In recent years, the treatment of infected tibial bone defects had been a challenge for orthopaedic surgeon. Ilizarov technique had demonstrated its several advantages to repair tibial bone defects, which was recommended by most studies. However, it takes too much time and makes it difficult for patient to persist. Autogenous bone grafts or bone graft substitutes had demonstrated a recognized clinical efficacy, but the existing biomaterials could not meet the clinical requirements including bone induction, structural support, and controllable biodegradability. In order to offer the possibility of individualized treatment, the application of three-dimensional (3D) printing technology in the medical field has been expanding. A 1:1 3D reconstruction model can be used to control the accuracy of implantation in Masquelet's technique for tibial bone defect patients, which could improve the quality and size of induction membrane. However, there are still many disadvantages of its application. Infectious bone defects of the tibia are often frequently accompanied with defect or deficiency of skin, muscle, blood vessels, or some other soft tissues. Moreover, it is difficult to be applied in some hospitals because it requires requirement cooperation of orthopedic surgeons, imaging physicians, and device engineers. This paper reviews the research and application of 3D printing technology in Masquelet membrane induction in patients with infectious tibial bone defect, as well as its clinical advantages and challenges.

Background and Objectives: Digital heart atlases play important roles in computational cardiac simulation and medical image analysis. During the past decades, various heart anatomy models were developed, but they mostly focused on the ventricular part. Recently, a number of whole-heart atlases were developed but they rarely modelled the motion features. This study constructed a whole-heart atlas incorporating dynamic cardiac motion. Materials and Methods: The shape and motion features of the atlas were learnt from a training set of 57 dynamic computed tomographic angiography images including 20 cardiac phases. Inter-subject variations of the heart anatomy and motion were incorporated into the atlas using the statistical shape modelling approach. Clinically relevant physiological parameters (e.g., chamber volumes, ejection fraction, and percentage of systolic phase) were correlated with the shape and motion variations using the linear regression approach. The shape and motion pattern of the atlas can be adapted by adjusting the physiological parameters. Results: Quantitative experiments were conducted to measure the anatomical accuracy of the atlas for whole-heart shape reconstruction of different subjects, a mean Dice score of 0.89–0.93 and a mean surface distance of 1.02–1.91 mm were achieved for the four heart chambers, respectively. Conclusions: This atlas provides a novel computational tool with adjustable shape and motion parameters for cardiac simulation research.