Image Processing-based Method for Automatic Design of Patient-Specific Cranial Implant for Additive Manufacturing

Abstract

Decompressive craniectomy (DC) is a surgical procedure where a portion of the skull (flap) is removed to relieve the built-up pressure from the patient’s brain due to swelling of the brain tissue after a traumatic injury to the head. Subsequently, another surgical procedure called cranioplasty is carried out to fix an implant or bone flap in patients who have undergone DC. In this paper, an automatic design methodology for additive manufacturing of a PSCI (patient-specific cranial implant) has been proposed. The input is the DICOM digital data from a CT scan and the output is the STL file geometry of the cranial implant. The proposed method has been tested and validated using real de-identified DICOM data, and the resultant implant was 3D printed and fit to the skull of a cadaver. The key contribution made in this paper is the complete automation of the design of a PSCI based on the skull’s unique geometry using a combination of image-processing and computational geometry techniques. Another important characteristic of the proposed method is that medical professionals need not have any technical expertise in additive manufacturing or part design for generating a PSCI.