Craniosynostosis surgery: workflow based on virtual surgical planning, intraoperative navigation and 3D printed patient-specific guides and templates

Craniosynostosis must often be corrected using surgery, by which the affected bone tissue is remodeled. Nowadays, surgical reconstruction relies mostly on the subjective judgement of the surgeon to best restore normal skull shape, since remodeled bone is manually placed and fixed. Slight variations can compromise the cosmetic outcome. The objective of this study was to describe and evaluate a novel workflow for patient-specific correction of craniosynostosis based on intraoperative navigation and 3D printing. The workflow was followed in five patients with craniosynostosis. Virtual surgical planning was performed, and patient-specific cutting guides and templates were designed and manufactured. These guides and templates were used to control osteotomies and bone remodeling. An intraoperative navigation system based on optical tracking made it possible to follow preoperative virtual planning in the operating room through real-time positioning and 3D visualization. Navigation accuracy was estimated using intraoperative surface scanning as the gold-standard. An average error of 0.62 mm and 0.64 mm was obtained in the remodeled frontal region and supraorbital bar, respectively. Intraoperative navigation is an accurate and reproducible technique for correction of craniosynostosis that enables optimal translation of the preoperative plan to the operating room. © 2019, The Author(s).This work has been supported by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, project “PI18/01625”, co-funded by European Regional Development Fund (ERDF), “A way of making Europe”

Robust head CT image registration pipeline for craniosynostosis skull correction surgery

Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of \u3c2.5 mm in the targeted skull region across both the normal subjects and patients. Keywords: image registration, bone, surgery, medical image processing, computerised tomography, deformation, biomechanics, image resolution, optimisation Keywords: robust head CT image registration pipeline, craniosynostosis skull correction surgery, congenital malformation, infant skull, corrective surgery, deformation, optimal correction strategy, patient-specific skull model extraction, presurgical computed tomography image, robust multistage multiresolution registration pipeline, patient-specihc CT image, normal CT images, initial optimisation, very low resolution, mean surface-to-surface distance, template skull, targeted skull regio

A computational framework for evaluating outcomes in infant craniosynostosis reconstruction

Historically, surgical outcomes in craniosynostosis have been evaluated by qualitative analysis, direct and indirect anthropometry, cephalometrics, and CT craniometric analysis. Three-dimensional meshes constructed from 3dMD images acquired on patients with synostosis at multiple times across the course of surgical treatment provide ideal raw data for a novel approach to 3D geometric shape analysis of surgical results. We design a automatic computational framework for evaluating and visualizing the results of infant cranial surgeries based on 3dMD images. The goal of this framework is to assist surgeons in evaluating the efficacy of their surgical techniques. Feedback from surgeons in Texas Children's Hospital confirms that this framework is a robust computational system within which surgical outcomes in synostosis can be accurately and meaningfully evaluated. We also propose an algorithm to generate normative infant cranial models from the input of 3D meshes, which are extracted from CT scans of normal infant skulls. Comparing of the head shape of an affected subject with a normal control will more clearly illustrate in what aspect the subject's head deviates from the norm. Comparing of a post-treatment subject's head shape and an age-matched control would allow assessing of a specific treatment approach or surgical technique

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

The 3D skull 0–4 years: A validated, generative, statistical shape model

BACKGROUND: This study aims to capture the 3D shape of the human skull in a healthy paediatric population (0–4 years old) and construct a generative statistical shape model. METHODS: The skull bones of 178 healthy children (55% male, 20.8 ± 12.9 months) were reconstructed from computed tomography (CT) images. 29 anatomical landmarks were placed on the 3D skull reconstructions. Rotation, translation and size were removed, and all skull meshes were placed in dense correspondence using a dimensionless skull mesh template and a non-rigid iterative closest point algorithm. A 3D morphable model (3DMM) was created using principal component analysis, and intrinsically and geometrically validated with anthropometric measurements. Synthetic skull instances were generated exploiting the 3DMM and validated by comparison of the anthropometric measurements with the selected input population. RESULTS: The 3DMM of the paediatric skull 0–4 years was successfully constructed. The model was reasonably compact - 90% of the model shape variance was captured within the first 10 principal components. The generalisation error, quantifying the ability of the 3DMM to represent shape instances not encountered during training, was 0.47 mm when all model components were used. The specificity value was <0.7 mm demonstrating that novel skull instances generated by the model are realistic. The 3DMM mean shape was representative of the selected population (differences <2%). Overall, good agreement was observed in the anthropometric measures extracted from the selected population, and compared to normative literature data (max difference in the intertemporal distance) and to the synthetic generated cases. CONCLUSION: This study presents a reliable statistical shape model of the paediatric skull 0–4 years that adheres to known skull morphometric measures, can accurately represent unseen skull samples not used during model construction and can generate novel realistic skull instances, thus presenting a solution to limited availability of normative data in this field

3D statistical shape analysis of the face in Apert syndrome

Timely diagnosis of craniofacial syndromes as well as adequate timing and choice of surgical technique are essential for proper care management. Statistical shape models and machine learning approaches are playing an increasing role in Medicine and have proven its usefulness. Frameworks that automate processes have become more popular. The use of 2D photographs for automated syndromic identification has shown its potential with the Face2Gene application. Yet, using 3D shape information without texture has not been studied in such depth. Moreover, the use of these models to understand shape change during growth and its applicability for surgical outcome measurements have not been analysed at length. This thesis presents a framework using state-of-the-art machine learning and computer vision algorithms to explore possibilities for automated syndrome identification based on shape information only. The purpose of this was to enhance understanding of the natural development of the Apert syndromic face and its abnormality as compared to a normative group. An additional method was used to objectify changes as result of facial bipartition distraction, a common surgical correction technique, providing information on the successfulness and on inadequacies in terms of facial normalisation. Growth curves were constructed to further quantify facial abnormalities in Apert syndrome over time along with 3D shape models for intuitive visualisation of the shape variations. Post-operative models were built and compared with age-matched normative data to understand where normalisation is coming short. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation diagnostics and surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

Cleft Palate Craniofac J

ObjectiveWith the current widespread use of 3D facial surface imaging in clinical and research environments, there is a growing demand for high quality craniofacial norms based on 3D imaging technology. The principal goal of the 3D Facial Norms (3DFN) project was to create an interactive, web-based repository of 3D facial images and measurements. Unlike other repositories, users can gain access to both summary-level statistics as well as individual-level data, including 3D facial landmark coordinates, 3D-derived anthropometric measurements, 3D facial surface images and genotypes from every individual in the dataset. The 3DFN database currently consists of 2454 male and female participants ranging in age from 3\u201340 years. These subjects were recruited at four US sites and screened for a history of craniofacial conditions. The goal of this paper is to introduce readers to the 3DFN repository by providing a general overview of the project, explaining the rationale behind the creation of the database, and describing the methods used to collect the data.SupplementSex and age-specific summary statistics (means and standard deviations) and growth curves for every anthropometric measurement in the 3DFN dataset are provided as a supplement. These summary statistics and growth curves can aid clinicians in the assessment of craniofacial dysmorphology.U01 DE020078/DE/NIDCR NIH HHS/United StatesR01 DE016148/DE/NIDCR NIH HHS/United StatesUL1 TR000423/TR/NCATS NIH HHS/United StatesR01 DD000295/DD/NCBDD CDC HHS/United StatesU01 DE020057/DE/NIDCR NIH HHS/United States2017-11-01T00:00:00Z26492185PMC4841760vault:1687

Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis

Clinical diagnosis of craniofacial anomalies requires expert knowledge. Recent studies have shown that artificial intelligence (AI) based facial analysis can match the diagnostic capabilities of expert clinicians in syndrome identification. In general, these systems use 2D images and analyse texture and colour. They are powerful tools for photographic analysis but are not suitable for use with medical imaging modalities such as ultrasound, MRI or CT, and are unable to take shape information into consideration when making a diagnostic prediction. 3D morphable models (3DMMs), and their recently proposed successors, mesh autoencoders, analyse surface topography rather than texture enabling analysis from photography and all common medical imaging modalities and present an alternative to image-based analysis. We present a craniofacial analysis framework for syndrome identification using Convolutional Mesh Autoencoders (CMAs). The models were trained using 3D photographs of the general population (LSFM and LYHM), computed tomography data (CT) scans from healthy infants and patients with 3 genetically distinct craniofacial syndromes (Muenke, Crouzon, Apert). Machine diagnosis outperformed expert clinical diagnosis with an accuracy of 99.98%, sensitivity of 99.95% and specificity of 100%. The diagnostic precision of this technique supports its potential inclusion in clinical decision support systems. Its reliance on 3D topography characterisation make it suitable for AI assisted diagnosis in medical imaging as well as photographic analysis in the clinical setting

Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1 . We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2 , selected using World Health Organization recommendations for growth standards3 . Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5 . The atlas was produced using 1,059 optimal quality, three dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6–8 . The atlas corresponds structurally to published magnetic resonance images9 , but with finer anatomical details in deep grey matter. The between study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks’ gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks’ gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment

Three-Dimensional Morphometric Analysis of the Craniofacial Complex in the Unaffected Relatives of Individuals with Nonsyndromic Orofacial Clefts

Numerous studies have described altered patterns of craniofacial form in the unaffected relatives of individuals with nonsyndromic oral clefts. Unfortunately, results from these studies have been highly variable and have failed to provide a reliable method for discriminating at-risk relatives from controls. In the present study, we compared craniofacial shape between a sample of unaffected relatives (33 females; 14 males) from CL/P multiplex families and an equal number of age/sex/ethnicity-matched controls. A total of 16 x,y,z facial landmark coordinates derived from 3D photogrammetry were analyzed via Euclidean Distance Matrix Analysis (EDMA), while 14 additional linear distances from direct anthropometry were analyzed via t-tests. Variables identified as significantly different (p ≤ 0.10 from EDMA; 0.05 from t-tests) were then entered into a two-group discriminant function analysis. All analyses were carried out for each sex separately. Compared to controls, female unaffected relatives demonstrated increased upper facial width, midface reduction and lateral displacement of the alar cartilage. A single discriminant function was derived (canonical correlation = 0.43; p = 0.01) which correctly classified 70% of female unaffected relatives and 73% of female controls. Male unaffected relatives demonstrated increased upper facial and cranial base width, increased lower facial height and decreased upper facial height. Again, a single discriminant function was derived (canonical correlation = 0.79; p < 0.001) which correctly classified 86% of male unaffected relatives and 93% of male controls. In both males and females, upper facial width contributed most to group discrimination. Based on the discriminant function results, unaffected relatives were classified into risk/liability classes (high risk or low risk) based on the degree of phenotypic divergence from controls. Results suggest that the craniofacial shape differences characterizing unaffected relatives are partly sex-specific and perhaps more pronounced in males. The pattern of relative-control differences observed in both sexes is in broad agreement with previous findings from both humans and animal models. Although preliminary, these results suggest that a quantitative assessment of the craniofacial phenotype may allow for the identification of at-risk individuals within CL/P multiplex families. Importantly, the identification of such individuals could lead to improvements in recurrence risk estimation and gene mapping