choroid plexus papilloma with lateral extension were considered.
|Figure 1: Magnetic resonance images showing the extent of pilocytic astrocytoma. Contrast-enhanced T1-weighted magnetic resonance images demonstrate a large mass with solid and cystic components located in the posterior cranial fossa with compression to the brain stem and ventricle systems. The tumor is ill-defined with enhancement in the solid components as shown in the axial (a-c) and sagittal MR images (d). Hydrocephalus is noted|
Click here to view
Neurosurgery and suboccipital craniotomy confirmed the MRI findings. The tumor was greyish and elastic with multi-cystic components. The tumor abutted the lower medulla and right cerebello-pontine angle; there was poor tissue plane to excise the entire tumor. The excised specimens from the right cerebellar tumor measured up to 2.3 cm × 2.0 cm × 1.0 cm in size. The final pathology and immunohistochemistry revealed that the tumor was a Grade I pilocytic astrocytoma according to the criteria of the World Health Organization.
Image data acquisition
MRI images were collected as the source data for 3D printing of the brain model. The scan was performed on a 1.5 T clinical MRI scanner (Magnetom Espree, Avanto, Siemens, Erlangen, Germany) with the following parameters: Repetition time = 1950 ms, echo time = 3.51 ms, inversion time = 1100 ms, flip angle = 15°, isotropic resolution 1.0 mm × 1.0 mm × 1.0 mm, matrix size = 224 × 256, number of slices = 191, field of view = 227 mm × 260 mm, pixel bandwidth = 130, scan time = 4:23. A standard dose (0.1 mmol/kg body weight) of gadopentetate dimeglumine (Magnevist, Bayer-Schering, Burgess Hill, UK) was administered intravenously for postcontrast MR images.
Original digital imaging and communications in medicine (DICOM) images were used for image processing and segmentation. Ethical approval was obtained from Human Research Ethics Committee.
Image segmentation for three-dimensional printing
To create a high-quality 3D printed brain model, there are three essential steps that need to be taken in image postprocessing: (1) Image analysis and segmentation, (2) Conversion from DICOM data to model mesh in standard tessellation language (STL) file format, and (3) clean-up and optimization of STL file to ensure the quality of 3D printed model.
The original MRI dataset in DICOM format was first imported into an open source software 3D Slicer for segmentation. 3D Slicer is a software platform for medical image analysis and visualization. Critical anatomical structures such as cerebellum, brain stem, and brain tumor were segmented using the threshold paint function [Figure 2]a. Painted structures that have the intensity value in between the threshold values were highlighted. The cerebellum and brain tumor were segmented separately to create 3D printed model in which the tumor could be dissembled from the cerebellum. The next step is to use the model maker function to create a computerized model of the highlighted structures [Figure 2]b. Before exporting the models in STL format, a Laplacian filter was applied to optimize the models. Following that, both STL files were optimized for 3D printing using Meshlab (Pisa, Italy), which is open-source software for viewing and editing 3D files. Tiny spikes and free-floating pieces that could have resulted from imaging artifacts or other body structures were removed in the optimization process.
|Figure 2: Screen-display of the (a) segmentation result of the cerebellum and brain stem using the threshold paint function in 3D Slicer (b) computerized model generated in 3D Slicer showing the brain tumor (red) and cerebellum (grey)|
Click here to view
Both the finalized cerebellum and brain tumor STL files were printed with a commercial Stratasys 3D printer (Objet Eden 260VS) using the PolyJet printing technology in VeroClear material. PolyJet printing is similar to inkjet printing; however, instead of jetting drops of ink onto paper, tiny droplets of liquid photopolymer are jetted onto the building tray and cured using ultraviolet light. The tumor was printed separately from the cerebellum so that the observers could dissemble them and assess the entirety of the tumor [Figure 3]. The total cost for 3D printing was around AUD $250.
|Figure 3: Three-dimensional printed brain model showing the cerebellum (greyish) and brain tumor (yellowish). (a) Anterior view of the model. (b) Posterior view of the model. (c) Dissembled brain tumor and cerebellum|
Click here to view
Measurements of anatomic accuracy
To investigate the accuracy of the 3D printed model, diameter of the brain tumor was measured from left to right (LR), from superior to inferior (SI), and from anterior to posterior (AP) using an electronic caliper (accuracy of 0.1 mm). These measurements were compared to the measurements obtained from the computerized 3D brain tumor model, which was measured using the measurement tool in 3D Slicer. As the shape of the tumor is not regular, the anatomical locations for measurements were identified based on the surface landmarks. Sharp tips on the surface of the tumor were chosen, and the distances between the tips were measured. The most prominent tip on each surface was chosen as the surface landmark for measurement so that the caliper can be used more efficiently. The measurements for each anatomical location were repeated for three times with the mean value calculated. [Figure 4] shows an example of how the measurements were taken on both the physical model and the computerized model.
|Figure 4: Measurement taken superior-inferiorly (a) on the computerized model and (b) on the three-dimensional printed model using corresponding anatomical landmark|
Click here to view
The absolute mean difference between the two measurements was calculated at all three anatomical landmarks. In this study, 0.5% deviation in diameter between the 3D printed brain tumor and computerized tumor was considered acceptable. This degree of variation is deemed to be negligible according to the study by Nizam et al.
| Results|| |
The 3D model of the brain tissue and the tumor was successfully printed out using the STL files generated from the MRI images. [Figure 3] shows the 3D printed model of cerebellum and brain stem with tumor closely attached to it. In addition, the tumor was detachable from the brain structures which aids in demonstrating the realistic relationship between the tumor and normal brain tissue.
In addition to providing excellent 3D demonstration of the brain tumor, 3D printed model was also shown to be anatomically accurate based on the quantitative assessment. The mean percentage difference in diameter measurements of the brain tumor between the 3D printed model and computerized model was 0.21 and 0.58% at LR and SI measurements, which is within or close to the 0.5% acceptance level. In contrast, the corresponding mean percentage difference was 2.20% at AP measurements which was beyond the tolerance of 0.5% [Table 1].
|Table 1: Comparison of measurements of the brain tumor diameters between the computerized model and three-dimensional printed model|
Click here to view
| Discussion|| |
The current treatment options for brain cancer are chemotherapy, radiotherapy, steroid therapy, craniotomy, or a combination of these treatments. If the surgical option is chosen, the neurosurgeons will usually plan the surgery utilizing MRI volumetric images. This neurosurgical planning step is crucial to determine the best and safest surgical approach in minimizing permanent damage to normal brain tissues. Although MRI images are superior in demonstrating the distinction between the brain tumor and normal brain tissues, it is difficult for the surgeons to infer the precise volume and dimension merely based on the medical images viewed on the two-dimensional screen.,, Such limitation could be resolved by creating graspable, realistic 3D printed model from the volumetric dataset to fully exploit the 3D potential of MRI scans.
This is confirmed by our preliminary report. The 3D printed physical model shows the brain tumor in relation to the surrounding structures with the tumor detachable from the brain tissue. Further research is warranted to investigate the clinical value of 3D printed models in presurgical planning of brain tumor.
Currently, only a few studies are available in the literature on the 3D printed models of brain tumors or neck tumors extending to the brain.,,,,, Wiedermann et al. created a 3D printed model in a 6-week-old infant with a giant cranio-cervicofacial teratoma diagnosed on the day three of life. Head and neck CT and MRI images showed a huge heterogeneous infratemporal mass with extension through the skull base generating significant mass effect on the left brain, compressing ventricular system with midline shifted to the right side. A 3D printed model was generated using both CT and MRI images to demonstrate different tissue types. The model was found to enhance preoperative planning and intraoperative navigation in this case due to its complex involvement with the surrounding structures such as skull base and vasculature. Waran et al. reported their experience of using multimaterial 3D printed models to represent more realistic feature of tissues for simulation of brain tumor resection. 3D models were created using CT data in a patient diagnosed with brain tumor. The models consisted of different tissue components including skin, bone, dura matter and the tumor. Authors also registered the 3D printed models with a navigation station for the purpose of simulating surgical procedure. Their models allowed the users (neurosurgeons and trainee) to plan the operation and perform basic steps of a craniotomy.
Waran et al. in their study further demonstrated the usefulness of 3D printed models for simulation of neurosurgical and neuroendoscopic procedure. They created 3D models using CT and MRI data in a patient with hydrocephalus due to a large pineal tumor. The models were scored outstanding (a score of 5 out of 5) by neurosurgeons for performing image guidance and surgical procedure due to its ability of representing very realistic environment and scored very satisfactory for conducting a biopsy procedure (an average score of 4 out of 5). Despite promising results, these models did not show the brain tumor in relation to the white matter tracts. This has been addressed by a recent study. Thawani et al. created impressive 3D printed models in three patients with low-grade gliomas and reported the novel application of 3D printing in delineating anatomic relationships between white matter tracts and brain tumors. High-resolution MRI images were used for image processing and segmentation, with corticospinal tract and corpus callosum specially identified as tracts in relation to the tumors. Their 3D models clearly demonstrated the spatial anatomy of brain tumors in relation to the surrounding subcortical white matter tracts which could play an important role in clinical decision making during surgical resection. Further, their 3D models can provide more accurate representation of the interface between the white matter tracts and the tumor.
There are several commercial software platforms and some open-source freeware packages available to perform the above steps including image processing and segmentation. The most commonly used commercial software is the Mimics Innovation Suite (Materialise, Leuven, Belgium), which includes a comprehensive set of segmentation and computer-aided design tools, while Osirix (Pixmeo, Geneva, Switzerland) or 3D Slicer offers free software for these steps.,,, An open-source software was used in this study, indicating that it is feasible to utilize widely accessible software as a segmentation tool. Nevertheless, the navigation and operation of the software is highly dependent on the expertise of the individuals, and it takes time to learn the functions of the software.
In terms of cost of the 3D printed model, it can be reduced by choosing a different printing material. However, most of the available 3D printing materials are rigid, which does not truly represent or mimic the texture of human brain. Very few studies reported the mechanical properties of the 3D printed models, according to a recent systematic review. Hence, these are the areas that need to be further researched and considered when applying 3D printing technology in creating realistic anatomical structures.
The dimensional accuracy of 3D printed models is a key to determine whether 3D printing can be safely implemented in facilitating preoperative planning of neurosurgery. In this study, the mean percentage difference in diameter of the 3D printed brain tumor model and the computerized model was 0.98% (range 0.21 and 2.20%), indicating that the 3D printed model does not accurately replicate the brain tumor in its entirety. Ideally, the geometry of the 3D printed model should be exactly identical to that of the real anatomy. However, according to Ogden et al., this is always not the case due to various factors that could directly affect the dimensional accuracy of 3D printing. These include the quality of the source data set, reconstruction kernels that are used to reduce noise of the scan, methods of segmentation, surface extraction algorithm used and the resolution of the 3D printer. In this study, as the printed model is compared to the computerized STL model, the possible reason for the discrepancy in brain tumor diameter, in particular, in the AP dimensional measurements, could be due to error that was introduced during the printing process. Future research should focus on developing a standard method to generate 3D printed brain models while minimizing the errors that may result from the creation of the models.
There are also some limitations in this study. All measurements in this study was recorded by only one observer, hence the results are prone to observer bias. Further studies with inclusion of more cases and with measurements performed by two observers are necessary to enable robust conclusion to be drawn. In addition, the calculated mean difference in diameter can only roughly represent the dimensional accuracy of the model, as only three surface landmarks were measured in this study. More measurements comprising both normal brain anatomy and pathology would be desirable to allow assessment of degree of agreement in measurements between 3D reconstructions and 3D printed models.
| Conclusion|| |
This preliminary study shows that it is feasible to generate a 3D printed model of brain tumor. Although promising results are achieved for the 3D printed model to replicate brain tumor with dimensional accuracy, its diagnostic accuracy is also yet to be determined in future investigation, which is a key element for precise presurgical planning. Furthermore, future research based on more cases and with different types of brain tumor is needed to confirm its clinical value, and decide how the 3D printed models facilitate the neurosurgical planning and hence improve the surgical outcome.
Financial support and sponsorship
This study was supported by Cancer Council WA James Crofts Hope Foundation (JCHF) Vacation Scholarship.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Zuniga J, Katsavelis D, Peck J, Stollberg J, Petrykowski M, Carson A, et al
. Cyborg beast: A low-cost 3D-printed prosthetic hand for children with upper-limb differences. BMC Res Notes 2015;8:10.
Ebert J, Ozkol E, Zeichner A, Uibel K, Weiss O, Koops U, et al.
Direct inkjet printing of dental prostheses made of zirconia. J Dent Res 2009;88:673-6.
Ventola CL. Medical applications for 3D printing: Current and projected uses. P
Naftulin JS, Kimchi EY, Cash SS. Streamlined, inexpensive 3D printing of the brain and skull. PLoS One 2015;10:e0136198.
Müller A, Krishnan KG, Uhl E, Mast G. The application of rapid prototyping techniques in cranial reconstruction and preoperative planning in neurosurgery. J Craniofac Surg 2003;14:899-914.
Wurm G, Tomancok B, Pogady P, Holl K, Trenkler J. Cerebrovascular stereolithographic biomodeling for aneurysm surgery. technical note. J Neurosurg 2004;100:139-45.
Furlow B. Medical 3D-printing. Radiol Technol 2017;88:519CT-37CT.
Jacobs CA, Lin AY. A new classification of three-dimensional printing technologies: Systematic review of three-dimensional printing for patient-specific craniomaxillofacial surgery. Plast Reconstr Surg 2017;139:1211-20.
Sun Z, Lee SY. A systematic review of 3-D printing in cardiovascular and cerebrovascular diseases. Anatol J Cardiol 2017;17:423-35.
Mashiko T, Otani K, Kawano R, Konno T, Kaneko N, Ito Y, et al.
Development of three-dimensional hollow elastic model for cerebral aneurysm clipping simulation enabling rapid and low cost prototyping. World Neurosurg 2015;83:351-61.
Namba K, Higaki A, Kaneko N, Mashiko T, Nemoto S, Watanabe E, et al.
Microcatheter shaping for intracranial aneurysm coiling using the 3-dimensional printing rapid prototyping technology: Preliminary result in the first 10 consecutive cases. World Neurosurg 2015;84:178-86.
Vakharia VN, Vakharia NN, Hill CS. Review of 3-dimensional printing on cranial neurosurgery simulation training. World Neurosurg 2016;88:188-98.
Waran V, Pancharatnam D, Thambinayagam HC, Raman R, Rathinam AK, Balakrishnan YK, et al.
The utilization of cranial models created using rapid prototyping techniques in the development of models for navigation training. J Neurol Surg A Cent Eur Neurosurg 2014;75:12-5.
Waran V, Menon R, Pancharatnam D, Rathinam AK, Balakrishnan YK, Tung TS, et al.
The creation and verification of cranial models using three-dimensional rapid prototyping technology in field of transnasal sphenoid endoscopy. Am J Rhinol Allergy 2012;26:e132-6.
Waran V, Narayanan V, Karuppiah R, Thambynayagam HC, Muthusamy KA, Rahman ZA, et al.
Neurosurgical endoscopic training via a realistic 3-dimensional model with pathology. Simul Healthc 2015;10:43-8.
Thawani JP, Singh N, Pisapia JM, Abdullah KG, Parker D, Pukenas BA, et al.
Three-dimensional printed modeling of diffuse low-grade gliomas and associated white matter tract anatomy. Neurosurgery 2017;80:635-45.
Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al.
The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007;114:97-109.
Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al.
3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 2012;30:1323-41.
Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G. MeshLab: An Open-source mesh processing tool. In: Scarano V, De Chiara R, Erra U, editors. Proceedings of the Eurographics Italian Chapter Conference. Geneva: The Eurographics Association; 2008.
Nizam A, Gopal RN, Naing L, Hakim AB, Samsudin AR. Dimensional accuracy of the skull models produced by rapid prototyping technology using stereolithography apparatus. Arch Orofac Sci 2006;1:60-6.
Fagan M, editor. Understanding Brain Tumours: A Guide For People With Brain Or Spinal Cord Tumours, their Families and Friends. Australia: Cancer Council Australia; 2016.
Spottiswoode BS, van den Heever DJ, Chang Y, Engelhardt S, Du Plessis S, Nicolls F, et al.
Preoperative three-dimensional model creation of magnetic resonance brain images as a tool to assist neurosurgical planning. Stereotact Funct Neurosurg 2013;91:162-9.
Friedman T, Michalski M, Goodman TR, Brown JE. 3D printing from diagnostic images: A radiologist's primer with an emphasis on musculoskeletal imaging-putting the 3D printing of pathology into the hands of every physician. Skeletal Radiol 2016;45:307-21.
Firouzian A, Manniesing R, Flach ZH, Risselada R, van Kooten F, Sturkenboom MC, et al.
Intracranial aneurysm segmentation in 3D CT angiography: Method and quantitative validation with and without prior noise filtering. Eur J Radiol 2011;79:299-304.
Wiedermann JP, Joshi AS, Jamshidi A, Conchenour C, Preciado D. Utilization of a submental island flap and 3D printed model for skull base reconstruction: Infantile giant cranio-cervicofacial teratoma. Int J Pediatr Otorhinolaryngol 2017;92:143-45.
Waran V, Narayanan V, Karuppiah R, Owen SL, Aziz T. Utility of multimaterial 3D printers in creating models with pathological entities to enhance the training experience of neurosurgeons. J Neurosurg 2014;120:489-92.
Byrne N, Velasco Forte M, Tandon A, Valverde I, Hussain T. A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system. JRSM Cardiovasc Dis 2016;5:2048004016645467.
Oyama R, Jakab M, Kikuchi A, Sugiyama T, Kikinis R, Pujol S, et al.
Towards improved ultrasound-based analysis and 3D visualization of the fetal brain using the 3D slicer. Ultrasound Obstet Gynecol 2013;42:609-10.
Bauch T, Vijayaraman P, Dandamudi G, Ellenbogen K. Three-dimensional printing for in Vivo visualization of his bundle pacing leads. Am J Cardiol 2015;116:485-6.
Ploch CC, Mansi CS, Jayamohan J, Kuhl E. Using 3D printing to create personalized brain models for neurosurgical training and preoperative planning. World Neurosurg 2016;90:668-74.
Ogden KM, Aslan C, Ordway N, Diallo D, Tillapaugh-Fay G, Soman P, et al.
Factors affecting dimensional accuracy of 3-D printed anatomical structures derived from CT data. J Digit Imaging 2015;28:654-63.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
|This article has been cited by|
||3D surgical planning of pediatric tumors: a review
| ||Helena Rico Pereira, Mojtaba Barzegar, Osama Hamadelseed, Arnau Valls Esteve, Josep Munuera |
| ||International Journal of Computer Assisted Radiology and Surgery. 2022; |
|[Pubmed] | [DOI]|
||Error Minimization in Pre-surgical Model of Brain Tumor for 3-D Printing
| ||Chetan Mahatme, Jayant Giri |
| ||Journal of The Institution of Engineers (India): Series C. 2022; |
|[Pubmed] | [DOI]|
||Endoscopy-assisted resection of a sphenoid-wing meningioma using a 3D-printed patient-specific pointer in a dog: A case report
| ||JaeWon So, HaeBeom Lee, JaeMin Jeong, Franck Forterre, YoonHo Roh |
| ||Frontiers in Veterinary Science. 2022; 9 |
|[Pubmed] | [DOI]|
||Multiple Meningioma Resection by Bilateral Extended Rostrotentorial Craniotomy with a 3D-Print Guide in a Cat
| ||Kyohyuk Song, Haebeom Lee, Jaemin Jeong, Yoonho Roh |
| ||Veterinary Sciences. 2022; 9(10): 512 |
|[Pubmed] | [DOI]|
||Quantitative Assessment of 3D Printed Model Accuracy in Delineating Congenital Heart Disease
| ||Shenyuan Lee,Andrew Squelch,Zhonghua Sun |
| ||Biomolecules. 2021; 11(2): 270 |
|[Pubmed] | [DOI]|
||Clinical application of patient-specific 3D printing brain tumor model production system for neurosurgery
| ||Yun-Sik Dho,Doohee Lee,Teahyun Ha,So Young Ji,Kyung Min Kim,Ho Kang,Min-Sung Kim,Jin Wook Kim,Won-Sang Cho,Yong Hwy Kim,Young Gyu Kim,Sang Joon Park,Chul-Kee Park |
| ||Scientific Reports. 2021; 11(1) |
|[Pubmed] | [DOI]|
||Additive Fabrication of a Vascular 3D Phantom for Stereotactic Radiosurgery of Arteriovenous Malformations
| ||Elisa Legnani,Pasqualina Gallo,Federico Pezzotta,Francesco Padelli,Giuseppe Farag˛,Andrea Gioppo,Lorenzo Gentili,Elena De Martin,Maria Luisa Fumagalli,Francesco Cavaliere,Maria Grazia Bruzzone,Paolo Milani,Tommaso Santaniello |
| ||3D Printing and Additive Manufacturing. 2021; |
|[Pubmed] | [DOI]|
||Use of Three-dimensional Printing in the Development of Optimal Cardiac CT Scanning Protocols
| ||Zhonghua Sun |
| ||Current Medical Imaging Formerly Current Medical Imaging Reviews. 2020; 16(8): 967 |
|[Pubmed] | [DOI]|