Thickness accuracy of virtually designed patient-specific implants for large neurocranial defects

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Parietal bone thickness for predicting operative transfusion and blood loss in patients undergoing spring-mediated cranioplasty for nonsyndromic sagittal craniosynostosis

OBJECTIVE

Variables that can predict outcomes in patients with craniosynostosis, including bone thickness, are important for surgical decision-making, yet are incompletely understood. Recent studies have demonstrated relative risks and benefits of surgical techniques for correcting head shape in patients with nonsyndromic sagittal craniosynostosis. The purpose of this study was to characterize the relationships between parietal bone thickness and perioperative outcomes in patients who underwent spring-mediated cranioplasty (SMC) for nonsyndromic sagittal craniosynostosis.

METHODS

Patients who underwent craniectomy and SMC for nonsyndromic sagittal craniosynostosis at a quaternary pediatric hospital between 2011 and 2021 were included. Parietal bone thickness was determined on patient preoperative CT at 27 suture-related points: at the suture line and at 0.5 cm, 1.0 cm, 1.5 cm, and 2.0 cm from the suture at the anterior parietal, midparietal, and posterior parietal bones. Preoperative skull thickness was compared with intraoperative blood loss, need for intraoperative transfusion, and hospital length of stay (LOS).

RESULTS

Overall, 124 patients with a mean age at surgery ± SD of 3.59 ± 0.87 months and mean parietal bone thickness of 1.83 ± 0.38 mm were included in this study. Estimated blood loss (EBL) and EBL per kilogram were associated with parietal bone thickness 0.5 cm (ρ = 0.376, p < 0.001 and ρ = 0.331, p = 0.004; respectively) and 1.0 cm (ρ = 0.324, p = 0.007 and ρ = 0.245, p = 0.033; respectively) from the suture line. Patients with a thicker parietal bone 0.5 cm (OR 18.08, p = 0.007), 1.0 cm (OR 7.16, p = 0.031), and 1.5 cm (OR 7.24, p = 0.046) from the suture line were significantly more likely to have undergone transfusion when controlling for age, sex, and race. Additionally, parietal bone thickness was associated with hospital LOS (β 0.575, p = 0.019) when controlling for age, sex, and race. Patient age at the time of surgery was not independently associated with these perioperative outcomes.

CONCLUSIONS

Parietal bone thickness, but not age at the time of surgery, may predict perioperative outcomes including transfusion, EBL, and LOS. The need for transfusion and EBL were most significant for parietal bone thickness 0.5 cm to 1.5 cm from the suture line, within the anticipated area of suturectomy. For patients undergoing craniofacial surgery, parietal bone thickness may have important implications for anticipating the need for intraoperative transfusion and hospital LOS.