COMPUTER-AIDED MODELING AND ADDITIVE MANUFACTURING FABRICATION OF PATIENT-SPECIFIC MANDIBULAR IMPLANT

With the recent advances in computer-aided technologies and their breach into the medical field, there can be seen more and more successful outcomes, especially in the files of reconstructive prosthetic surgery. With the application of advanced tools for reconstruction of complex shape such as human anatomy, it allowed accurate and fast design of complex implants as a substitution for deformed or damaged regions in the field of maxillofacial surgery. Design, in compliance with application of additive manufacturing (AM) technologies, is starting to gain more recognition as a tool for fast and accurate delivery of patient-specific 3D implants. This paper present a case study where such 3D technologies are used to design and fabricate a patient-specific mandibular implant. Tools for design of complex anatomical surfaces, such as mandible are presented and demonstrated in this paper. As the verification stage, AM technologies are used for visual inspection and surgical procedure planning of the designed 3D model of the mandibular implant

An Innovative Photogrammetric System for 3D Digitization of Dental Models

This paper presents an innovative system for 3D reconstruction of a physical dental model. The innovative system is based on close-range photogrammetry and enables the projection of digital light texture on the objects surface. It is based on the application of mirrors that direct the digital light texture to the vertical surfaces of the physical model. In this way, high coverage of the object is achieved, and 3D reconstruction from one set of photographs is possible. 3D digitization, verification and comparison of the proposed methodology was performed on dental models that are characterized by extremely complex surfaces. It was performed by comparing the proposed approach with active stereovision, and the efficiency was evaluated in relation to the reference 3D model obtained by the structured light 3D scanner. The comparison of the results was performed on the basis of the mean deviation and standard deviation for the 3D model with combined teeth and for the 3D model with metal caps. The absolute mean deviations for the 3D model with combined teeth are 0.004-0.021 mm, with a standard deviation of 0.055-0.058 mm, and for the 3D model with metal caps absolute mean deviations are 0.015-0.033 mm, with a standard deviation of 0.095-0.113 mm, respectively. Absolute minimum values of mean deviation of 0.004 mm and standard deviations of 0.055 mm were obtained by 3D model with combined teeth,which was reconstructed by the proposed innovative approach. The obtained results indicate a higher accuracy of the innovative approach in relation to the use of a commercial 3D scanner that uses active stereovision principle

Multi-Criteria Evaluation of Design Complexity for Patient-Specific Bone Graft

With the rise of modern computer-aided technologies, their use in various different fields is becoming more and more apparent, but more profoundly in the field of medicine. The use of such technology enables the design of complex anatomical structures, often found in different areas of medicine. Maxillofacial and oral fields are becoming more and more popular with the use of such technologies, all leaning toward designing and fabrication of patient-specific implants from a biocompatible material. The level of complexity in personal graft design depends on criteria that describe the bone graft\u27s various properties. This research applies multi-criteria decision aiding in selection of patient-specific bone graft optimal design.Twelve different patient-specific bone-grafts designs have been evaluated by four decision makers who expressed their preferences with direct weighting and revised Simos procedure. Well known VIKOR method was used for multi-criteria decision aiding and the final results verified that the fully curved shape graft design is the least complex while the complex shape is the most demanding from the graft design perspective

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

Computer-aided methods for single stage fibrous dysplasia excision and reconstruction in the zygomatico-orbital complex

Computer Aided Design and Additive Manufacture (CAD/AM) technologies are sufficiently refined and meet the necessary regulatory requirements for routine incorporation into the medical field, with long-standing application in surgeries of the maxillofacial and craniofacial region. They have resulted in better medical care for patients, and faster, more accurate procedures. Despite ever-growing evidence about the advantages of computer aided planning, CAD and AM in surgery, detailed reporting on critical design decisions that enable methodological replication, and the development and establishment of guidelines to ensure safety, are limited. This paper presents a novel application of CAD and AM to a single stage resection and reconstruction of fibrous dysplasia in the zygoma and orbit. It is reported in sufficient fidelity to permit methods replication and design guideline developments in future cases, wherever they occur in the world. The collaborative approach included engineers, designers, surgeons and prosthetists to design patient-specific cutting guides and a custom implant. An iterative design process was used, until the desired shape and function were achieved, for both of the devices. The surgery followed the CAD plan precisely and without problems. Immediate post-operative subjective clinical judgements were of an excellent result. At 19 months post-op, a CT scan was undertaken to verify the clinical and technical outcomes. Dimensional analysis showed maximum deviation of 4.73 mm from the plan to the result, while CAD-Inspection showed that the deviations range between -0.1 and -0.8 mm, and that the majority of deviations are located around the –0.3 mm. Improvements are suggested and conclusions drawn regarding the design decisions considered critical to a successful outcome for this type of procedure in the future

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT 1 and CBCT 2 datasets are 99 %, 98.4 %, 47.2 % and 89.9 %, respectively. For MRI 1 and MRI 2 datasets, the average sensitivity and accuracy values are 99.1 %, 100 %, 75.6 % and 99.6 %, respectively. The average values for the Dice coefficient and Jaccard index for the CBCT 1 and CBCT 2 datasets are 95.88, 0.88, 0.6, and 0.51, respectively, while for MRI 1 and MRI 2 datasets, average values are 0.96, 0.93, 0.81 and 0.7, respectively, which confirms the high accuracy of the proposed method

Development of Expert System for the Selection of 3D Digitization Method in Tangible Cultural Heritage

Selection of an appropriate 3D digitization method in the field of cultural heritage represents a big challenge, especially for non-expert users, such as conservators, art historians, archaeologists etc. Considering the above, the aim of this paper is to develop an expert system for the selection of 3D digitization method, which is the tool for suggesting for the most acceptable 3D digitization method for any individual cultural heritage object. The development of the expert system was presented through the analysis of its components, i.e. main modules – user interface, database and knowledge base. This expert system was based on different parameters defined through theoretical-methodological analysis of representative tangible cultural heritage objects. The database contains technical specifications of various 3D digitization methods, devices, and additional equipment available on market, while the knowledge base defines their limitations. The expert selection system requires as input information details about the cultural heritage object and the end user requirements. During the evaluation phase through the case studies, the system proposed satisfactory solutions depending on the entered input data

Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography

The paper demonstrates a novel methodology for surface extraction of multi-material components (MMCs) on industrial X-ray computed tomography (CT) datasets. The methodology is based on a combination of fuzzy C-means clustering (FCM) and region growing (RG) methods. FCM, used as a preprocessing step, allows proper classification and improvement of different objects boundaries present on industrial X-ray CT datasets. Afterwards, application of RG method enables accurate segmentation of classified and improved X-ray CT datasets. The performance of presented approach has been tested on two CT datasets acquired on an industrial X-ray CT system NIKON XT H 225. It was also compared against two commercial industrial software VGStudio Max v3.1 and GOM Inspect v2018. Obtained results from application of the proposed approach show significant improvement in surface extraction of MMCs in CT datasets, especially in cases of low-density materials such as polymers. Verification has been conducted by obtaining reference measurements using contact coordinate measuring machine (CMM) Contura G2 by CARL ZEISS

Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography

The paper demonstrates a novel methodology for surface extraction of multi-material components (MMCs) on industrial X-ray computed tomography (CT) datasets. The methodology is based on a combination of fuzzy C-means clustering (FCM) and region growing (RG) methods. FCM, used as a preprocessing step, allows proper classification and improvement of different objects boundaries present on industrial X-ray CT datasets. Afterwards, application of RG method enables accurate segmentation of classified and improved X-ray CT datasets. The performance of presented approach has been tested on two CT datasets acquired on an industrial X-ray CT system NIKON XT H 225. It was also compared against two commercial industrial software VGStudio Max v3.1 and GOM Inspect v2018. Obtained results from application of the proposed approach show significant improvement in surface extraction of MMCs in CT datasets, especially in cases of low-density materials such as polymers. Verification has been conducted by obtaining reference measurements using contact coordinate measuring machine (CMM) Contura G2 by CARL ZEISS

Measurement of dental crown wear-In vitro study

The purpose of the study was to test new method for in vitro evaluation of dental material wear with 3D digitization procedure. Thirty dental crowns, made of polyetheretherketone and veneered with composite material, were subjected to wear test. The crown surface was digitized using coordinate measuring machine before and after the performed wear test. Mesh 3D models were reconstructed and average and maximum depth of lost material and volume loss was calculated (GOM Inspect 2016 software). Mean average depth value amounted 12 7 gm, maximum depth value was 42 gm, while mean volume loss was 0.0024 mm(3). The smallest measured values were 4 gm for depth value and 0.0003 mm(3) for volume loss. Coefficient of variation was very high for all tested parameters (>50%) as a result of data inconsistency. Within the limitations of applied methodology, the possibility of using coordinate measuring machine in measurement of dental material wear was confirmed