Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 81
  • Home
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2017  |  Volume : 3  |  Issue : 4  |  Page : 170-177

Patient-specific three-dimensional printed pulmonary artery model: A preliminary study


1 Department of Medical Radiation Sciences, Curtin University, Perth, Western, Australia
2 Department of Exploration Geophysics, Western n School of Mines; Curtin Institute for Computation, Curtin University, Perth, Western, Australia

Date of Web Publication26-Mar-2018

Correspondence Address:
Zhonghua Sun
Department of Medical Radiation Sciences, Curtin University, GPO Box U1987, Perth
Australia
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_42_17

Rights and Permissions
  Abstract 


Background and Objectives: Three-dimensional (3D) printing has potential value in medical applications with increasing reports in the diagnostic assessment of cardiovascular diseases. The use of 3D printing in replicating pulmonary artery anatomy and diagnosing pulmonary embolism is very limited. The purpose of this study was to develop a 3D printed pulmonary artery model and test different computed tomography (CT) scanning protocols for determination of an optimal protocol with acceptable image quality but low radiation dose. Materials and Methods: A patient-specific 3D printed pulmonary artery model was created based on contrast-enhanced CT images in a patient with suspected pulmonary embolism. Different CT pulmonary angiography protocols consisting of 80, 100, and 120 kVp, pitch 0.7, 0.9, and 1.2 with 1 mm slice thickness, and 0.6 mm reconstruction interval were tested on the phantom. Quantitative assessment of image quality in terms of signal-to-noise ratio (SNR) was measured in the images acquired with different protocols. Measurements in pulmonary artery diameters were conducted and compared between pre- and post-3D printed images and 3D printed model. Results: The 3D printed model was found to replicate normal pulmonary artery with high accuracy. The mean difference in diameter measurements was <0.8 mm (<0.5% deviation in diameter). There was no significant difference in SNR measured between these CT protocols (P = 0.96–0.99). Radiation dose was reduced by 55% and 75% when lowering kVp from 120 to 100 and 80 kVp, without affecting image quality. Conclusions: It is feasible to produce a 3D printed pulmonary artery model with high accuracy in replicating normal anatomy. Different CT scanning protocols are successfully tested on the model with 80 kVp and pitch 0.9 being the optimal one with resultant diagnostic images but at much lower radiation dose.

Keywords: Diagnosis, image quality, model, pulmonary artery, three-dimensional printing


How to cite this article:
Aldosari S, Squelch A, Sun Z. Patient-specific three-dimensional printed pulmonary artery model: A preliminary study. Digit Med 2017;3:170-7

How to cite this URL:
Aldosari S, Squelch A, Sun Z. Patient-specific three-dimensional printed pulmonary artery model: A preliminary study. Digit Med [serial online] 2017 [cited 2023 Jun 8];3:170-7. Available from: http://www.digitmedicine.com/text.asp?2017/3/4/170/228667




  Introduction Top


Three-dimensional (3D) printing is a rapidly developing technique showing increasing interest and great potential in medicine.[1],[2],[3],[4],[5] The diagnostic application of using patient-specific 3D printed models has been reported in the diagnostic assessment of cardiovascular disease, presurgical planning and simulation, as well as medical education.[6],[7],[8],[9],[10],[11],[12],[13] These studies created 3D printed realistic models using either computed tomography (CT) or magnetic resonance imaging or echocardiography data with accurate replication of anatomical structures and pathological changes. A recent systematic review of 48 studies has demonstrated the usefulness of 3D printed models in replicating complex cardiovascular anatomy with high accuracy, serving as a valuable tool for presurgical planning and simulation of cardiovascular disease and medical education to healthcare professionals and medical students.[14]

To the best of our knowledge, very few research studies have been conducted in the diagnostic assessment of 3D printed models in pulmonary artery diseases.[15] CT pulmonary angiography (CTPA) is the preferred imaging modality in the diagnosis of pulmonary embolism.[16],[17],[18],[19],[20] Although CTPA has high diagnostic value in detecting pulmonary embolism, it has disadvantages of the associated high radiation dose. Further, administration of contrast medium during CTPA represents another limitation with a potential risk of contrast-induced nephropathy.[21],[22] Therefore, reduction of both radiation and contrast medium doses during CTPA is the current research direction with promising results achieved.

According to these studies, there is still potential for further lowering of the radiation and contrast medium doses during CTPA. To test the feasibility of different protocols, a realistic anatomic phantom is an ideal option, and a 3D printed model serves this purpose. Thus, the primary aim of this study was to use a patient-specific 3D printed pulmonary artery model to test different CTPA protocols with the aim of identifying optimal CTPA protocol. Further, potential factors including image segmentation, editing, and 3D printing processes could affect the dimensional accuracy of 3D printed models.[23],[24] Witowski et al. in their recent systematic review indicate the lack of quantitative methods to validate liver model accuracy.[25] Similarly, no studies have reported the quantitative assessment of 3D printed pulmonary model accuracy. Therefore, the secondary aim of this study was to quantitatively assess the model accuracy of 3D printed pulmonary artery model in delineating anatomical structures.


  Materials and Methods Top


Sample image selection

CTPA images from a 53-year-old female with suspected pulmonary embolism were selected in this study to generate 3D reconstructed pulmonary artery model for 3D printing. CTPA showed normal pulmonary artery without any sign of pulmonary embolism. The CT scan was performed on a 128-slice scanner (Siemens Definition Flash, Siemens Healthcare, Forchheim, Germany) with slice thickness of 1.0 mm and reconstruction interval of 0.6 mm.

Image postprocessing and segmentation for three-dimensional printing

Original digital imaging and communications in medicine (DICOM) of CTPA images was transferred to a separate workstation equipped with analyze 12.0 (AnalyzeDirect, Inc., Lexana, KS, USA) for image processing and segmentation. Semiautomatic approach was used to perform image postprocessing and segmentation of 3D volume data. A CT number thresholding technique was first used to produce 3D volume rendering images with inclusion of the pulmonary trunk, left main, and right main pulmonary arteries. In brief, CT attenuation in the pulmonary arteries was measured (around 150 Hounsfield unit [HU]) and applied as the lowest threshold to demonstrate only contrast-enhanced pulmonary arteries and cardiac chambers, while soft tissue, pulmonary veins, and other structures with CT attenuation <150 HU were removed as the focus of this study was pulmonary arteries. Bony structures and cardiac chambers have high CT attenuation (>300 HU); thus, removal of these structures was conducted by the function of object separator that is available with analyze 12.0. Some manual editing was applied to ensure the accuracy of 3D model in the delineation of pulmonary arterial tree. This involved further removal of some structures that were still included in the 3D volume data such as overlapping tissues and presence of artifacts and applying a median filter to remove some image noise for better definition of pulmonary arteries with side branches. The generated model of the segmented pulmonary arteries was subsequently exported to the standard tessellation language (STL) file format which is commonly used for 3D printing. [Figure 1] shows the steps of image postprocessing and segmentation from original 2D DICOM images to generation of segmented volume data and STL file to the final step of 3D printed model.
Figure 1: Flow diagram shows the image postprocessing and segmentation steps from two-dimensional computed tomography images to creation of three-dimensional printed model. Original two-dimensional digital imaging and communications in medicine images were used to create three-dimensional volume-rendering image with use of computed tomography number thresholding technique to display contrast-enhanced vessels (blue color - pulmonary arteries, pink color - aorta and its branches, white color - left atrium and pulmonary veins). Three-dimensional volume rendering of pulmonary artery tree is segmented through semiautomatic segmentation and manual editing. Standard tessellation language file of three-dimensional segmented volume data was generated for three-dimensional printing of patient-specific three-dimensional printed model

Click here to view


The STL file of 3D segmented pulmonary artery was uploaded to Shapeways, an online 3D printing service.[26] The model was printed in “elastoplastic” material, which has material property closest to that of arterial wall.[27]

Computed tomography pulmonary angiography scanning protocols for three-dimensional printed model

To determine the accuracy of the 3D printed model in replicating anatomical structures, a series of CTPA scans were conducted on the 3D printed pulmonary artery model with different scanning protocols. A total of nine scans were performed, in which three tube voltages of 80, 100, and 120 kVp and three pitch values of 0.7, 0.9, and 1.2 were tested. The 3D printed model was placed in a plastic box filled with a contrast medium (Omnipaque 370) [Figure 2], and scans were performed on a 64-slice CT scanner (Siemens Definition AS, Siemens Healthcare, Forchheim, Germany) with slice thickness of 1.0 mm and reconstruction interval of 0.6 mm. The contrast medium was diluted to 6% resulting in CT attenuation of 150 HU which is similar to that of clinical CTPA examination. DICOM images of the scanned model were transferred to a workstation for measurements of pulmonary artery diameters and image quality.
Figure 2: Three-dimensional printed pulmonary model is placed in a plastic box which is used to be filled with contrast medium for computed tomography scans

Click here to view


Measurements of pulmonary artery diameters

Diameter measurements at the pulmonary trunk, right, and left main pulmonary arteries were performed on original CTPA images, STL file, 3D printed model, and post-3D printing scanned CT images. Measurements were performed by two observers with >10 years of experience in CT imaging, with each measurement repeated three times. The two observers performed measurements separately, and the results showed a very high correlation between these two observers (r = 0.99–1.0, P < 0.001). [Figure 3] shows an example of measuring the pulmonary trunk using an electronic caliper.
Figure 3: Measurement of the diameter of pulmonary trunk using an electronic caliper

Click here to view


Quantitative measurements of image quality

Image quality was assessed by measuring the image noise, which is defined as standard deviation (SD) of CT attenuation (HU) in the pulmonary arteries. A circular region of interest (ROI) with a diameter of 50 mm 2 (containing 300 voxels within the ROI) was placed at the pulmonary trunk, left main, and right main pulmonary arteries to measure the signal-to-noise ratio (SNR) which is defined as:

SNR = CT attenuation in the pulmonary artery/SD

[Figure 4] is an example showing measurement of image quality (SNR) at the pulmonary trunk, left main, and right main pulmonary arteries. Contrast-to-noise ratio was not measured in this study due to lack of background tissue since 3D printed model was immersed into the contrast medium. The two observers performed measurements separately, and the results showed a high correlation between these two observers (r = 0.99–1.0, P < 0.05).
Figure 4: Region of interest is placed at the pulmonary trunk, right, and left main pulmonary arteries for measurement of image quality

Click here to view


Radiation dose

Radiation dose values in terms of volume CT dose index (CTDIvol) and dose length product (DLP) were available on the CT console. Effective dose (ED) was calculated by multiplying the DLP by a tissue coefficient factor, which is 0.014 mSv/mGy/cm for chest CT scan.[28]

Statistical analysis

Data were entered into MS Excel for analysis. Continuous variables were presented as mean ± SD. A two-sided Student's t-test was used to determine any significant differences between measurements performed at original CT, STL, 3D printed model, and post-3D printing scanned images, with P < 0.05 indicating statistical significance.


  Results Top


CT scans of the 3D printed pulmonary artery model were successfully performed. [Table 1] shows measurements of the main pulmonary arteries made with different scanning protocols when compared to the original CTPA images with differences <0.8 mm, indicating high accuracy of 3D printed model in replicating anatomical structures. There was also very good correlation between measurements on STL file in comparison to those on original CT images, post-3D printed CT images, and 3D printed model, with the mean difference <0.5 mm as shown in [Table 2].
Table 1: Diameters of pulmonary arteries measured on computed tomography scanned three-dimensional printed model

Click here to view
Table 2: Diameters of pulmonary arteries measured on original CT images, STL images and 3D printed model

Click here to view


[Table 3] shows SNR measurements at different CTPA protocols with corresponding radiation dose values. With 80 and 100 kVp protocols, SNR was slightly decreased when the pitch was increased from 0.7 to 0.9 and 1.2 although this did not reach significant difference in measurements with these protocols (P = 0.96–0.99). With 120 kVp protocol, SNR was slightly increased with the increase of pitch in most of the measurements, with no statistical significance difference noted (P = 0.97–0.99).
Table 3: Quantitative measurement of image quality and radiation dose in different computed tomography scanning protocols

Click here to view


[Table 3] also shows radiation dose values associated with these CTPA protocols. As shown in [Table 3], CTDIvol and DLP remained almost the same despite the use of different pitch values, mainly due to the use of tube current modulation. With 80 kVp as the selected protocol, the ED was reduced by 55% and 75% when kVp was lowered from 100 and 120 kVp to 80 kVp, respectively, while still maintaining diagnostic image quality. [Figure 5] shows coronal reformatted CT images acquired with 9 different protocols with good visualization of the pulmonary trunk and left and right main pulmonary arteries.
Figure 5: Computed tomography pulmonary angiography scanning protocols in the three-dimensional printed model. Two-dimensional coronal reformatted images showing main pulmonary trunk, right, and left main pulmonary arteries with 80 kVp, pitch 0.7, 0.9, and 1.2 (a), 100 kVp, pitch 0.7, 0.9 ,and 1.2 (b), 120 kVp, pitch 0.7, 0.9, and 1.2 (c)

Click here to view



  Discussion Top


This preliminary study has two main findings:first, 3D printed pulmonary artery model has high accuracy in replicating anatomical structures; thus, it can be used as a reliable tool for testing CT scans. Second, an optimal CTPA protocol can be developed through testing different scanning protocols on the 3D printed model, with a protocol of 80 kVp and pitch 0.9 being the optimal one with resultant low radiation dose but maintaining diagnostic image quality.

3D printed models have been shown to enhance understanding of the complexity of cardiovascular disease by demonstrating the accuracy of delineating anatomical structures and pathologies, preoperative planning and simulation, and medical education.[6],[7],[8],[9],[10],[11],[12],[13],[14],[15] Case reports and case series studies have proved successful applications of 3D printed models in assisting diagnosis and clinical management of congenital heart diseases including pulmonary artery abnormalities.[29],[30],[31],[32],[33] A recent case report discussing the 3D printed model of ventricular septal defect (VSD) and pulmonary atresia showed that 3D printing assisted the development of preoperative planning and treatment approach for managing this complex case.[30] Sahayaraj et al. further confirmed the clinical value of using 3D printed model in managing complex cardiovascular cases involving great vessels.[31] The 3D printed model was found to have great value in improving understanding of the spatial relationship between cardiac chambers, VSD, and great arteries, with biventricular physiologic repair successfully performed owing to the increased spatial perception provided by the 3D printed model.

Biglino et al. provided an insight into the clinical applications of 3D printed heart model based on the perspective from different stakeholders.[29] The 3D printed model was considered by surgeon and cardiologist to improve understanding of the 3D relationship of different structures, such as better demonstrating the narrowed pulmonary artery and the dilated ascending aorta. Medical imaging specialist considered that 3D printed model improved communication in multidisciplinary meetings, thus allowing better decision-making in patient treatment. Further, a medical student indicated the great potential of 3D printed models in teaching anatomy and pathology.[29]

Despite promising results about the clinical value of 3D printed models, reports on the dimensional accuracy of 3D printed pulmonary artery model are scarce. Most of the current studies focus on the accuracy of 3D printed heart models, in particular, congenital heart disease with good correlation between 3D printed models and original source images.[5],[6],[7],[8],[9] However, in their recent study, Ho et al. reported the mean difference of >1.0 mm in aortic vessel diameters between contrast-enhanced CT images before and after 3D printing.[34] The variance in dimensional accuracy was also demonstrated by Lau et al. who showed that the mean diameter difference between 3D printed model of brain tumor and original images being 0.98%, which exceeds the recommended 0.5% deviation.[35] Findings in this study showed high accuracy of the 3D printed pulmonary artery model with the mean difference <0.5% deviation in measurements between pre- and post-3D printing images. Thus, results of CT scans based on the 3D printed model could be used as a reliable source for determining optimal scanning protocols in terms of acquiring diagnostic images with radiation dose reduction.

Increased use of CTPA in clinical practice has raised concerns because of its associated high radiation exposure and potential risk of contrast-induced nephropathy.[16],[20] Therefore, optimization of CTPA protocol is a hot topic in the current literature with successful reductions in both radiation dose and contrast medium dose achieved. Findings of this study are in line with these previous reports on patient's data.[16],[17],[18],[19],[20] Low tube voltage and low pitch value such as 80 kVp and 0.9 are preferred with acquisition of acceptable diagnostic images (similar SNR values) but low radiation dose when compared to the protocol of 100 or 120 kVp and high pitch value. The current multislice CT scanners are equipped with latest dose-reduction protocols, such as automatic tube current or tube potential modulation; therefore, high pitch is not recommended. The pitch of 0.9 as recommended by this study is consistent with Boos et al. who also proposed the 70 kVp and pitch of 0.9 CTPA protocol.[36] We did not include 70 kVp in this study as 100 or 120 kVp is commonly used in CTPA. Further, due to 3D printed model being static instead of having hemodynamic flow features, we did not assess the effect of changing contrast medium volume on image quality. This could be addressed in further studies.

Despite promising results, this study has several limitations which need to be acknowledged. First, the 3D printed model was based on a normal case without showing any sign of pulmonary embolism. Thus, no subjective assessment of image quality was conducted. Experiments on the optimal CTPA protocols in the detection of pulmonary embolism with use of 3D printed model are under investigation. Second, although the 3D printed model was made with elastic material, it still does not represent the real tissue properties of vascular wall. Further, the phantom was scanned in a static condition instead of representing realistic CTPA with blood circulating to the pulmonary arteries. Finally, due to including only one case, we did not perform Bland–Altman assessment of degree of agreement in measurements between pre- and post-3D printing images. Further studies should include more cases for generating 3D printed models, which would allow for more reliable detection of trends in bias.


  Conclusions Top


We have shown the feasibility of generating patient-specific 3D printed pulmonary artery model with high accuracy in replicating normal anatomical structures. The 3D printed model is used to test different CTPA protocols with the protocol of 80 kVp, pitch 0.9 with 1 mm slice thickness, and reconstruction interval of 0.6 mm being the optimal one. Future research based on simulation of pulmonary embolism with different CT scanning parameters is needed to determine the clinical value of 3D printed model in the detection of pulmonary embolism with lower radiation dose while still maintaining diagnostic image quality.

Acknowledgments

This work was supported by The Pawsey Supercomputing Centre through the use of advanced visualization resources located at The Australian Resources Research Centre with funding from the Australian Government and the Government of Western Australia. The authors would also like to acknowledge the assistance of Dr. Lionel Esteban, Manager, NGL medical XCT facility, CSIRO, oil, gas and fuels program in the acquisition of the CT scans of the pulmonary artery model.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Valverde I, Gomez G, Gonzalez A, Suarez-Mejias C, Adsuar A, Coserria JF, et al. Three-dimensional patient-specific cardiac model for surgical planning in Nikaidoh procedure. Cardiol Young 2015;25:698-704.  Back to cited text no. 1
    
2.
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.  Back to cited text no. 2
    
3.
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.  Back to cited text no. 3
    
4.
Martelli N, Serrano C, van den Brink H, Pineau J, Prognon P, Borget I, et al. Advantages and disadvantages of 3-dimensional printing in surgery: A systematic review. Surgery 2016;159:1485-500.  Back to cited text no. 4
    
5.
Bartel T, Rivard A, Jimenez A, Mestres CA, Müller S. Medical three-dimensional printing opens up new opportunities in cardiology and cardiac surgery. Eur Heart J 2017;Feb 16. doi: 10.1093/eurheartj/ehx016. [Epub ahead of print].  Back to cited text no. 5
    
6.
Schmauss D, Haeberle S, Hagl C, Sodian R. Three-dimensional printing in cardiac surgery and interventional cardiology: A single-centre experience. Eur J Cardiothorac Surg 2015;47:1044-52.  Back to cited text no. 6
    
7.
Biglino G, Koniordou D, Gasparini M, Capelli C, Leaver LK, Khambadkone S, et al. Piloting the use of patient-specific cardiac models as a novel tool to facilitate communication during cinical consultations. Pediatr Cardiol 2017;38:813-8.  Back to cited text no. 7
    
8.
Cantinotti M, Valverde I, Kutty S. Three-dimensional printed models in congenital heart disease. Int J Cardiovasc Imaging 2017;33:137-44.  Back to cited text no. 8
    
9.
Giannopoulos AA, Mitsouras D, Yoo SJ, Liu PP, Chatzizisis YS, Rybicki FJ, et al. Applications of 3D printing in cardiovascular diseases. Nat Rev Cardiol 2016;13:701-18.  Back to cited text no. 9
    
10.
Bhatla P, Tretter JT, Chikkabyrappa S, Chakravarti S, Mosca RS. Surgical planning for a complex double-outlet right ventricle using 3D printing. Echocardiography 2017;34:802-4.  Back to cited text no. 10
    
11.
Lim KH, Loo ZY, Goldie SJ, Adams JW, McMenamin PG. Use of 3D printed models in medical education: A randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy. Anat Sci Educ 2016;9:213-21.  Back to cited text no. 11
    
12.
Costello JP, Olivieri LJ, Krieger A, Thabit O, Marshall MB, Yoo SJ, et al. Utilizing three-dimensional printing technology to assess the feasibility of high-fidelity synthetic ventricular septal defect models for simulation in medical education. World J Pediatr Congenit Heart Surg 2014;5:421-6.  Back to cited text no. 12
    
13.
Costello JP, Olivieri LJ, Su L, Krieger A, Alfares F, Thabit O, et al. Incorporating three-dimensional printing into a simulation-based congenital heart disease and critical care training curriculum for resident physicians. Congenit Heart Dis 2015;10:185-90.  Back to cited text no. 13
    
14.
Sun Z, Lee SY. A systematic review of 3-D printing in cardiovascular and cerebrovascular diseases. Anatol J Cardiol 2017;17:423-35.  Back to cited text no. 14
    
15.
Giannopoulos AA, Steigner ML, George E, Barile M, Hunsaker AR, Rybicki FJ, et al. Cardiothoracic applications of 3-dimensional printing. J Thorac Imaging 2016;31:253-72.  Back to cited text no. 15
    
16.
Mayo J, Thakur Y. Pulmonary CT angiography as first-line imaging for PE: Image quality and radiation dose considerations. AJR Am J Roentgenol 2013;200:522-8.  Back to cited text no. 16
    
17.
Wittram C. How I do it: CT pulmonary angiography. AJR Am J Roentgenol 2007;188:1255-61.  Back to cited text no. 17
    
18.
den Exter PL, van der Hulle T, Klok FA, Huisman MV. Advances in the diagnosis and management of acute pulmonary embolism. Thromb Res 2014;133 Suppl 2:S10-6.  Back to cited text no. 18
    
19.
Righini M, Le Gal G, Aujesky D, Roy PM, Sanchez O, Verschuren F, et al. Diagnosis of pulmonary embolism by multidetector CT alone or combined with venous ultrasonography of the leg: A randomised non-inferiority trial. Lancet 2008;371:1343-52.  Back to cited text no. 19
    
20.
Sun Z, Lei J. Diagnostic yield of CT pulmonary angiography in the diagnosis of pulmonary embolism: A single center experience. Interv Cardiol 2017;9:191-8.  Back to cited text no. 20
    
21.
Ong CW, Malipatil V, Lavercombe M, Teo KG, Coughlin PB, Leach D, et al. Implementation of a clinical prediction tool for pulmonary embolism diagnosis in a tertiary teaching hospital reduces the number of computed tomography pulmonary angiograms performed. Intern Med J 2013;43:169-74.  Back to cited text no. 21
    
22.
Newman DH, Schriger DL. Rethinking testing for pulmonary embolism: Less is more. Ann Emerg Med 2011;57:622-7.e3.  Back to cited text no. 22
    
23.
Mitsouras D, Liacouras P, Imanzadeh A, Giannopoulos AA, Cai T, Kumamaru KK, et al. Medical 3D printing for the radiologist. Radiographics 2015;35:1965-88.  Back to cited text no. 23
    
24.
Madurska MJ, Poyade M, Eason D, Rea P, Watson AJ. Development of a patient-specific 3D-printed liver model for preoperative planning. Surg Innov 2017;24:145-50.  Back to cited text no. 24
    
25.
Witowski JS, Coles-Black J, Zuzak TZ, Pędziwiatr M, Chuen J, Major P, et al. 3D printing in liver surgery: A systematic review. Telemed J E Health 2017;23:943-7.  Back to cited text no. 25
    
26.
Shapeways. Frequently Asked Questions. Available from: http://www.shapeways.com/support/faq?li=footer#faq-whatisshapeways. [Last accessed on 2017 Nov 29].  Back to cited text no. 26
    
27.
Available from: https://www.shapeways.com/materials/elasto-plastic. [Last accessed on 2017 Nov 29].  Back to cited text no. 27
    
28.
McCollough CH, Primak AN, Braun N, Kofler J, Yu L, Christner J, et al. Strategies for reducing radiation dose in CT. Radiol Clin North Am 2009;47:27-40.  Back to cited text no. 28
    
29.
Biglino G, Moharem-Elgamal S, Lee M, Tulloh R, Caputo M. The perception of a three-dimensional-printed heart model from the perspective of different stakeholders: A complex case of truncus arteriosus. Front Pediatr 2017;5:209.  Back to cited text no. 29
    
30.
Jaworski R, Haponiuk I, Chojnicki M, Olszewski H, Lulewicz P. Three-dimensional printing technology supports surgery planning in patients with complex con-genital heart defects. Kardiol Pol 2017;75:185.  Back to cited text no. 30
    
31.
Sahayaraj RA, Ramanan S, Subramanyan R, Cherian KM. 3D printing to model surgical repair of complex congenitally corrected transposition of the great arteries. World J Pediatr Congenit Heart Surg 2017;2017 Jan 1:2150135117704655. doi: 10.1177/2150135117704655. [Epub ahead of print].  Back to cited text no. 31
    
32.
Jones TW, Seckeler MD. Use of 3D models of vascular rings and slings to improve resident education. Congenit Heart Dis 2017;12:578-82.  Back to cited text no. 32
    
33.
Kappanayil M, Koneti NR, Kannan RR, Kottayil BP, Kumar K. Three-dimensional-printed cardiac prototypes aid surgical decision-making and preoperative planning in selected cases of complex congenital heart diseases: Early experience and proof of concept in a resource-limited environment. Ann Pediatr Cardiol 2017;10:117-25.  Back to cited text no. 33
    
34.
Ho D, Squelch A, Sun Z. Modelling of aortic aneurysm and aortic dissection through 3D printing. J Med Radiat Sci 2017;64:10-7.  Back to cited text no. 34
    
35.
Lau I, Squelch A, Wan YL, Wong A, 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.  Back to cited text no. 35
  [Full text]  
36.
Boos J, Kröpil P, Lanzman RS, Aissa J, Schleich C, Heusch P, et al. CT pulmonary angiography: Simultaneous low-pitch dual-source acquisition mode with 70 kVp and 40 ml of contrast medium and comparison with high-pitch spiral dual-source acquisition with automated tube potential selection. Br J Radiol 2016;89:20151059.  Back to cited text no. 36
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]


This article has been cited by
1 Deep Learning in Personalization of Cardiovascular Stents
Yugyung Lee,Krishna Veerubhotla,Myung Ho Jeong,Chi H. Lee
Journal of Cardiovascular Pharmacology and Therapeutics. 2020; 25(2): 110
[Pubmed] | [DOI]
2 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]
3 Clinical Applications of Patient-Specific 3D Printed Models in Cardiovascular Disease: Current Status and Future Directions
Zhonghua Sun
Biomolecules. 2020; 10(11): 1577
[Pubmed] | [DOI]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusions
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed3593    
    Printed192    
    Emailed0    
    PDF Downloaded269    
    Comments [Add]    
    Cited by others 3    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]