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The remodeling behavior of bone is influenced by its mechanical environment. By mapping bone's mechanical properties in detail, orthopedic implants with respect to its mechanical properties could stimulate and harness remodeling to improve patient outcomes. In this study, multiaxial apparent modulus and strength of cadaveric proximal tibial bone are mapped and predicted from computed tomography (CT) derived apparent density. Group differences are identified from testing order, subchondral depth, condyle, and sub-meniscal bone with covariates; age and gender. Axial modulus is 50% greater than the transverse modulus. Medial axial modulus is 30% greater than the lateral side. On the lateral side, axial modulus decreases by 50% from proximal to 25 mm distal. On the medial side, axial modulus remains relatively constant. Differences are quantified for density and multiaxial modulus across all subchondral depths, and different power law relationships are provided for each location. Density explains 75% of variation when grouped by subchondral depth and condyle. Yield strength is well-predicted across all test directions, with density predicting 81% of axial strength variation and no differences over subchondral depth. Quantified mapping of bone multiaxial modulus based on condyle and subchondral depth is shown for the first time in a clinically viable protocol using conventional CT.

Aims Unicompartmental and total knee arthroplasty (UKA and TKA) are successful treatments for osteoarthritis, but the solid metal implants disrupt the natural distribution of stress and strain which can lead to bone loss over time. This generates problems if the implant needs to be revised. This study investigates whether titanium lattice UKA and TKA implants can maintain natural load transfer in the proximal tibia. Methods In a cadaveric model, UKA and TKA procedures were performed on eight fresh-frozen knee specimens, using conventional (solid) and titanium lattice tibial implants. Stress at the bone-implant interfaces were measured and compared to the native knee. Results Titanium lattice implants were able to restore the mechanical environment of the native tibia for both UKA and TKA designs. Maximum stress at the bone-implant interface ranged from 1.2 MPa to 3.3 MPa compared with 1.3 MPa to 2.7 MPa for the native tibia. The conventional solid UKA and TKA implants reduced the maximum stress in the bone by a factor of 10 and caused > 70% of bone surface area to be underloaded compared to the native tibia. Conclusion Titanium lattice implants maintained the natural mechanical loading in the proximal tibia after UKA and TKA, but conventional solid implants did not. This is an exciting first step towards implants that maintain bone health, but such implants also have to meet fatigue and micromotion criteria to be clinically viable. Cite this article: Bone Joint Res 2022;11(2):91–101.

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The increasing use of surgical robotics has provoked the necessity for new medical imaging methods. Many assistive surgical robotic systems influence the surgeon's movements based on a model of constraints and boundaries driven by anatomy. This study aims to demonstrate that Near-Infrared Fluorescence (NIRF) imaging could be applied in surgical applications to provide subsurface mapping of capillaries beneath soft tissue as a method for imaging active constraints. The manufacture of a system for imaging in the near-infrared wavelength range is presented, followed by a description of computational methods for stereo-post-processing and data acquisition and testing used to demonstrate that the proposed methods are viable. The results demonstrate that it is possible to use NIRF for the imaging of a capillary submersed up to 11 mm below a soft tissue phantom, over a range of angles from 0 • through 45 •. Phantom depth has been measured to an accuracy of ±3 mm and phantom angle to a constant accuracy of ±1.6 •. These findings suggest that NIRF could be used for the next generation of medical imaging in surgical robotics and provide a basis for future research into real-time depth perception in the mapping of active constraints.