Skeletal muscle architecture, the macrostructural arrangement of muscle fibres in the muscle belly, is the primary determinant of a muscle’s capacity to generate force and change length (Lieber & Fridén, 2000). While the architecture of any particular muscle is broadly similar across individuals, muscle architecture can adapt to exercise (Alonso-Fernandez et al., 2018; Blazevich et al., 2003; Roig et al., 2008), ageing (Narici et al., 2003; Papenkort et al., 2021; Shur et al., 2021; Siebert et al., 2017) and disease (Bodine et al., 1982; Fahn-Lai et al., 2020; Kruse et al., 2018; J. E. Park et al., 2019). Quantitative measurement of muscle- and subject-specific architecture is therefore useful for the study of muscle adaptation and muscle dysfunction.
The human rotator cuff, comprising the supraspinatus, subscapularis, infraspinatus, and teres minor muscles, plays a significant role in movement of the upper limb and provides dynamic stability to the glenohumeral joint. Rotator cuff injuries are common – they constitute up to half of all significant shoulder injuries seen in some clinical contexts (Gazielly et al., 1994; Gerber et al., 2000) – and can be difficult to treat. Surgical treatment of rotator cuff injuries might be improved with preoperative planning tools built around biomechanical models of the shoulder. Computational models can be used to predict the biomechanical consequences of rotator cuff tears on muscle function and joint forces (Khandare et al., 2022; Vidt et al., 2018) or guide the design of implants in shoulder arthroplasty procedures (Büchler & Farron, 2004). However, due to the difficulty in obtaining subject-specific measurements of rotator cuff muscle architecture, most musculoskeletal shoulder models are generic or scaled-generic models (Prinold et al., 2013), which, despite their utility, often fall short of capturing the anatomical variations unique to each individual. Computational models and surgical planning tools might be more useful if they could incorporate subject-specific measurements of rotator cuff muscle architecture.
Diffusion-weighted imaging (DWI) enables accurate measurement of human rotator cuff muscle architecture in three dimensions, using fibre tracking algorithms which propagate fibre tracts along the principal eigenvectors of diffusion tensors throughout a muscle (Zhang et al., 2023, 2024). This approach not only provides a visual representation of muscle fibres orientations, but also allows for the quantification of muscle architecture such as the fascicle length and pennation angle – parameters that are crucial in understanding muscle function but cannot be measured with commonly used anatomical MRI scans. However, DWI-based reconstructions of muscle architecture are sensitive to noise and image artifacts inherent in DWI data, which can distort the path of fibre tracts and create regions in the muscle that contain few fibres. New methods are needed to improve muscle architecture reconstructions from MRI data. Ideally such methods would use standard scanning protocols and automated image processing procedures so that the methods could be routinely implemented in clinical practice.
Some of the current limitations of DWI-based muscle architecture reconstructions can be addressed with a population-based approach that characterises inter-individual variability. Statistical shape modelling (SSM) is now commonly used for this purpose. After computing a mean shape across the population and transforming each individual’s shape to the mean, principal component analysis can be used to identify the main modes of inter-individual shape variation (Hotelling, 1933; Pearson, 1901). SSMs have been used predominantly to study human skeletal anatomy (e.g., humerus (Vlachopoulos et al., 2018), scapula (Salhi et al., 2020)). However, there have been relatively few attempts to apply SSMs to skeletal muscles (e.g., facial muscles (Tran et al., 2023), levator ani (Su-Lin Lee et al., 2009), soleus (Bin Ghouth et al., 2022) and hamstrings (Sutherland et al., 2023)). To the best of our knowledge, no studies have applied SSM or population-based methods to shoulder muscles. Moreover, existing population-based methods usually only model the shape (outer surface) of the muscle, but do not model internal fibre orientations. One recent study included model fibre orientations in a population-averaged muscle modelling framework (Bolsterlee, 2022). However, that study only used surface features to find corresponding points between muscles from different individuals. SSMs might be able to more accurately represent the internal architecture of muscles if, in addition to using information about the shape of the muscle surface, they used information about muscle fibre orientations. This could be achieved by registering multiple channels of information – specifically, by combining muscle surface data derived from anatomical MRI with fibre orientation data derived from DWI. Registration of fibre orientations and development of DWI-based population-averaged atlases have been a focus in neuroimaging (Forsberg et al., 2011; Roura, 2015; Uus et al., 2020, 2021), but to our knowledge those methods have not yet been used in muscle imaging.
Population-averaged atlases built using multi-channel registration introduce novel possibilities for muscle architecture analysis. Firstly, atlases may enhance the representation of individual muscle architecture by aggregating information across the population. Aggregation can potentially reduce noise and image artifacts typically present in individual scans and enhance the robustness and reliability of muscle architecture reconstructions. Secondly, since previous studies on rotator cuff muscle architecture have reported limited interindividual variability (Zhang et al., 2024), there is potential that atlases can be used to predict individual muscle architecture from muscle shape alone, simplifying the generation of high-quality muscle architecture reconstructions for musculoskeletal modelling and assessments.
The goals of this study were, therefore, to: 1) develop population-averaged atlases of human rotator cuff muscles using multi-channel registration that combines anatomical data derived from MRI with fibre orientation data derived from DWI; and 2) use the atlases to predict muscle fibre orientations from anatomical MRI scans. We hypothesised that fibre orientations can be predicted accurately without DWI by registering anatomical MRI from a new subject to the anatomical MRI channels of the population-averaged atlas and then applying the resulting transformation to the channels of the atlas that encode muscle fibre orientations.