## 1 Introduction  
Skeletal muscle is one of the largest tissues of the body, accounting for approximately 45-50% of body mass, and besides its main biomechanical role in locomotion, it is also involved in physiological and pathological metabolic processes, such as oxygen (O\({}_{2}\)) consumption, energy metabolism, and substrate turnover and storage, and it is the major protein reserve of the human body [1, 2]. Medical imaging techniques, such as Computed Tomography (CT) [3], Ultrasound (US) [4], Dual-Energy X-ray Absorptiometry (DXA) [5], and Magnetic Resonance Imaging (MRI) [6], allow to non-invasively evaluate skeletal muscle morphological and functional properties related to a broad spectrum of conditions. MRI, in particular, has been gaining increasing importance in the diagnosis and follow-up of skeletal muscle disorders [7] due to its reproducibility, variety of contrasts, and the absence of ionizing radiation exposure [1]. In particular, MRI has been used in different applications to evaluate muscle changes due to strenuous physical exercise [8], aging [9], neuromuscular disorders and dystrophies [10; 11], and atrophy [12], and can be also used to assess the outcome of motor rehabilitation after an adverse event [13]. T1- and T2-weighted conventional MRI sequences usually offer an insight into anatomy and gross muscular structure, provide a reliable measure of volume [14], and can be qualitatively linked to inflammation and fat infiltration [15], but they are mostly based on the training of the observer and their scales are semi-quantitative. More advanced acquisitions, such as Dixon, quantitative T2 measurements, diffusion MRI (dMRI), and diffusion tensor imaging (DTI), provide quantitative variables and more refined characterization of physiological or degenerative processes occurring in the muscular tissue [16]. In particular, Dixon sequences can quantify fat infiltration in muscles, whereas quantitative T2 values have been related to fat infiltration, edema, inflammation, and changes in fibers structure. DTI-derived parameters are sensitive to muscular modifications occurring after ischemia, exercise, denervation, trauma, and inflammation [17], whereas DTI-based fiber tracking can help in investigating many fundamental muscle architectural properties, including pennation angle, fiber tract length, and curvature [18]. Furthermore, perfusion and pseudo-diffusion parameters calculated by the Intravoxel Incoherent Motion approach are correlated with muscle perfusion during exercise [19]. Nowadays, both clinical evaluations and research studies often employ a multimodal acquisition approach and multiple time points. Longitudinal studies are crucial to assess even subtle changes occurring to muscle tissue in the time and require a robust protocol for image alignment and registration. Most of the specific literature regarding image registration is focused on other areas/organs such as the brain [20; 21] and the cardiac parenchyma [22; 23] and includes workflows and metrics [24]. However, detailed protocols and pipelines, including even more technical aspects, suited for skeletal muscle images are still missing. Indeed, registration of skeletal muscle multimodal MR images remains a challenging task since muscle tissue cannot be considered a rigid body and its shape and volume can vary depending on both patient positioning and intrinsic anatomical variations occurring in the subject between two subsequent acquisition points. As a consequence, a subtle difference in position between MRI acquisitions (e.g., the angular position of the leg, the relative position of the coil on the thigh, the stress of the coil on the thigh) can lead to high intraindividual variability, thereby making registration difficult and often requiring the calculation of a deformation field. The same issue can become even more relevant in inter-subjects' analysis given the high variability in muscle shape and limb fat among subjects.