## 4 Discussion
Previous papers have shown that suppressing the elements of no interest increases the accuracy and robustness of image registration in other anatomical regions such as the brain [52] and the lung [53]. Performance metrics calculated on the selected registration scheme are consistent with those reported in previous literature. [PERSON] et al. [49], in their ex-vivo experiment, investigating the effect of heating on bovine muscle, reported an MSD, calculated between couples of landmark points on sixconsecutive slices, ranging between 0.34 and 1.1 mm but with a higher image resolution. More recently, [PERSON] et al. [29] reported, using clinical MRI, average DSI values ranging from 0.72 to 0.94. It is worth noticing that the muscle groups considered in the previous papers are different from those analyzed in this work. While significant differences were found in the considered registration schemes, the paper is based on a limited number of subjects and not entirely representative of a usually studied population; this can mainly affect the variability of the results. Therefore, a wider group of patients, including also healthy subjects, can strengthen the significance of our results, and validation on different muscle groups could provide information on the robustness of the method even in other experimental settings. The study is accompanied by an open-source software that is virtually extendable to the image registration of other body parts since the protocol could be easily adapted accordingly and the implementation of more features (e.g., non-gradient descent-based optimizers, finer evaluation metrics, or a regularization term) may be added to account for eventual peculiar characteristics of the specific anatomical district. In conclusion, the present study is a first attempt to propose and assess a reliable pipeline for the automated intra-session and longitudinal muscle MR images registration, in a multimodal study. Using a robust image registration protocol is crucial to estimate accurate deformation fields to be applied to multimodal images or different contours for extracting reliable parameters from quantitative MRI-derived maps reducing the bias due to the manual ROIs delineation, especially in longitudinal studies. The protocol can be a useful guide for researchers that approach for the first time the issues related to the muscle image registration. Conceptualization, L.F., A.M., E.S., and G.R. ; methodology, L.F., A.M., E.S., and D.P. ; software, L.F.; investigation, D.P., E.B., S.S., and F.A. ; data curation, D.P., E.B., and F.A. ; writing--original draft preparation, L.F. and A.M.; writing--review and editing, L.F., A.M., E.S., D.P., E.B., S.S., and G.R. ; supervision, A.M. and G.R. ; funding acquisition, F.A. and G.R. All authors have read and agreed to the published version of the manuscript. The work was partially funded by the Italian Ministry of Health (Ricerca Corrente \"Valutazione funzionale e RM degli effetti sul muscolo di programmi di training riabilitativo robot-assitito e tradizionale in soggetti con esiti di lesioni cerebrali acquisite: studio pilota\") and by Fondazione Cariplo and Regione Lombardia, Grant/Award Number: Progetto Empatia@Lecco, ref 2016-1428. The authors declare no conflict of interest.