3D Modelling and Printing Technology to Produce Patient-Specific 3D Models

The increasing complexity of cardiovascular interventions requires advanced peri-procedural imaging and tailored treatment. Three-dimensional printing technology represents one of the most significant advances in the field of cardiac imaging, interventional cardiology or cardiovascular surgery. Patient-specific models may provide substantial information on intervention planning in complex cardiovascular diseases, and volumetric medical imaging from CT or MRI can be translated into patient-specific 3D models using advanced post-processing applications. 3D printing and additive manufacturing have a great variety of clinical applications targeting anatomy, implants and devices, assisting optimal interventional treatment and post-interventional evaluation. Although the 3D printing technology still lacks scientific evidence, its benefits have been shown in structural heart diseases as well as for treatment of complex arrhythmias and corrective surgery interventions. Recent development has enabled transformation of conventional 3D printing into complex 3D functional living tissues contributing to regenerative medicine through engineered bionic materials such hydrogels, cell suspensions or matrix components. This review aims to present the most recent clinical applications of 3D printing in cardiovascular medicine, highlighting also the potential for future development of this revolutionary technology in the medical field.

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Involving patients, families and medical staff in the evaluation of 3D printing models of congenital heart disease

Communication & Medicine ◽

10.1558/cam.28455 ◽

2016 ◽

Vol 12 (2-3) ◽

Cited By ~ 13

Author(s):

Giovanni Biglino ◽

Claudio Capelli ◽

Lindsay-Kay Leaver ◽

Silvia Schievano ◽

Andrew M. Taylor ◽

...

Keyword(s):

Congenital Heart Disease ◽

Heart Disease ◽

3D Printing ◽

Congenital Heart ◽

Patient Empowerment ◽

3D Models ◽

Patient Specific ◽

Printing Technology ◽

Training Tool ◽

3D Printing Technology

Objective: To evaluate the usefulness of 3D printing patient-specific models of congenital heart disease (CHD) from the perspective of different stakeholders potentially benefiting from the technology (patients, parents, clinicians and nurses). Methods: Workshops, focus groups and teaching sessions were organized, each targeting a different group of stakeholders. Sessions involved displaying and discussing different 3D models of CHD. Model evaluation involved questionnaires, audio-recorded discussions and written feedback. Results: All stakeholders expressed a liking for the 3D models and for the patient-specific quality of such models. Patients indicated that 3D models can help them imagine “what’s going on inside” and parents agreed that these tools can spark curiosity in the young people. Clinicians indicated that teaching might be the most relevant application of such novel technology and nurses agreed that 3D models improved their learning experience during a course focused on CHD. Conclusion: The successful engagement of different stakeholders to evaluate 3D printing technology for CHD identified different priorities, highlighting the importance of eliciting the views of different groups. Practice Implications: A PPI-based approach in the evaluation and translation of 3D printing technology may increase patient empowerment, improve patient-doctor communication and provide better access to a new teaching and training tool.

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3D modelling for realistic training and learning

Turkish Journal of Biochemistry ◽

10.1515/tjb-2019-0182 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Ayse Hilal Bati

Keyword(s):

3D Printing ◽

3D Reconstruction ◽

Three Dimensional ◽

3D Models ◽

3D Modelling ◽

Patient Specific ◽

Physical Interaction ◽

Imaging Data ◽

Data Set ◽

Modelling Techniques

AbstractObjectivesThree-dimensional (3D) reconstruction and modelling techniques based on computer vision have shown significant progress in recent years. Patient-specific models, which are derived from the imaging data set and are anatomically consistent with each other, are important for the development of knowledge and skills. The purpose of this article is to share information about three-dimensional (3D) reconstruction and modelling techniques and its importance in medical education.MethodsAs 3D printing technology develops and costs are lower, adaptation to the original model will increase, thus making models suitable for the anatomical structure and texture. 3D printing has emerged as an innovative way to help surgeons implement more complex procedures.ResultsRecent studies have shown that 3D modelling is a powerful tool for pre-operative planning, proofing, and decision-making. 3D models have excellent potential for alternative interventions and surgical training on both normal and pathological anatomy. 3D printing is an attractive, powerful and versatile technology.ConclusionsPatient-specific models can improve performance and improve learning faster, while improving the knowledge, management and confidence of trainees, whatever their area of expertise. Physical interaction with models has proven to be the key to gaining the necessary motor skills for surgical intervention.

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Experience in Use of 3D Printing in Engineering Education at University of Stavanger

Nordic Journal of STEM Education ◽

10.5324/njsteme.v5i1.3934 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Hirpa G. Lemu ◽

Ove Mikkelsen

Keyword(s):

3D Printing ◽

Global Economy ◽

3D Model ◽

Manufacturing Sector ◽

3D Models ◽

3D Modelling ◽

Printing Technology ◽

3D Print ◽

3D Printing Technology ◽

3D Printed

This article is based on a project run in 2018 and 2019 entitled “Educating Mechanical Engineering using 3D Printing – Under3DP”. The project was funded by Faculty of Science and Technology, University of Stavanger (UiS). The project is motivated by the current developments of the 3D printing technology in diverse disciplines whose initial inception was for rapid prototyping that can transform 3D models in computers to physical objects that the designer and/or the customer can touch, feel and better comprehend. Being one of the enablers of the digital transformations in manufacturing, the 3D printing technology is the fastest growing technologies and it is bringing more and more significant impacts to the manufacturing sector, healthcare, daily life, and the global economy. The pedagogical benefit of the project was evaluated using questionnaire based survey after the students of a course in Product Development and 3D Modelling have executed a mandatory group exercise to make 3D models of 3D printed samples and 3D print some of their 3D model ideas. According to the assessment results, more than 80% of the students who participated in the assessment responded that use of 3D fabricated parts in product design tasks have contributed to better understanding of the task and 3D printing has supported the learning process.

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Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽

10.3390/ma14041021 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1021

Author(s):

Bernhard Dorweiler ◽

Pia Elisabeth Baqué ◽

Rayan Chaban ◽

Ahmed Ghazy ◽

Oroa Salem

Keyword(s):

Wall Thickness ◽

Large Scale ◽

Fused Deposition Modeling ◽

Vascular Anatomy ◽

3D Models ◽

Patient Specific ◽

Stl File ◽

Fused Deposition ◽

3D Printed ◽

The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

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Complex wall modeling for hemodynamic simulations of intracranial aneurysms based on histologic images

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02334-z ◽

2021 ◽

Vol 16 (4) ◽

pp. 597-607

Author(s):

Annika Niemann ◽

Samuel Voß ◽

Riikka Tulamo ◽

Simon Weigand ◽

Bernhard Preim ◽

...

Keyword(s):

Wall Thickness ◽

Vessel Wall ◽

Intracranial Aneurysms ◽

Geometric Model ◽

Image Data ◽

Outer Wall ◽

3D Models ◽

Patient Specific ◽

Intracranial Vessel ◽

Hemodynamic Simulations

Abstract Purpose For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall. Methods In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations. Result We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious. Conclusion The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.

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Transcript: Robotic right complete mesocolectomy with patient‐specific 3D‐modelling for planning and intra‐operative navigation

Colorectal Disease ◽

10.1111/codi.15738 ◽

2021 ◽

Author(s):

JA Fletcher ◽

C Behrenbruch ◽

P Lung ◽

D Miskovic

Keyword(s):

3D Modelling ◽

Patient Specific

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The applicability of patient-Specific 3D models in the planning of nerve-sparing robot-assisted radical prostatectomy

European Urology ◽

10.1016/s0302-2838(21)01469-x ◽

2021 ◽

Vol 79 ◽

pp. S1556-S1557

Author(s):

H. Veerman ◽

T.N. Boellaard ◽

C. Hoeks ◽

J.A. Van Eick ◽

J. Sluijter ◽

...

Keyword(s):

Radical Prostatectomy ◽

3D Models ◽

Patient Specific ◽

Nerve Sparing ◽

Robot Assisted

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Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

Journal of Dental Research ◽

10.1177/00220345211005338 ◽

2021 ◽

pp. 002203452110053

Author(s):

H. Wang ◽

J. Minnema ◽

K.J. Batenburg ◽

T. Forouzanfar ◽

F.J. Hu ◽

...

Keyword(s):

Gold Standard ◽

Orthodontic Treatment ◽

3D Models ◽

Similarity Coefficient ◽

Patient Specific ◽

Dice Similarity Coefficient ◽

Surface Deviations ◽

Surface Deviation ◽

Binary Segmentation ◽

Time Required

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

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