Feasibility of Electromagnetic Knee Imaging Verified on Ex-Vivo Pig Knees

Objective: The potential of electromagnetic knee imaging system verified on ex-vivo pig knee joint as an essential step before clinical trials is demonstrated. The system, which includes an antenna array of eight printed biconical elements operating at the band 0.7-2.2 GHz, is portable and cost-effective. Importantly, it can provide daily monitoring and onsite real-time examinations imaging tool for knee injuries. Methods: Six healthy hind legs from three dead adult pigs were removed at the hip and suspended in the developed system. For each pig, the right- and left-knee were scanning sequentially. Then ligament tear was emulated by injecting distilled water into the left knee joint of each pig for early (5 mL water) and mid-stage (10 mL water) injuries. The injured left knees were re-scanned. A modified multi-static fast delay, multiply and sum algorithm (MS-FDMAS) is used to reconstruct imaging of the knee. All knee's connective tissues, such as anterior and posterior cruciate ligaments (ACL, PCL), lateral and medial collateral ligaments (LCL, MCL), tendons, and meniscus, are extracted from a healthy hind leg along with collected synovial fluid. The extracted tissues and fluid were characterized and modelled as their data are not available in the literature, then imported to build an equivalent model for pig knee of 1 mm(3) resolution in a realistic simulation environment. Results: The obtained results proved potential of the proposed system to detect ligament/tendon tears. Conclusion: The proposed system has the potential to detect early knee injuries in a realistic environment. Significance: Contactless EM knee imaging system verified on ex-vivo pig joints confirms its potential to reconstruct knee images. This work lays the groundwork for clinical EM system for detecting and monitoring knee injuries. (EM)

Automated summary

The study demonstrated the potential of an electromagnetic knee imaging system verified on ex-vivo pig knee joints to detect knee injuries, laying the groundwork for building a clinical application system that can detect and monitor knee injuries.