Publications by authors named "James J Xia"

78 Publications

A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect.

Med Image Anal 2021 May 5;72:102095. Epub 2021 May 5.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, 407 E 61st St, New York, NY 10065, USA. Electronic address:

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect.
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http://dx.doi.org/10.1016/j.media.2021.102095DOI Listing
May 2021

Diverse data augmentation for learning image segmentation with cross-modality annotations.

Med Image Anal 2021 Jul 20;71:102060. Epub 2021 Apr 20.

Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA. Electronic address:

The dearth of annotated data is a major hurdle in building reliable image segmentation models. Manual annotation of medical images is tedious, time-consuming, and significantly variable across imaging modalities. The need for annotation can be ameliorated by leveraging an annotation-rich source modality in learning a segmentation model for an annotation-poor target modality. In this paper, we introduce a diverse data augmentation generative adversarial network (DDA-GAN) to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. This is achieved by generating diverse augmented data for the target domain by one-to-many source-to-target translation. The DDA-GAN uses unpaired images from the source and target domains and is an end-to-end convolutional neural network that (i) explicitly disentangles domain-invariant structural features related to segmentation from domain-specific appearance features, (ii) combines structural features from the source domain with appearance features randomly sampled from the target domain for data augmentation, and (iii) train the segmentation model with the augmented data in the target domain and the annotations from the source domain. The effectiveness of our method is demonstrated both qualitatively and quantitatively in comparison with the state of the art for segmentation of craniomaxillofacial bony structures via MRI and cardiac substructures via CT.
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http://dx.doi.org/10.1016/j.media.2021.102060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8184609PMC
July 2021

A Better Understanding of Unilateral Condylar Hyperplasia of the Mandible.

J Oral Maxillofac Surg 2021 May 29;79(5):1122-1132. Epub 2020 Dec 29.

Director of the Surgical Planning Laboratory, Oral and Maxillofacial Surgery Department, Houston Methodist Research Institute; Professor of Oral and Maxillofacial Surgery, Houston Methodist Academic Institute, Houston, TX; and Professor of Surgery (Oral and Maxillofacial Surgery), Weill-Cornell Medical College, New York, NY. Electronic address:

Purpose: Our current understanding of unilateral condylar hyperplasia (UCH) was put forth by Obwegeser. He hypothesized that UCH is 2 separate conditions: hemimandibular hyperplasia and hemimandibular elongation. This hypothesis was based on the following 3 assumptions: 1) the direction of overgrowth, in UCH, is bimodal-vertical or horizontal, with rare cases growing obliquely; 2) UCH can expand a hemimandible with and without significant condylar enlargement; and 3) there is an association between the condylar expansion and the direction of overgrowth-minimal expansion resulting in horizontal growth and significant enlargement causing vertical displacement. The purpose of this study was to test these assumptions.

Patients And Methods: We analyzed the computed tomography scans of 40 patients with UCH. First, we used a Silverman Cluster analysis to determine how the direction of overgrowth is distributed in the UCH population. Next, we evaluated the relationship between hemimandibular overgrowth and condylar enlargement to confirm that overgrowth can occur independently of condylar expansion. Finally, we assessed the relationship between the degree of condylar enlargement and the direction of overgrowth to ascertain if condylar expansion determines the direction of growth.

Results: Our first investigation demonstrates that the general impression that UCH is bimodal is wrong. The growth vectors in UCH are unimodally distributed, with the vast majority of cases growing diagonally. Our second investigation confirms the observation that UCH can expand a hemimandible with and without significant condylar enlargement. Our last investigation determined that in UCH, there is no association between the degree of condylar expansion and the direction of the overgrowth.

Conclusions: The results of this study disprove the idea that UCH is 2 different conditions: hemimandibular hyperplasia and hemimandibular elongation. It also provides new insights about the pathophysiology of UCH.
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http://dx.doi.org/10.1016/j.joms.2020.12.034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096674PMC
May 2021

Validity of Medical Insurance Guidelines for Orthognathic Surgery.

J Oral Maxillofac Surg 2021 Mar 24;79(3):672-684. Epub 2020 Nov 24.

Director of Surgical Planning Laboratory, Oral and Maxillofacial Surgery Department, Houston Methodist Hospital, Houston, TX; Professor of Oral and Maxillofacial Surgery, Houston Methodist Academic Institute, Houston, TX; and Professor of Surgery (Oral and Maxillofacial Surgery), Weill-Cornell Medical College, New York, NY. Electronic address:

Purpose: The purpose of this study was to assess the validity of the medical insurance guidelines for orthognathic surgery used by the major American medical insurance companies.

Materials And Methods: This study assessed the validity of the medical insurance guidelines for orthognathic surgery used by Aetna, Anthem Blue Cross Blue Shield (BCBS), Cigna, Humana, and UnitedHealthcare (UHC). To evaluate the validity, we calculated the approval and denial rates of the 5 guidelines when we used them to assess the medical necessity for a control group of carefully selected patients. Patients were included in the control group if they met the criteria of a "prudent provider," crafted for this study. All rejected cases were analyzed to determine the root cause of the denials. The validity of the guidelines was also ascertained by determining their completeness and correctness.

Results: The current study proves that no insurance guideline is in agreement with the criteria of a "prudent provider." When applied to carefully chosen patients, the requirements of BCBS, Aetna, Humana, and Cigna produce modest rejection rates of 6 to 12%. UHC is an outlier. Its guideline rejects 86% of patients, a rate about 7 times higher than its peers. Insurance guidelines disqualified patients for 3 different reasons: 1) no significant jaw deformity, 2) no demonstrable health impairment, and 3) the etiology of the condition is not a covered benefit. Additional evaluations demonstrate that the private insurance guidelines are incomplete, and at times, incorrect.

Conclusions: This study shows that the medical insurance guidelines for orthognathic surgery used by the major American medical insurance plans need revision. The most consequential flaw was considering etiology in judging medical necessity. Fortunately, only one company adopted this policy. Moreover, all guidelines have omissions and errors in the way jaw deformity is determined and how health impairment is determined.
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http://dx.doi.org/10.1016/j.joms.2020.11.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925386PMC
March 2021

Anatomy-Regularized Representation Learning for Cross-Modality Medical Image Segmentation.

IEEE Trans Med Imaging 2021 01 29;40(1):274-285. Epub 2020 Dec 29.

An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an assumption that different modalities share the same anatomical structure information. However, since these methods commonly use voxel/pixel-wise cycle-consistency to regularize the mappings between modalities, high-level semantic information is not necessarily preserved. In this paper, we propose a novel anatomy-regularized representation learning approach for segmentation-oriented cross-modality image synthesis. It learns a common feature encoding across different modalities to form a shared latent space, where 1) the input and its synthesis present consistent anatomical structure information, and 2) the transformation between two images in one domain is preserved by their syntheses in another domain. We applied our method to the tasks of cross-modality skull segmentation and cardiac substructure segmentation. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art cross-modality medical image segmentation methods.
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http://dx.doi.org/10.1109/TMI.2020.3025133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8120796PMC
January 2021

Estimating Reference Shape Model for Personalized Surgical Reconstruction of Craniomaxillofacial Defects.

IEEE Trans Biomed Eng 2021 Feb 20;68(2):362-373. Epub 2021 Jan 20.

Objective: To estimate a patient-specific reference bone shape model for a patient with craniomaxillofacial (CMF) defects due to facial trauma.

Methods: We proposed an automatic facial bone shape estimation framework using pre-traumatic conventional portrait photos and post-traumatic head computed tomography (CT) scans via a 3D face reconstruction and a deformable shape model. Specifically, a three-dimensional (3D) face was first reconstructed from the patient's pre-traumatic portrait photos. Second, a correlation model between the skin and bone surfaces was constructed using a sparse representation based on the CT images of training normal subjects. Third, by feeding the reconstructed 3D face into the correlation model, an initial reference shape model was generated. In addition, we refined the initial estimation by applying non-rigid surface matching between the initially estimated shape and the patient's post-traumatic bone based on the adaptive-focus deformable shape model (AFDSM). Furthermore, a statistical shape model, built from the training normal subjects, was utilized to constrain the deformation process to avoid overfitting.

Results And Conclusion: The proposed method was evaluated using both synthetic and real patient data. Experimental results show that the patient's abnormal facial bony structure can be recovered using our method, and the estimated reference shape model is considered clinically acceptable by an experienced CMF surgeon.

Significance: The proposed method is more suitable to the complex CMF defects for CMF reconstructive surgical planning.
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http://dx.doi.org/10.1109/TBME.2020.2990586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163108PMC
February 2021

An automatic approach to establish clinically desired final dental occlusion for one-piece maxillary orthognathic surgery.

Int J Comput Assist Radiol Surg 2020 Nov 25;15(11):1763-1773. Epub 2020 Feb 25.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, USA.

Purpose: One critical step in routine orthognathic surgery is to reestablish a desired final dental occlusion. Traditionally, the final occlusion is established by hand articulating stone dental models. To date, there are still no effective solutions to establish the final occlusion in computer-aided surgical simulation. In this study, we consider the most common one-piece maxillary orthognathic surgery and propose a three-stage approach to digitally and automatically establish the desired final dental occlusion.

Methods: The process includes three stages: (1) extraction of points of interest and teeth landmarks from a pair of upper and lower dental models; (2) establishment of Midline-Canine-Molar (M-C-M) relationship following the clinical criteria on these three regions; and (3) fine alignment of upper and lower teeth with maximum contacts without breaking the established M-C-M relationship. Our method has been quantitatively and qualitatively validated using 18 pairs of dental models.

Results: Qualitatively, experienced orthodontists assess the algorithm-articulated and hand-articulated occlusions while being blind to the methods used. They agreed that occlusion results of the two methods are equally good. Quantitatively, we measure and compare the distances between selected landmarks on upper and lower teeth for both algorithm-articulated and hand-articulated occlusions. The results showed that there was no statistically significant difference between the algorithm-articulated and hand-articulated occlusions.

Conclusion: The proposed three-stage automatic dental articulation method is able to articulate the digital dental model to the clinically desired final occlusion accurately and efficiently. It allows doctors to completely eliminate the use of stone dental models in the future.
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http://dx.doi.org/10.1007/s11548-020-02125-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484002PMC
November 2020

Clinical Evaluation of Digital Dental Articulation for One-Piece Maxillary Surgery.

J Oral Maxillofac Surg 2020 May 7;78(5):799-805. Epub 2020 Jan 7.

Professor, Department of Oral and Maxillofacial Surgery, and Director, Surgical Planning Laboratory, Houston Methodist, Houston, TX; and Professor of Surgery (Oral and Maxillofacial Surgery) Department of Surgery, Joan & Sanford I. Weill Medical College of Cornell University, New York, NY. Electronic address:

Purpose: Methods for digital dental alignment are not readily available to automatically articulate the upper and lower jaw models. The purpose of the present study was to assess the accuracy of our newly developed 3-stage automatic digital articulation approach by comparing it with the reference standard of orthodontist-articulated occlusion.

Materials And Methods: Thirty pairs of stone dental models from double-jaw orthognathic surgery patients who had undergone 1-piece Le Fort I osteotomy were used. Two experienced orthodontists manually articulated the models to their perceived final occlusion for surgery. Each pair of models was then scanned twice-while in the orthodontist-determined occlusion and again with the upper and lower models separated and positioned randomly. The separately scanned models were automatically articulated to the final occlusion using our 3-stage algorithm, resulting in an algorithm-articulated occlusion (experimental group). The models scanned together represented the manually articulated occlusion (control group). A qualitative evaluation was completed using a 3-point categorical scale by the same orthodontists, who were unaware of the methods used to articulate the models. A quantitative evaluation was also completed to determine whether any differences were present in the midline, canine, and molar relationships between the algorithm-determined and manually articulated occlusions using repeated measures analysis of variance (ANOVA). Finally, the mean ± standard deviation values were computed to determine the differences between the 2 methods.

Results: The results of the qualitative evaluation revealed that all the algorithm-articulated occlusions were as good as the manually articulated ones. The results of the repeated measures ANOVA found no statistically significant differences between the 2 methods [F(1,28) = 0.03; P = .87]. The mean differences between the 2 methods were all within 0.2 mm.

Conclusions: The results of our study have demonstrated that dental models can be accurately, reliably, and automatically articulated using our 3-stage algorithm approach, meeting the reference standard of orthodontist-articulated occlusion.
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http://dx.doi.org/10.1016/j.joms.2019.12.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265171PMC
May 2020

A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.

Med Image Comput Comput Assist Interv 2019 Oct 10;11768:336-344. Epub 2019 Oct 10.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, USA.

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is generated based on accurately digitized lip surface landmarks. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect in addition to the mucosa sliding effect. The soft-tissue change is then simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. A preliminary evaluation of prediction accuracy was conducted using retrospective clinical data. The results showed that there was a significant prediction accuracy improvement in the lip region when the realistic lip sliding effect was applied along with the mucosa sliding effect.
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http://dx.doi.org/10.1007/978-3-030-32254-0_38DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934101PMC
October 2019

An Automatic Approach to Reestablish Final Dental Occlusion for 1-Piece Maxillary Orthognathic Surgery.

Med Image Comput Comput Assist Interv 2019 Oct 10;11768:345-353. Epub 2019 Oct 10.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX.

Accurately establishing a desired final dental occlusion of the upper and lower teeth is a critical step in orthognathic surgical planning. Traditionally, the final occlusion is established by hand-articulating the stone dental models. However, this process is inappropriate to digitally plan the orthognathic surgery using computer-aided surgical simulation. To date, there is no effective method of digitally establishing final occlusion. We propose a 3-stage approach to digitally and automatically establish a desired final dental occlusion for 1-piece maxillary orthognathic surgery, including: 1) to automatically extract points of interest and four key teeth landmarks from the occlusal surfaces; 2) to align the upper and lower teeth to a clinically desired Midline-Canine-Molar relationship by minimization of sum of distances between them; and 3) to finely align the upper and lower teeth to a maximum contact with the constraints of collision and clinical criteria. The proposed method was evaluated qualitatively and quantitatively and proved to be effective and accurate.
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http://dx.doi.org/10.1007/978-3-030-32254-0_39DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6914315PMC
October 2019

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects.

Med Image Comput Comput Assist Interv 2019 Oct 10;11768:327-335. Epub 2019 Oct 10.

BRIC and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, USA.

In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient's pre-traumatic photographs. An initial estimation of the patient's normal bony shape is then obtained with the reconstructed facial surface via sparse representation using a dictionary of paired facial and bony surfaces of normal subjects. We further refine the bony shape model by deforming the initial bony shape model to the post-traumatic 3D CT bony model, regularized by a statistical shape model built from a database of normal subjects. Experimental results show that our method is capable of effectively recovering the patient's normal facial bony shape in regions with defects, allowing CMF surgical planning to be performed precisely for a wider range of defects caused by trauma.
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http://dx.doi.org/10.1007/978-3-030-32254-0_37DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6910247PMC
October 2019

Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization.

Med Image Anal 2020 02 23;60:101621. Epub 2019 Nov 23.

Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address:

Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization.
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http://dx.doi.org/10.1016/j.media.2019.101621DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7360136PMC
February 2020

One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures.

IEEE Trans Med Imaging 2020 03 14;39(3):787-796. Epub 2019 Aug 14.

Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.
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http://dx.doi.org/10.1109/TMI.2019.2935409DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219540PMC
March 2020

Both the Observer's Expertise and the Subject's Facial Symmetry Can Affect Anatomical Position of the Head.

J Oral Maxillofac Surg 2019 Feb 11;77(2):406.e1-406.e9. Epub 2018 Oct 11.

Director, Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX; Professor of Oral and Maxillofacial Surgery, Institute for Academic Medicine, Houston Methodist Hospital, Houston, TX; and Professor of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, NY. Electronic address:

Purpose: It is easier to judge facial deformity when the patient's head is in anatomic position. The purposes of this study were to determine 1) whether a group of expert observers would agree more than a group of nonexperts on what is the correct anatomic position of the head, 2) whether there would be more variation in the alignment of an asymmetrical face compared with a symmetrical one, and 3) whether the alignments of experts would be more repeatable than those of nonexperts.

Materials And Methods: Thirty-one orthodontists (experts) and 31 dental students (nonexperts) were recruited for this mixed-model study. They were shown randomly oriented 3-dimensional head photographs of an adult with a symmetrical face and an adolescent with an asymmetrical face. In viewing software, the observers oriented the images into anatomic position. They repeated the orientations 4 weeks later. Data were analyzed using a generalized linear model and Bland-Altman plots. The primary predictor variables were experience and symmetry status. The outcome variable was the anatomic position of the head. The other variables of interest included time and orientation direction.

Results: There was a statistically significant difference between measurements completed by experts and nonexperts (F = 14.83; P < .01). The interaction between expertise and symmetrical status showed a statistically significant difference between symmetrical and asymmetrical faces in the expert and nonexpert groups (F = 9.93; P = .003). The interaction between expertise and time showed a statistically significant difference in measurement over time in the expert and nonexpert groups (F = 4.66; P = .03).

Conclusions: The study shows that experts can set a head into anatomic position better than nonexperts. In addition, facial asymmetry has a profound effect on the ability of an observer to align a head in the correct anatomic position. Moreover, observer-guided alignment is not reproducible.
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http://dx.doi.org/10.1016/j.joms.2018.09.037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359970PMC
February 2019

Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization by Context-Guided Fully Convolutional Networks.

Med Image Comput Comput Assist Interv 2017 Sep 4;10434:720-728. Epub 2017 Sep 4.

Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA.

Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association. In addition, these methods usually ignore the spatial context information (, displacements from voxels to landmarks) in CBCT images. To this end, we propose a context-guided fully convolutional network (FCN) for joint bone segmentation and landmark digitization. Specifically, we first train an FCN to learn the to capture the spatial context information in CBCT images. Using the learned displacement maps as guidance information, we further develop a multi-task FCN to jointly perform bone segmentation and landmark digitization. Our method has been evaluated on 107 subjects from two centers, and the experimental results show that our method is superior to the state-of-the-art methods in both bone segmentation and landmark digitization.
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http://dx.doi.org/10.1007/978-3-319-66185-8_81DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786437PMC
September 2017

An eFTD-VP framework for efficiently generating patient-specific anatomically detailed facial soft tissue FE mesh for craniomaxillofacial surgery simulation.

Biomech Model Mechanobiol 2018 Apr 12;17(2):387-402. Epub 2017 Oct 12.

Department of Oral and Craniomaxillofacial Surgery, Shanghai 9th Peoples Hospital, Shanghai Jiaotong University School of Medicine and Shanghai Key Laboratory of Stomatology, Shanghai, China.

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft tissue changes following osteotomy. This can only be achieved by using an anatomically detailed facial soft tissue model. The current state-of-the-art of model generation is not appropriate to clinical applications due to the time-intensive nature of manual segmentation and volumetric mesh generation. The conventional patient-specific finite element (FE) mesh generation methods are to deform a template FE mesh to match the shape of a patient based on registration. However, these methods commonly produce element distortion. Additionally, the mesh density for patients depends on that of the template model. It could not be adjusted to conduct mesh density sensitivity analysis. In this study, we propose a new framework of patient-specific facial soft tissue FE mesh generation. The goal of the developed method is to efficiently generate a high-quality patient-specific hexahedral FE mesh with adjustable mesh density while preserving the accuracy in anatomical structure correspondence. Our FE mesh is generated by eFace template deformation followed by volumetric parametrization. First, the patient-specific anatomically detailed facial soft tissue model (including skin, mucosa, and muscles) is generated by deforming an eFace template model. The adaptation of the eFace template model is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. Then, high-quality hexahedral mesh is constructed by using volumetric parameterization. The user can control the resolution of hexahedron mesh to best reflect clinicians' need. Our approach was validated using 30 patient models and 4 visible human datasets. The generated patient-specific FE mesh showed high surface matching accuracy, element quality, and internal structure matching accuracy. They can be directly and effectively used for clinical simulation of facial soft tissue change.
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http://dx.doi.org/10.1007/s10237-017-0967-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845478PMC
April 2018

Application of A Novel Three-dimensional Printing Genioplasty Template System and Its Clinical Validation: A Control Study.

Sci Rep 2017 07 14;7(1):5431. Epub 2017 Jul 14.

Acting Chair and Professor, Department of Oral and Craniomaxillofacial Surgery, Shanghai 9th People's Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, China.

The purpose of this control study was to assess the accuracy and clinical validation of a novel genioplasty template system. Eighty-eight patients were enrolled and divided into 2 groups: experimental group (using genioplasty templates) and control group (without genioplasty templates). For the experimental group, the templates were designed based on computerized surgical plan and manufactured using three-dimensional printing technique. The template system included a cutting guide and a pair of repositioning guides. For the control group, traditional intraoperative measurements were used without genioplasty templates. The outcome evaluation was completed by comparing planned outcomes with postoperative outcomes. Linear and angular differences for the chin was measured and reported using root mean square deviation (RMSD) and the Bland-Altman method. All surgeries were successfully completed. There was no difficulty to use genioplasty templates. For the experimental group, the largest RMSDs were 1.1 mm in anteroposterior direction and 2.6° in pitch orientation. For the control group without templates, the largest RMSDs were 2.63 mm in superoinferior direction and 7.21° in pitch orientation. Our findings suggest that this genioplasty template system provides greater accuracy in repositioning the chin than traditional intraoperative measurements, and the computerized plan can be transferred accurately to the patient for genioplasty.
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http://dx.doi.org/10.1038/s41598-017-05417-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511285PMC
July 2017

A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery.

Med Phys 2017 Aug 10;44(8):4252-4261. Epub 2017 Jul 10.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.

Purpose: It is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction.

Methods: In Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method.

Results: The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods.

Conclusion: The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.
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http://dx.doi.org/10.1002/mp.12391DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5553697PMC
August 2017

Design, development and clinical validation of computer-aided surgical simulation system for streamlined orthognathic surgical planning.

Int J Comput Assist Radiol Surg 2017 Dec 21;12(12):2129-2143. Epub 2017 Apr 21.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA.

Purpose: There are many proven problems associated with traditional surgical planning methods for orthognathic surgery. To address these problems, we developed a computer-aided surgical simulation (CASS) system, the AnatomicAligner, to plan orthognathic surgery following our streamlined clinical protocol.

Methods: The system includes six modules: image segmentation and three-dimensional (3D) reconstruction, registration and reorientation of models to neutral head posture, 3D cephalometric analysis, virtual osteotomy, surgical simulation, and surgical splint generation. The accuracy of the system was validated in a stepwise fashion: first to evaluate the accuracy of AnatomicAligner using 30 sets of patient data, then to evaluate the fitting of splints generated by AnatomicAligner using 10 sets of patient data. The industrial gold standard system, Mimics, was used as the reference.

Result: When comparing the results of segmentation, virtual osteotomy and transformation achieved with AnatomicAligner to the ones achieved with Mimics, the absolute deviation between the two systems was clinically insignificant. The average surface deviation between the two models after 3D model reconstruction in AnatomicAligner and Mimics was 0.3 mm with a standard deviation (SD) of 0.03 mm. All the average surface deviations between the two models after virtual osteotomy and transformations were smaller than 0.01 mm with a SD of 0.01 mm. In addition, the fitting of splints generated by AnatomicAligner was at least as good as the ones generated by Mimics.

Conclusion: We successfully developed a CASS system, the AnatomicAligner, for planning orthognathic surgery following the streamlined planning protocol. The system has been proven accurate. AnatomicAligner will soon be available freely to the boarder clinical and research communities.
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http://dx.doi.org/10.1007/s11548-017-1585-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664166PMC
December 2017

Improved Rubin-Bodner model for the prediction of soft tissue deformations.

Med Eng Phys 2016 11 4;38(11):1369-1375. Epub 2016 Oct 4.

Department of Radiology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address:

In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue. Subsequently, a finite element model (FEM) computers the stress of each node in soft facial tissue mesh data resulted from bone displacement. Next, the Generalized Regression Neural Network (GRNN) method is implemented to obtain the relationship between the facial soft tissue deformation and the stress distribution corresponding to different CMF surgical types and to improve evaluation of elastic parameters included in the RB model. Therefore, the soft facial tissue deformation can be predicted by biomechanical properties and statistical model. Leave-one-out cross-validation is used on eleven patients. As a result, the average prediction error of our model (0.7035mm) is lower than those resulting from other approaches. It also demonstrates that the more accurate bio-mechanical information the model has, the better prediction performance it could achieve.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5087603PMC
http://dx.doi.org/10.1016/j.medengphy.2016.09.008DOI Listing
November 2016

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression.

Comput Biol Med 2016 08 7;75:1-9. Epub 2016 May 7.

Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA. Electronic address:

Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient's biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.
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http://dx.doi.org/10.1016/j.compbiomed.2016.04.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5279917PMC
August 2016

Automated segmentation of dental CBCT image with prior-guided sequential random forests.

Med Phys 2016 Jan;43(1):336

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7513 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea.

Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT.

Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images.

Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors' method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001).

Conclusions: The authors have developed and validated a novel fully automated method for CBCT segmentation.
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http://dx.doi.org/10.1118/1.4938267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4698124PMC
January 2016

Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features.

IEEE Trans Biomed Eng 2016 09 24;63(9):1820-1829. Epub 2015 Nov 24.

Objective: The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images.

Methods: We propose a segmentation-guided partially-joint regression forest (S-PRF) model to automatically digitize CMF landmarks. In this model, a regression voting strategy is first adopted to localize each landmark by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, CBCT image segmentation is utilized to remove uninformative voxels caused by morphological variations across patients. Third, a partially-joint model is further proposed to separately localize landmarks based on the coherence of landmark positions to improve the digitization reliability. In addition, we propose a fast vector quantization method to extract high-level multiscale statistical features to describe a voxel's appearance, which has low dimensionality, high efficiency, and is also invariant to the local inhomogeneity caused by artifacts.

Results: Mean digitization errors for 15 landmarks, in comparison to the ground truth, are all less than 2 mm.

Conclusion: Our model has addressed challenges of both interpatient morphological variations and imaging artifacts. Experiments on a CBCT dataset show that our approach achieves clinically acceptable accuracy for landmark digitalization.

Significance: Our automatic landmark digitization method can be used clinically to reduce the labor cost and also improve digitalization consistency.
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http://dx.doi.org/10.1109/TBME.2015.2503421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4879598PMC
September 2016

A Geometric Classification of Jaw Deformities.

J Oral Maxillofac Surg 2015 Dec;73(12 Suppl):S26-31

Professor and Chief, Division of Pediatric Plastic Surgery, Department of Pediatric Surgery, The University of Texas Health Science Center at Houston, Houston, TX.

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http://dx.doi.org/10.1016/j.joms.2015.05.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666701PMC
December 2015

Reconstruction-Based Digital Dental Occlusion of the Partially Edentulous Dentition.

IEEE J Biomed Health Inform 2017 01 12;21(1):201-210. Epub 2015 Nov 12.

Partially edentulous dentition presents a challenging problem for the surgical planning of digital dental occlusion in the field of craniomaxillofacial surgery because of the incorrect maxillomandibular distance caused by missing teeth. We propose an innovative approach called Dental Reconstruction with Symmetrical Teeth (DRST) to achieve accurate dental occlusion for the partially edentulous cases. In this DRST approach, the rigid transformation between two symmetrical teeth existing on the left and right dental model is estimated through probabilistic point registration by matching the two shapes. With the estimated transformation, the partially edentulous space can be virtually filled with the teeth in its symmetrical position. Dental alignment is performed by digital dental occlusion reestablishment algorithm with the reconstructed complete dental model. Satisfactory reconstruction and occlusion results are demonstrated with the synthetic and real partially edentulous models.
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http://dx.doi.org/10.1109/JBHI.2015.2500191DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5114178PMC
January 2017

An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation.

Ann Biomed Eng 2016 May 13;44(5):1656-71. Epub 2015 Oct 13.

Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin Street, Suite 1280, Houston, TX, 77030, USA.

Accurate surgical planning and prediction of craniomaxillofacial surgery outcome requires simulation of soft-tissue changes following osteotomy. This can only be accomplished on an anatomically-detailed facial soft tissue model. However, current anatomically-detailed facial soft tissue model generation is not appropriate for clinical applications due to the time intensive nature of manual segmentation and volumetric mesh generation. This paper presents a novel semi-automatic approach, named eFace-template method, for efficiently and accurately generating a patient-specific facial soft tissue model. Our novel approach is based on the volumetric deformation of an anatomically-detailed template to be fitted to the shape of each individual patient. The adaptation of the template is achieved by using a hybrid landmark-based morphing and dense surface fitting approach followed by a thin-plate spline interpolation. This methodology was validated using 4 visible human datasets (regarded as gold standards) and 30 patient models. The results indicated that our approach can accurately preserve the internal anatomical correspondence (i.e., muscles) for finite element modeling. Additionally, our hybrid approach was able to achieve an optimal balance among the patient shape fitting accuracy, anatomical correspondence and mesh quality. Furthermore, the statistical analysis showed that our hybrid approach was superior to two previously published methods: mesh-matching and landmark-based transformation. Ultimately, our eFace-template method can be directly and effectively used clinically to simulate the facial soft tissue changes in the clinical application.
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http://dx.doi.org/10.1007/s10439-015-1480-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833683PMC
May 2016

Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation.

Med Phys 2015 Oct;42(10):5809-16

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea.

Purpose: A significant number of patients suffer from craniomaxillofacial (CMF) deformity and require CMF surgery in the United States. The success of CMF surgery depends on not only the surgical techniques but also an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. Currently, the outcome of the surgery is often subjective and highly dependent on surgeon's experience. In this paper, the authors present an automatic method to estimate an anatomically correct reference shape of jaws for orthognathic surgery, a common type of CMF surgery.

Methods: To estimate a patient-specific jaw reference model, the authors use a data-driven method based on sparse shape composition. Given a dictionary of normal subjects, the authors first use the sparse representation to represent the midface of a patient by the midfaces of the normal subjects in the dictionary. Then, the derived sparse coefficients are used to reconstruct a patient-specific reference jaw shape.

Results: The authors have validated the proposed method on both synthetic and real patient data. Experimental results show that the authors' method can effectively reconstruct the normal shape of jaw for patients.

Conclusions: The authors have presented a novel method to automatically estimate a patient-specific reference model for the patient suffering from CMF deformity.
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http://dx.doi.org/10.1118/1.4929974DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4575319PMC
October 2015

A Modified Method of Proximal Segment Alignment After Sagittal Split Ramus Osteotomy for Patients With Mandibular Asymmetry.

J Oral Maxillofac Surg 2015 Dec 11;73(12):2399-2407. Epub 2015 May 11.

Professor and Acting Chair, Department of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; Professor, Shanghai Key Laboratory of Stomatology, Shanghai, China.

Purpose: The purpose of this study was to evaluate a modified method of aligning the proximal segment after bilateral sagittal split ramus osteotomy (BSSO) in the treatment of patients with facial asymmetry.

Patients And Methods: Eleven patients with mandibular excess and facial asymmetries were enrolled in this prospective study. The surgery was planned according to a computer-aided surgical simulation protocol. In addition, the proximal segment on the hypoplastic side was intentionally flared out after the distal segment was rotationally set back. If the gap between the proximal and distal segments was too wide, then bone grafts were used. The surgery was completed according to the computerized plan. The proximal segment on the hypoplastic side was fixed with bicortical lag screws, and the proximal segment on the hyperplastic side was fixed with a 4-hole titanium miniplate. Postoperative evaluation was performed 6 months after surgery. Statistical analyses were performed.

Results: All surgeries were completed uneventfully. Of the 11 patients, 4 also underwent genioplasty and 3 underwent bone grafting to fill in the gap and smooth the anterior step. The physicians and patients were satisfied with the surgical outcomes. Only 1 patient underwent a secondary revision using an onlay hydroxyapatite implant. Results of statistical analyses showed that the computerized surgical plan could be accurately transferred to the patients at the time of surgery and the surgical outcomes achieved with this modified method were better than with the routine method of aligning the proximal and distal segments in maximal contact.

Conclusion: The present modified method of aligning the proximal segment for BSSO can effectively correct mandibular asymmetry and obviate a secondary revision surgery.
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http://dx.doi.org/10.1016/j.joms.2015.05.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4673673PMC
December 2015

Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation.

Med Image Comput Comput Assist Interv 2014 ;17(Pt 2):73-80

The success of craniomaxillofacial (CMF) surgery depends not only on the surgical techniques, but also upon an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. In this paper, we present a method to automatically estimate an anatomically correct reference shape of jaws for the patient requiring orthognathic surgery, a common type of CMF surgery. We employ the sparse representation technique to represent the normal regions of the patient with respect to the normal subjects. The estimated representation is then used to reconstruct a patient-specific reference model with "restored" normal anatomy of the jaws. We validate our method on both synthetic subjects and patients. Experimental results show that our method can effectively reconstruct the normal shape of jaw for patients. Also, a new quantitative measurement is introduced to quantify the CMF deformity and validate the method in a quantitative approach, which is rarely used before.
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http://dx.doi.org/10.1007/978-3-319-10470-6_10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197838PMC
January 2015

Microscopic versus open approach to craniosynostosis: a long-term outcomes comparison.

J Craniofac Surg 2014 Jul;25(4):1245-8

From the *Division of Pediatric Plastic Surgery, Department of Pediatric Surgery, The University of Texas Health Science Center at Houston, TX; †Memorial Hermann Hospital, Houston, TX; ‡Medical School, The University of Texas Health Science Center at Houston, TX; §Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital, Houston, TX; ∥Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, NY; and ¶Department of Orthodontics, The University of Texas Health Science Center at Houston, TX.

The purpose of this retrospective study was to evaluate the long-term outcomes of using the microscopic minimally invasive approach for the treatment of nonsyndromic craniosynostosis. During the last 10 years, 180 consecutive patients with nonsyndromic craniosynostosis were treated: 67 patients were treated with microscopic minimally invasive approach, and 113 were treated with the open approach. In the microscopic group, there was 1 intraoperative complication (1.5%). There were 10 postoperative complications (14.9%), of which 9 required major reoperations and 1 required a minor procedure. The major complications occurred in 7 unicoronal patients (58.3%) and 2 metopic patients (25.0%). In the open-approach group, there were 8 complications (7.1%), 2 patients required major reoperations and 6 required minor procedures. Chi-squared test showed that there was no statistically significant difference in the overall complication rate between the microscopic and open approaches. However, in the unicoronal patients, the complication rate was significantly higher in the microscopic group (P < 0.001). In conclusion, the microscopic approach is our treatment of choice in nonsyndromic patients with sagittal and lambdoidal craniosynostosis. We no longer use the microscopic approach in patients with unicoronal or metopic craniosynostosis because of the high complication rate.
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http://dx.doi.org/10.1097/SCS.0000000000000925DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4638328PMC
July 2014