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Stem cell-based therapy holds promise for cartilage regeneration in treating knee osteoarthritis (KOA). Injectable hydrogels have been developed to mimic the extracellular matrix (ECM) and facilitate stem cell growth, proliferation, and differentiation. However, these hydrogels face limitations such as poor mechanical strength, inadequate biocompatibility, and suboptimal biodegradability, collectively hindering their effectiveness in cartilage regeneration. This study introduces an injectable, biodegradable, and self-healing hydrogel composed of chitosan-PEG and PEG-dialdehyde for stem cell delivery. This hydrogel can form in situ by blending two polymer solutions through injection at physiological temperature, encapsulating human adipose-derived stem cells (hADSCs) during the gelation process. Featuring a 3D porous structure with large pore size, optimal mechanical properties, biodegradability, easy injectability, and rapid self-healing capability, the hydrogel supports the growth, proliferation, and differentiation of hADSCs. Notably, encapsulated hADSCs form 3D spheroids during proliferation, with their sizes increasing over time alongside hydrogel degradation while maintaining high viability for at least 10 days. Additionally, hADSCs encapsulated in this hydrogel exhibit upregulated expression of chondrogenic differentiation genes and proteins compared to those cultured on 2D surfaces. These characteristics make the chitosan-PEG/PEG-dialdehyde hydrogel-stem cell construct suitable for direct implantation through minimally invasive injection, enhancing stem cell-based therapy for KOA and other cell-based treatments.

Registration of pre-operative CT datasets to intra-operative 3D freehand ultrasound has been of high interest for computer assisted orthopedic surgery. Feature-based registration relies on an accurate detection of the bone surface in the B-mode ultrasound images. In this work we present a fully automatic bone detection approach for US. The pre-operative CT is utilized to create a patient-specific bone model for our joint detection-registration framework. The model provides a geometric constraint for accurate and robust detection. Simultaneously to the detection, our method yields a close estimate of the rigid transformation from US to CT, which can be used as an initialization for further refinement through sophisticated intensity-/feature-based registration methods. We evaluated our approach on datasets of the human femur acquired in a cadaver study and demonstrate a mean bone detection error of below 0.4 mm.