Nmam Institute of Technology

In pursuit of a sustainable alternative to conventional petroleum-derived polymers, this study aims to enhance the comprehensive performance of polylactic acid (PLA). Despite its inherent biocompatibility and biodegradability, the intrinsic brittleness of PLA significantly restricts its broader applicability, particularly in the medical domain. To overcome this limitation, we utilized the Hummers method to functionalize the surface of carbon nanotubes (CNTs) with carboxylic acid groups, followed by a green and highly efficient solution-mixing approach to immobilize the modified CNTs onto the surface of HKUST-1, a metal-organic framework (MOF). The resulting inorganic-organic nanocomposite, denoted as CNTₘ/HKUST-1, was uniformly integrated into a PLA matrix via a mechanical hot-pressing technique, yielding the CNTₘ/MOF-PLA composite. This composite exhibited remarkable enhancements in mechanical properties, including tensile strength, modulus, and fracture toughness, alongside improved thermal stability. Furthermore, it demonstrated exceptional antibacterial efficacy against *Staphylococcus aureus* and Escherichia coli, coupled with outstanding biocompatibility and minimal cytotoxicity. The green synthesis strategy employed herein positions the CNTₘ/MOF-PLA composite as a highly promising material for applications in tissue engineering and advanced biomedical devices.

Accurate pedicle screw placement is crucial for ensuring safety during spine surgery. This study aims to develop and validate a feature-based 3D to 2D registration framework to integrate pre-operative MR with intra-operative X-ray images for estimating the pedicle screw trajectory and achieving robust 3D visualization. The method aims to overcome intensity mismatches and tissue non-correspondence issues inherent to multimodal registration.

A feature-based registration framework was proposed, leveraging vertebral endplates as key features due to their consistency after pedicle screw insertion. The segmented MR images were forward-projected and registered to intra-operative X-ray images using a binary image matching similarity measure, with optimization achieved through CMA-ES. Validation was performed by mapping the registered trajectory position onto pre-operative MR images and further evaluating the accuracy through 3D to 3D registration of pre-operative MR and post-operative CT images.

The framework effectively estimated the pedicle screw trajectory by registering intra-operative images onto pre-operative MR, with errors consistently below 2.5 mm for entry point, endpoint, and directional trajectory. The 3D to 3D registration validated the accuracy, demonstrating precise alignment between trajectory positions across modalities.

The proposed feature-based registration framework provides fast and accurate 3D trajectory information. This method improves surgical precision and offers robust evaluation metrics, making it a reliable tool for spine navigation and clinical diagnosis during pedicle screw placement procedures.