Estimating Reference Shape Model for Personalized Surgical Reconstruction of Craniomaxillofacial Defects

Automated planning of mandible reconstruction with fibula free flap based on shape completion and morphometric descriptors

Vascularized fibula free flap (FFF) grafts are frequently used to reconstruct mandibular defects. However, the current planning methods for osteotomy, splicing, and fibula placement present challenges in achieving satisfactory facial aesthetics and restoring the original morphology of the mandible. In this study, we propose a novel two-step framework for automated preoperative planning in FFF mandibular reconstruction. The framework is based on mandibular shape completion and morphometric descriptors. Firstly, we utilize a 3D generative model to estimate the entire mandibular geometry by incorporating shape priors and accounting for partial defect mandibles. Accurately predicting the premorbid morphology of the mandible is crucial for determining the surgical plan. Secondly, we introduce new two-dimensional morphometric descriptors to assess the quantitative difference between the planning scheme and the full morphology of the mandible. We have designed intuitive and valid variables specifically designed to describe the planning scheme and constructed an objective function to measure the difference. By optimizing this function, we can achieve the best shape-matched 3D planning solution. Through a retrospective study involving 65 real tumor patients, our method has exhibited favorable results in both qualitative and quantitative analyses when compared to the planned results of experienced clinicians using existing methods. This demonstrates that our method can implement an automated preoperative planning technique, eliminating subjectivity and achieving user-independent results. Furthermore, we have presented the potential of our automated planning process in a clinical case, highlighting its applicability in clinical settings.

A bidirectional framework for fracture simulation and deformation-based restoration prediction in pelvic fracture surgical planning

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

Adaptable cascaded registration for personalized maxilla completion and cleft defect volume estimation

BACKGROUND

Cone-beam computed tomography (CBCT) images provide high-resolution insights into the underlying craniofacial anomaly in patients with cleft lip and palate (CLP), requiring non-negligible annotation costs to measure the cleft defect for the guidance of the clinical secondary alveolar bone graft procedures. Considering the cumbersome volumetric image acquisition, there is a lack of paired CLP CBCTs and normal CBCTs for learning-based anatomical structure restoration models. Nowadays, the registration-based method relieves the annotation burden, though one-shot registration and the regular mask are limited to handling fine-grained shape variations and harmony between restored bony tissues and the defected maxilla.

PURPOSE

This study aimed to design and evaluate a novel method for deformable partial registration of the CLP CBCTs and normal CBCTs, enabling personalized maxilla completion and cleft defect volume prediction from CLP CBCTs.

METHODS

We proposed an adaptable deep registration framework for personalized maxilla completion and cleft defect volume prediction from CLP CBCTs. The key ingredient was a cascaded partial registration to exploit the maxillary morphology prior and attribute transfer. Cascaded registration with coarse-to-fine registration fields handled morphological variations of cleft defects and fine-grained maxillary restoration. We designed an adaptable cleft defect mask and volumetric Boolean operators for reliable voxel filling of the defected maxilla. A total of 36 clinically obtained CLP CBCTs were used to train and validate the proposed model, among which 22 CLP CBCTs were used to generate a training dataset with 440 synthetic CBCTs by B-spline deformation-based data augmentation and the remaining for testing. The proposed model was evaluated on maxilla completion and cleft defect volume prediction from clinically obtained unilateral and bilateral CLP CBCTs.

RESULTS

Extensive experiments demonstrated the effectiveness of the adaptable cleft defect mask and the cascaded partial registration on maxilla completion and cleft defect volume prediction. The proposed method achieved state-of-the-art performances with the Dice similarity coefficient of 0.90 ± $\pm$ 0.02 on the restored maxilla and 0.84 ± $\pm$ 0.04 on the estimated cleft defect, respectively. The average Hausdorff distance between the estimated cleft defect and the manually annotated ground truth was 0.30 ± $\pm$ 0.08 mm. The relative volume error of the cleft defect was0.09 ± $0.09\pm$ 0.08. The proposed model allowed for the prediction of cleft defect maps that were in line with the ground truth in the challenging unilateral and bilateral CLP CBCTs.

CONCLUSIONS

The results suggest that the proposed adaptable deep registration model enables patient-specific maxilla completion and automatic annotation of cleft defects, relieving tedious voxel-wise annotation and image acquisition burdens.

Creating high-resolution 3D cranial implant geometry using deep learning techniques

Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane.

Virtual reconstruction of midfacial bone defect based on generative adversarial network

BACKGROUND

The study aims to evaluate the accuracy of the generative adversarial networks (GAN) for reconstructing bony midfacial defects.

METHODS

According to anatomy, the bony midface was divided into five subunit structural regions and artificial defects are manually created on the corresponding CT images. GAN is trained to reconstruct artificial defects to their previous normal shape and tested. The clinical defects are reconstructed by the trained GAN, where the midspan defects were used for qualitative evaluation and the unilateral defects were used for quantitative evaluation. The cosine similarity and the mean error are used to evaluate the accuracy of reconstruction. The Mann-Whitney U test is used to detect whether reconstruction errors were consistent in artificial and unilateral clinical defects.

RESULTS

This study included 518 normal CT data, with 415 in training set and 103 in testing set, and 17 real patient data, with 2 midspan defects and 15 unilateral defects. Reconstruction of midspan clinical defects assessed by experts is acceptable. The cosine similarity in the reconstruction of artificial defects and unilateral clinical defects is 0.97 ± 0.01 and 0.96 ± 0.01, P = 0.695. The mean error in the reconstruction of artificial defects and unilateral clinical defects is 0.59 ± 0.31 mm and 0.48 ± 0.08 mm, P = 0.09.

CONCLUSION

GAN-based virtual reconstruction technology has reached a high accuracy in testing set, and statistical tests suggest that it can achieve similar results in real patient data. This study has preliminarily solved the problem of bony midfacial defect without reference.

Effect of quality control circle on nursing in orthopaedic trauma surgery

OBJECTIVE

To explore the application effect of Quality Management Circle (QCC) in nursing of orthopaedic trauma surgery.

METHODS

The clinical data of 134 cases undergoing orthopaedic trauma surgery were assigned into 2 groups according to different nursing methods. Thereinto, 67 cases with traditional nursing were considered as the control group (CG), and the left with traditional nursing and QCC activities were assigned as the study group (SG). The pain (VAS) score and psychological fluctuation index were observed and compared at various time points after operation. The recorded indexes included anxiety (SAS) and depression (SDS) scores before and after intervention, limb joint activity, health knowledge awareness rate, satisfaction rate, quantitative score of quality of life and nosocomial infection rate.

RESULTS

After intervention, the VAS scores in the SG were lower than those in the CG 2 weeks after intervention (all P<0.05). The quantitative scores of SDS and SAS in the SG after intervention were lower than those in the CG (all P<0.05). After that, the range of motion of lower limb joints in the SG was higher than that in the CG (all P<0.05). The awareness rate of health knowledge in the SG was higher than that in the CG (all P<0.05). The satisfaction rate of the SG was higher than that of the CG (P<0.05). The score level of each index of quality of life in the SG was higher than that in the CG (all P<0.05). There was no marked difference in nosocomial infection rate (P>0.05).

CONCLUSION

The application of QCC on patients undergoing orthopaedic trauma surgery can not only reduce patients' pain, negative emotions, but also improve limb joint activity, health knowledge awareness rate, satisfaction rate and quality of life.

Glycosaminoglycan content of a mineralized collagen scaffold promotes mesenchymal stem cell secretion of factors to modulate angiogenesis and monocyte differentiation

Effective design of biomaterials to aid regenerative repair of craniomaxillofacial (CMF) bone defects requires approaches that modulate the complex interplay between exogenously added progenitor cells and cells in the wound microenvironment, such as osteoblasts, osteoclasts, endothelial cells, and immune cells. We are exploring the role of the glycosaminoglycan (GAG) content in a class of mineralized collagen scaffolds recently shown to promote osteogenesis and healing of craniofacial bone defects. We previously showed that incorporating chondroitin-6-sulfate or heparin improved mineral deposition by seeded human mesenchymal stem cells (hMSCs). Here, we examine the effect of varying scaffold GAG content on hMSC behavior, and their ability to modulate osteoclastogenesis, vasculogenesis, and the immune response. We report the role of hMSC-conditioned media produced in scaffolds containing chondroitin-6-sulfate (CS6), chondroitin-4-sulfate (CS4), or heparin (Heparin) GAGs on endothelial tube formation and monocyte differentiation. Notably, endogenous production by hMSCs within Heparin scaffolds most significantly inhibits osteoclastogenesis via secreted osteoprotegerin (OPG), while the secretome generated by CS6 scaffolds reduced pro-inflammatory immune response and increased endothelial tube formation. All conditioned media down-regulated many pro- and anti-inflammatory cytokines, such as IL6, IL-1β, and CCL18 and CCL17 respectively. Together, these findings demonstrate that modifying mineralized collagen scaffold GAG content can both directly (hMSC activity) and indirectly (production of secreted factors) influence overall osteogenic potential and mineral biosynthesis as well as angiogenic potential and monocyte differentiation towards osteoclastic and macrophage lineages. Scaffold GAG content is therefore a powerful stimulus to modulate reciprocal signaling between multiple cell populations within the bone healing microenvironment.

Stochastic PCA-Based Bone Models from Inverse Transform Sampling: Proof of Concept for Mandibles and Proximal Femurs

Principal components analysis is a powerful technique which can be used to reduce data dimensionality. With reference to three-dimensional bone shape models, it can be used to generate an unlimited number of models, defined by thousands of nodes, from a limited (less than twenty) number of scalars. The full procedure has been here described in detail and tested. Two databases were used as input data: the first database comprised 40 mandibles, while the second one comprised 98 proximal femurs. The “average shape” and principal components that were required to cover at least 90% of the whole variance were identified for both bones, as well as the statistical distributions of the respective principal components weights. Fifteen principal components sufficed to describe the mandibular shape, while nine components sufficed to describe the proximal femur morphology. A routine has been set up to generate any number of mandible or proximal femur geometries, according to the actual statistical shape distributions. The set-up procedure can be generalized to any bone shape given a sufficiently large database of the respective 3D shapes.