Customized scaffolds for large bone defects using 3D-printed modular blocks from 2D-medical images

Abstract

Additive manufacturing (AM) has revolutionized the design and manufacturing of patient-specific, three-dimensional (3D), complex porous structures known as scaffolds for tissue engineering applications. The use of advanced image acquisition techniques, image processing, and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds. However, these sophisticated techniques can be time-consuming, labor-intensive, and expensive. Moreover, the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients. In this study, a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient. These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional (2D) medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging, modeling, and 3D printing during surgery. The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation. This algorithm can generate both patient-specific and average bone models. Additionally, a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds, allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images. The algorithms are implemented, and the designed scaffold blocks are 3D printed using an extrusion-based AM process. Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

Graphic abstract