Personalised statistical modelling of soft tissue structures in the ankle. (May 2022) - Peiffer, M.; Burssens, A.; Duquesne, K.; Last, M.; De Mits, S.; Victor, J.; Audenaert, EA.
Highlights: 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Manual segmentation of bony and soft-tissue models is time-consuming and subject to manual errors. A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics. Cartilage and main ankle ligaments were predicted with submillimeter accuracy. Abstract: Background and objective: Revealing the complexity behind subject-specific ankle joint mechanics requires simultaneous analysis of three-dimensional bony and soft-tissue structures. 3D musculoskeletal models have become pivotal in orthopedic treatment planning and biomechanical research. Since manual segmentation of these models is time-consuming and subject to manual errors, (semi-) automatic methods could improve the accuracy and enlarge the sample size of personalised 'in silico' biomechanical experiments and computer-assisted treatment planning. Therefore, our aim was to automatically predict ligament paths, cartilage topography and thickness in the ankle joint based on statistical shape modelling. Methods: A personalised cartilage and ligamentous prediction algorithm was established using geometric morphometrics, based on an 'in-house' generated lower limb skeletal model ( N  = 542), tibiotalar cartilage ( N  = 60) and ankle ligament segmentations ( N  = 10). For cartilage, a population-averaged thickness map was determined by use of partial least-squares regression. Ligaments were wrapped around bony contours based on iterative shortest path calculation. Accuracy of ligament path and cartilage thickness prediction was quantified using leave-one-out experiments. The novel personalised thickness prediction was compared with a constant cartilage thickness of 1.50 mm by use of a paired sample T-test. Results: Mean distance error of cartilage and ligament prediction was 0.12 mm (SD 0.04 mm) and 0.54 mm (SD 0.05 mm), respectively. No significant differences were found between the personalised thickness cartilage and segmented cartilage of the tibia ( p  = 0.73, CI [-1.60 .10 −17, 1.13 .10 −17 ]) and talus ( p  = 0.95, CI[ -1.35 .10 −17, 1.28 .10 −17 ]). For the constant thickness cartilage, a statistically significant difference was found in 89% and 92% of the tibial ( p  < 0.001, CI [0.51, 0.58]) and talar ( p  < 0.001, CI [0.33, 0.40]) cartilage area. Conclusions: In this study, we described a personalised prediction algorithm of cartilage and ligaments in the ankle joint. We were able to predict cartilage and main ankle ligaments with submillimeter accuracy. The proposed method has a high potential for generating large (virtual) sample sizes in biomechanical research and mitigates technological advances in computer-assisted orthopaedic surgery.