AI-assisted six-dimensional CT imaging for joint instability assessment

Arthritis can lead to long-term functional disability and morbidity, yet assessing joint instability (i.e., the precursor of osteoarthritis) remains challenging with current standard static imaging methods. To tackle this challenge, we propose artificial intelligence (AI)-assisted six-dimensional CT (6DCT) imaging which integrates 3D spatial data, temporal information, spectral information, and joint kinematics, acquired from photon-counting-detector (PCD) CT. A physics-informed motion correction network (ATOM) was developed to reduce motion artifacts and improve quantitative accuracy in dynamic wrist CT images. Another physics-informed prior-assisted Bayesian network (PMBD) was developed to perform multi-material decomposition and facilitate soft tissue differentiation. Kinematics metrics were derived from motion-corrected bone images to characterize the patterns of joint motion. Patient, cadaver, and phantom scans were used in validation. ATOM reduced motion artifacts (p<0.05; before / after correction): e.g., low-intensity region scores 0.72±0.03 / 0.77±0.03; Structural similarity index 0.94±0.05 / 0.99±0.01. PMBD enhanced material quantification accuracy compared to conventional iterative algorithm: mean-absolute-percent-error PMBD [0.6%, 3.4%], conventional [0.8%, 5%]. Also, PMBD further improved the differentiation between collagen and water, successfully highlighting pathological features in cadaver ligaments. These techniques were further integrated for the downstream kinematic analyses. The proposed 6DCT has the potential to provide accurate visualization and quantitative assessment of dynamic joint pathology.