Development of a Dual-Energy Computed Tomography-Based Segmentation Method for Collateral Ligaments: A Porcine Knee Model

Abstract

The clinical value of dual-energy computed tomography (DECT) has gradually been recognized in many applications. In particular, DECT offers a new imaging method to improve the visualization of ligaments and tendons. However, limitations such as two-dimensional display and manual window adjustment hamper the evaluation of knee ligament injuries. In this study, we proposed a method to segment collateral ligaments from DECT images automatically. Based on various segmentation techniques, collateral ligaments can be visualized using the three-dimensional (3D) volume-rendering technique. To validate our methodology, we used a porcine knee model and focused on the detection of the medial collateral ligament (MCL) and lateral collateral ligament (LCL). Twenty porcine hind legs were scanned using DECT after specimens underwent surgery to cut either the MCL or the LCL. Using the proposed method, either a complete or a partial LCL rupture could be detected clearly, and a complete MCL rupture could be shown clearly. However, some cases might present some difficulty in identifying a partial MCL rupture since the MCL is a thin ligament. The proposed method can be used to automatically segment the main ligaments of the knee. In addition, the 3D volume rendering image makes DECT a valuable tool for the diagnosis of knee ligament injuries.