## 1 Introduction  
Ligaments play a crucial role for the mobility and stability of the human ankle complex, which includes the tibotalar joint above, between tibia-fibula-talus, and the subtolar joint below, between talus-calcaneus [1, 2]. Ankle sprains represent one of the most common musculoskeletal injuries (about 25%) [3, 4] and can imply partial or complete tear of the ligaments. Severe ankle sprains frequently result in chronic ankle instability [5, 6]. About 10-25% of these patients also present subtolar joint instability [7, 8]. When conservative techniques prove to be ineffective, surgery is indicated to restore overall joint functions [5, 6, 9] by ligament repair and reconstruction procedures. The knowledge of ankle complex anatomy, and in particular of the geometrical arrangement of the ligament fibres, is fundamental for a correct diagnosis and for successful treatments. In this context, the existing computational models represent useful tools for a better comprehension of the mechanical behaviour of this anatomical complex [10] and can offer a valuable clinical support [11], particularly when tailored to the specific case of interest. From medical images of the patient's ankle complex (via CT and MRI), information about origin and insertion areas (hereinafter all together referred to also as attachments) and dimension of the ligaments can be obtained. Different types of models have been developed: those on passive kinematics [12; 13; 14] are meant to replicate joint motion in unloaded conditions; dynamic [11; 14; 15; 16; 17] and finite element [10; 18; 19] models may estimate the mechanical behaviour of the joints under external loads, thus approximating realistic conditions. All these models represent the ankle complex with its bones and ligaments, but with a certain level of approximation when compared to the complexity of its real anatomical structures. Unfortunately, with the exception of a few studies [12], the current literature still lacks models that include accurate ligament mapping on a subject-specific basis. A previous attempt from the present authors proposed a 3D dynamic model of the human ankle joint complex with a careful ligament mapping validated against experiment evidence [12] and it was recently extended to a larger cohort of specimens [17]. It consists of the three rigid bone segments, i.e., the tibia-fibula, talus, and calcaneus, segmented from CT images, and nine ligaments approximated from the observation of anatomical atlases and MRIs, by visually selecting the corresponding points on the surface of the bones. The model predictions compared well with related experimental observations [20], but for a thorough customization of the model, more accurate ligament characterisation is necessary [17]. Tibio-talar, tibio-calcaneal, and fibula-calcaneal ligaments geometry and configuration have been studied extensively in the past [1; 21; 22]. However, the subtalar ligaments were much less investigated, likely because of their difficult accessibility [23; 24], particularly for those in the sinus tarsi. The most suitable medical imaging technology for the identification of soft tissues, i.e., the ligaments, is definitely MRI [25; 26; 27]. Some authors have compared \(1.5\) T versus \(3.0\) T MRI for the visualisation of cartilage, tendons, and ligaments of different anatomical joints, but the results were controversial. Among these studies, some [28; 29; 30; 31] did not find considerable improvements from \(1.5\) T to \(3.0\) T. On the other hand, other investigations [32; 33; 34] demonstrated higher image quality and better diagnostic performance of the \(3.0\) T MRI. From a technical point of view, the \(3.0\) T MRI has higher signal strength, but introduces artefacts due to field inhomogeneities [35]. In addition, the 3D Cube sequences offered by both MRI systems provide the opportunity to observe less accessible anatomical structures, such as subtalar ligaments, from any direction.