Three-dimensional virtual-reality surgical planning and soft-tissue prediction for orthognathic surgery

Complex maxillofacial malformations continue to present challenges in analysis and correction beyond modern technology. The purpose of this paper is to present a virtual-reality workbench for surgeons to perform virtual orthognathic surgical planning and soft-tissue prediction in three dimensions. A resulting surgical planning system, i.e., three-dimensional virtual-reality surgical-planning and soft-tissue prediction for orthognathic surgery, consists of four major stages: computed tomography (CT) data post-processing and reconstruction, three-dimensional (3-D) color facial soft-tissue model generation, virtual surgical planning and simulation, soft-tissue-change preoperative prediction. The surgical planning and simulation are based on a 3-D CT reconstructed bone model, whereas the soft-tissue prediction is based on color texture-mapped and individualized facial soft-tissue model. Our approach is able to provide a quantitative osteotomy-simulated bone model and prediction of postoperative appearance with photorealistic quality. The prediction appearance can be visualized from any arbitrary viewing point using a low-cost personal-computer-based system. This cost-effective solution can be easily adopted in any hospital for daily use.published_or_final_versio

Optimization of craniosynostosis surgery: virtual planning, intraoperative 3D photography and surgical navigation

Mención Internacional en el título de doctorCraniosynostosis is a congenital defect defined as the premature fusion of one or more cranial sutures. This fusion leads to growth restriction and deformation of the cranium, caused by compensatory expansion parallel to the fused sutures. Surgical correction is the preferred treatment in most cases to excise the fused sutures and to normalize cranial shape. Although multiple technological advancements have arisen in the surgical management of craniosynostosis, interventional planning and surgical correction are still highly dependent on the subjective assessment and artistic judgment of craniofacial surgeons. Therefore, there is a high variability in individual surgeon performance and, thus, in the surgical outcomes. The main objective of this thesis was to explore different approaches to improve the surgical management of craniosynostosis by reducing subjectivity in all stages of the process, from the preoperative virtual planning phase to the intraoperative performance. First, we developed a novel framework for automatic planning of craniosynostosis surgery that enables: calculating a patient-specific normative reference shape to target, estimating optimal bone fragments for remodeling, and computing the most appropriate configuration of fragments in order to achieve the desired target cranial shape. Our results showed that automatic plans were accurate and achieved adequate overcorrection with respect to normative morphology. Surgeons’ feedback indicated that the integration of this technology could increase the accuracy and reduce the duration of the preoperative planning phase. Second, we validated the use of hand-held 3D photography for intraoperative evaluation of the surgical outcome. The accuracy of this technology for 3D modeling and morphology quantification was evaluated using computed tomography imaging as gold-standard. Our results demonstrated that 3D photography could be used to perform accurate 3D reconstructions of the anatomy during surgical interventions and to measure morphological metrics to provide feedback to the surgical team. This technology presents a valuable alternative to computed tomography imaging and can be easily integrated into the current surgical workflow to assist during the intervention. Also, we developed an intraoperative navigation system to provide real-time guidance during craniosynostosis surgeries. This system, based on optical tracking, enables to record the positions of remodeled bone fragments and compare them with the target virtual surgical plan. Our navigation system is based on patient-specific surgical guides, which fit into the patient’s anatomy, to perform patient-to-image registration. In addition, our workflow does not rely on patient’s head immobilization or invasive attachment of dynamic reference frames. After testing our system in five craniosynostosis surgeries, our results demonstrated a high navigation accuracy and optimal surgical outcomes in all cases. Furthermore, the use of navigation did not substantially increase the operative time. Finally, we investigated the use of augmented reality technology as an alternative to navigation for surgical guidance in craniosynostosis surgery. We developed an augmented reality application to visualize the virtual surgical plan overlaid on the surgical field, indicating the predefined osteotomy locations and target bone fragment positions. Our results demonstrated that augmented reality provides sub-millimetric accuracy when guiding both osteotomy and remodeling phases during open cranial vault remodeling. Surgeons’ feedback indicated that this technology could be integrated into the current surgical workflow for the treatment of craniosynostosis. To conclude, in this thesis we evaluated multiple technological advancements to improve the surgical management of craniosynostosis. The integration of these developments into the surgical workflow of craniosynostosis will positively impact the surgical outcomes, increase the efficiency of surgical interventions, and reduce the variability between surgeons and institutions.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidente: Norberto Antonio Malpica González.- Secretario: María Arrate Muñoz Barrutia.- Vocal: Tamas Ung

Advent of artificial intelligence in orthognathic surgery: Advancements and challenges

Orthognathic surgery is a procedure used to correct facial deformities and jaw bone misalignment. The use of technology, specifically virtual surgical planning (VSP), has become increasingly prevalent in preoperative planning for orthognathic surgery. High-resolution computed tomography (CT) imaging has enabled computer-aided modelling. Artificial intelligence (AI) implementation has transformed orthognathic surgery methodology. This article highlights the latest market trends and modern-day advancements in the field, including the conventional and surgery first approach for orthognathic surgery. The use of computer-aided surgical simulation (CASS) in VSP for orthognathic surgery was studied. The different software used for orthognathic surgical planning and the detailed protocol followed during the surgery, including the preoperative procedure were discussed along with utilisation of 3-dimension cone-beam computed tomography (3D CBCT) images for surgical planning. The implementation of VSP with CASS had significantly enhanced the accuracy and efficiency of orthognathic surgery for dentofacial deformity correction. The use of technology allowed improved preoperative planning, resulting in better outcomes for patients. The study of different software for orthognathic surgical planning and the protocol followed during surgery has provided valuable insight into the surgery. The continued advancement of technology in orthognathic surgery is promising for the field and for the patients

Development and Validation Methodology of the Nuss Procedure Surgical Planner

Pectus excavatum (PE) is a congenital chest wall deformity which is characterized, in most cases, by a deep depression of the sternum. A minimally invasive technique for the repair of PE (MIRPE), often referred to as the Nuss procedure, has been proven to be more advantageous than many other PE treatment techniques. The Nuss procedure consists of placement of a metal bar(s) underneath the sternum, thereby forcibly changing the geometry of the ribcage. Because of the prevalence of PE and the popularity of the Nuss procedure, the demand to perform this surgery is greater than ever. Therefore, a Nuss procedure surgical planner would be an invaluable planning tool ensuring an optimal physiological and aesthetic outcome. In this dissertation, the development and validation of the Nuss procedure planner is investigated. First, a generic model of the ribcage is developed to overcome the issue of missing cartilage when PE ribcages are segmented and facilitate the flexibility of the model to accommodate a range of deformity. Then, the CT data collected from actual patients with PE is used to create a set of patient specific finite element models. Based on finite element analyses performed over those models, a set force-displacement data set is created. This data is used to train an artificial neural network to generalize the data set. In order to evaluate the planning process, a methodology which uses an average shape of the chest for comparison with results of the Nuss procedure planner is developed. This method is based on a sample of normal chests obtained from the ODU male population using laser surface scanning and overcomes challenging issues such as hole-filling, scan registration and consistency. Additionally, this planning simulator is optimized so that it can be used for training purposes. Haptic feedback and inertial tracking is implemented, and the force-displacement model is approximated using a neural network approach and evaluated for real-time performance. The results show that it is possible to utilize this approximation of the force-displacement model for the Nuss procedure simulator. The detailed ribcage model achieves real-time performance

Robotics in Dentistry : A Narrative Review

Background: Robotics is progressing rapidly. The aim of this study was to provide a comprehensive overview of the basic and applied research status of robotics in dentistry and discusses its development and application prospects in several major professional fields of dentistry. Methods: A literature search was conducted on databases: MEDLINE, IEEE and Cochrane Library, using MeSH terms: [“robotics” and “dentistry”]. Result: Forty-nine articles were eventually selected according to certain inclusion criteria. There were 12 studies on prosthodontics, reaching 24%; 11 studies were on dental implantology, accounting for 23%. Scholars from China published the most articles, followed by Japan and the United States. The number of articles published between 2011 and 2015 was the largest. Conclusions: With the advancement of science and technology, the applications of robots in dental medicine has promoted the development of intelligent, precise, and minimally invasive dental treatments. Currently, robots are used in basic and applied research in various specialized fields of dentistry. Automatic tooth-crown-preparation robots, tooth-arrangement robots, drilling robots, and orthodontic archwire-bending robots that meet clinical requirements have been developed. We believe that in the near future, robots will change the existing dental treatment model and guide new directions for further development

Advanced Applications of Rapid Prototyping Technology in Modern Engineering

Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems

Facial soft tissue segmentation

The importance of the face for socio-ecological interaction is the cause for a high demand on any surgical intervention on the facial musculo-skeletal system. Bones and soft-tissues are of major importance for any facial surgical treatment to guarantee an optimal, functional and aesthetical result. For this reason, surgeons want to pre-operatively plan, simulate and predict the outcome of the surgery allowing for shorter operation times and improved quality. Accurate simulation requires exact segmentation knowledge of the facial tissues. Thus semi-automatic segmentation techniques are required. This thesis proposes semi-automatic methods for segmentation of the facial soft-tissues, such as muscles, skin and fat, from CT and MRI datasets, using a Markov Random Fields (MRF) framework. Due to image noise, artifacts, weak edges and multiple objects of similar appearance in close proximity, it is difficult to segment the object of interest by using image information alone. Segmentations would leak at weak edges into neighboring structures that have a similar intensity profile. To overcome this problem, additional shape knowledge is incorporated in the energy function which can then be minimized using Graph-Cuts (GC). Incremental approaches by incorporating additional prior shape knowledge are presented. The proposed approaches are not object specific and can be applied to segment any class of objects be that anatomical or non-anatomical from medical or non-medical image datasets, whenever a statistical model is present. In the first approach a 3D mean shape template is used as shape prior, which is integrated into the MRF based energy function. Here, the shape knowledge is encoded into the data and the smoothness terms of the energy function that constrains the segmented parts to a reasonable shape. In the second approach, to improve handling of shape variations naturally found in the population, the fixed shape template is replaced by a more robust 3D statistical shape model based on Probabilistic Principal Component Analysis (PPCA). The advantages of using the Probabilistic PCA are that it allows reconstructing the optimal shape and computing the remaining variance of the statistical model from partial information. By using an iterative method, the statistical shape model is then refined using image based cues to get a better fitting of the statistical model to the patient's muscle anatomy. These image cues are based on the segmented muscle, edge information and intensity likelihood of the muscle. Here, a linear shape update mechanism is used to fit the statistical model to the image based cues. In the third approach, the shape refinement step is further improved by using a non-linear shape update mechanism where vertices of the 3D mesh of the statistical model incur the non-linear penalty depending on the remaining variability of the vertex. The non-linear shape update mechanism provides a more accurate shape update and helps in a finer shape fitting of the statistical model to the image based cues in areas where the shape variability is high. Finally, a unified approach is presented to segment the relevant facial muscles and the remaining facial soft-tissues (skin and fat). One soft-tissue layer is removed at a time such as the head and non-head regions followed by the skin. In the next step, bones are removed from the dataset, followed by the separation of the brain and non-brain regions as well as the removal of air cavities. Afterwards, facial fat is segmented using the standard Graph-Cuts approach. After separating the important anatomical structures, finally, a 3D fixed shape template mesh of the facial muscles is used to segment the relevant facial muscles. The proposed methods are tested on the challenging example of segmenting the masseter muscle. The datasets were noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. dental fillings and dental implants. Qualitative and quantitative experimental results show that by incorporating prior shape knowledge leaking can be effectively constrained to obtain better segmentation results