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Robertson E, Boulanger P, Kwan P, Louie G, Aalto D. Improving Cranial Vault Remodeling for Unilateral Coronal Craniosynostosis-Introducing Automated Surgical Planning. Craniomaxillofac Trauma Reconstr 2024; 17:203-213. [PMID: 39377079 PMCID: PMC11456203 DOI: 10.1177/19433875231178912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/09/2024] Open
Abstract
Study Design Cranial vault remodeling (CVR) for unicoronal synostosis is challenging due to the asymmetric nature of the deformity. Computer-automated surgical planning has demonstrated success in reducing the subjectivity of decision making in CVR in symmetric subtypes. This proof of concept study presents a novel method using Boolean functions and image registration to automatically suggest surgical steps in asymmetric craniosynostosis. Objective The objective of this study is to introduce automated surgical planning into a CVR virtual workflow for an asymmetric craniosynostosis subtype. Methods Virtual workflows were developed using Geomagic Freeform Plus software. Hausdorff distances and color maps were used to compare reconstruction models to the preoperative model and a control skull. Reconstruction models were rated as high or low performing based on similarity to the normal skull and the amount of advancement of the frontal bone (FB) and supra-orbital bar (SOB). Fifteen partially and fully automated workflow iterations were carried out. Results FB and SOB advancement ranged from 3.08 to 10.48 mm, and -1.75 to 7.78 mm, respectively. Regarding distance from a normal skull, models ranged from .85 to 5.49 mm at the FB and 5.40 to 10.84 mm at the SOB. An advancement of 8.43 mm at the FB and 7.73 mm at the SOB was achieved in the highest performing model, and it differed to a comparative normal skull by .02 mm at the FB and .48 mm at the SOB. Conclusions This is the first known attempt at developing an automated virtual surgical workflow for CVR in asymmetric craniosynostosis. Key regions of interest were outlined using Boolean operations, and surgical steps were suggested using image registration. These techniques improved post-operative skull morphology.
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Affiliation(s)
- Emilie Robertson
- Division of Plastic Surgery, University of Alberta, Edmonton, AB, Canada
- Institute for Reconstructive Sciences in Medicine, Misericordia Hospital, Edmonton, AB, Canada
| | - Pierre Boulanger
- Department of Computing Sciences, University of Alberta, Edmonton, AB, Canada
| | - Peter Kwan
- Division of Plastic Surgery, University of Alberta, Edmonton, AB, Canada
| | - Gorman Louie
- Division of Plastic Surgery, University of Alberta, Edmonton, AB, Canada
| | - Daniel Aalto
- Institute for Reconstructive Sciences in Medicine, Misericordia Hospital, Edmonton, AB, Canada
- Department of Rehabilitation Sciences, Division of Communication Sciences and Disorders, University of Alberta, Edmonton, AB, Canada
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Liu J, Froelicher JH, French B, Linguraru MG, Porras AR. Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis. Sci Rep 2023; 13:20557. [PMID: 37996454 PMCID: PMC10667230 DOI: 10.1038/s41598-023-47622-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.
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Affiliation(s)
- Jiawei Liu
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
| | - Joseph H Froelicher
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Brooke French
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, CO, 80045, USA
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, 20010, USA
- Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
| | - Antonio R Porras
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, CO, 80045, USA
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Pediatric Neurosurgery, Children's Hospital Colorado, Aurora, CO, 80045, USA
- Departments of Pediatrics and Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
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Liu J, Xing F, Shaikh A, French B, Linguraru MG, Porras AR. Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3117-3126. [PMID: 37216247 PMCID: PMC10760565 DOI: 10.1109/tmi.2023.3278493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.
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Data-driven Normative Reference of Pediatric Cranial Bone Development. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2022; 10:e4457. [PMID: 35983543 PMCID: PMC9377678 DOI: 10.1097/gox.0000000000004457] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/10/2022] [Indexed: 11/26/2022]
Abstract
Available normative references of cranial bone development and suture fusion are incomplete or based on simplified assumptions due to the lack of large datasets. We present a fully data-driven normative model that represents the age- and sex-specific variability of bone shape, thickness, and density between birth and 10 years of age at every location of the calvaria.
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Porras AR, Keating RF, Lee JS, Linguraru MG. Predictive Statistical Model of Early Cranial Development. IEEE Trans Biomed Eng 2022; 69:537-546. [PMID: 34324420 PMCID: PMC8776594 DOI: 10.1109/tbme.2021.3100745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We present a data-driven method to build a spatiotemporal statistical shape model predictive of normal cranial growth from birth to the age of 2 years. METHODS The model was constructed using a normative cross-sectional computed tomography image dataset of 278 subjects. First, we propose a new standard representation of the calvaria using spherical maps to establish anatomical correspondences between subjects at the cranial sutures - the main areas of cranial bone expansion. Then, we model the cranial bone shape as a bilinear function of two factors: inter-subject anatomical variability and temporal growth. We estimate these factors using principal component analysis on the spatial and temporal dimensions, using a novel coarse-to-fine temporal multi-resolution approach to mitigate the lack of longitudinal images of the same patient. RESULTS Our model achieved an accuracy of 1.54 ± 1.05 mm predicting development on an independent longitudinal dataset. We also used the model to calculate the cranial volume, cephalic index and cranial bone surface changes during the first two years of age, which were in agreement with clinical observations. SIGNIFICANCE To our knowledge, this is the first data-driven and personalized predictive model of cranial bone shape development during infancy and it can serve as a baseline to study abnormal growth patterns in the population.
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Affiliation(s)
- Antonio R. Porras
- Department of Biostatistics and Informatics at the Colorado School of Public Health and the Department of Pediatrics at the School of Medicine, University of Colorado Anschutz Medical Campus.,Departments of Plastic & Reconstructive Surgery and Neurosurgery at the Children’s Hospital Colorado, Aurora. CO, 80045, USA
| | - Robert F. Keating
- Department of Neurosurgery at the Children’s National Hospital, Washington, DC, 20010, USA
| | - Janice S. Lee
- National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute of Pediatric Surgical Innovation at Children’s National Hospital, Washington, DC, 20010, USA.,Departments of Radiology and Pediatrics at the George Washington University School of Medicine and Health Sciences, Washington, DC, 20052, USA
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Robertson E, Kwan P, Louie G, Boulanger P, Aalto D. Test-retest validation of a cranial deformity index in unilateral coronal craniosynostosis. Comput Methods Biomech Biomed Engin 2020; 23:1247-1259. [PMID: 32691624 DOI: 10.1080/10255842.2020.1795143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Unilateral coronal craniosynostosis (UCS) affects many infants resulting in abnormalities affecting the forehead and orbits. As a result, the deformity caused by UCS is very noticeable and there are several surgical treatment options available to normalize the head shape. However, there is a lack of consistently used outcome measures, resulting in difficulty assessing surgical outcomes and on-going debate over optimal treatments. Current techniques to quantify deformity in UCS are cumbersome, provide limited information, or are based on subjective assessments. In this study, a cranial deformity index was developed to quantify abnormality at the frontal bones for UCS that is accessible, user-friendly, and generates objective surface distance measurements. The cranial deformity index is defined as the Euclidean distance at the point of the largest deviation between the deformed skull compared to a reference skull. In addition, the index was successfully used to quantify post-operative changes in a single case of UCS that underwent corrective surgery. The reproducibility of the index was assessed using test-retest reliability and was demonstrated to be highly reproducible (ICC = 0.93). A user-friendly measurement index that is based on open-source software may be a valuable tool for surgical teams. In addition, this information can augment the consultation experience for patients and their families.
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Affiliation(s)
- Emilie Robertson
- Division of Plastic and Reconstructive Surgery, University of Alberta, Edmonton, Canada.,Faculty of Rehabilitation Medicine, Department of Communication Sciences and Disorders, University of Alberta, Edmonton, Canada.,Institute for Reconstructive Sciences in Medicine, Misericordia Community Hospital, Edmonton, Canada
| | - Peter Kwan
- Division of Plastic and Reconstructive Surgery, University of Alberta, Edmonton, Canada
| | - Gorman Louie
- Division of Plastic and Reconstructive Surgery, University of Alberta, Edmonton, Canada
| | - Pierre Boulanger
- Department of Computing Sciences, University of Alberta, Edmonton, Canada
| | - Daniel Aalto
- Faculty of Rehabilitation Medicine, Department of Communication Sciences and Disorders, University of Alberta, Edmonton, Canada.,Institute for Reconstructive Sciences in Medicine, Misericordia Community Hospital, Edmonton, Canada
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