{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:33:04Z","timestamp":1762522384753,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19H05730"],"award-info":[{"award-number":["19H05730"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Muscle functional MRI (mfMRI) is an imaging technique that assess muscles\u2019 activity, exploiting a shift in the T2-relaxation time between resting and active state on muscles. It is accompanied by the use of electromyography (EMG) to have a better understanding of the muscle electrophysiology; however, a technique merging MRI and EMG information has not been defined yet. In this paper, we present an anatomical and quantitative evaluation of a method our group recently introduced to quantify its validity in terms of muscle pattern estimation for four subjects during four isometric tasks. Muscle activation pattern are estimated using a resistive network to model the morphology in the MRI. An inverse problem is solved from sEMG data to assess muscle activation. The results have been validated with a comparison with physiological information and with the fitting on the electrodes space. On average, over 90% of the input sEMG information was able to be explained with the estimated muscle patterns. There is a match with anatomical information, even if a strong subjectivity is observed among subjects. With this paper we want to proof the method\u2019s validity showing its potential in diagnostic and rehabilitation fields.<\/jats:p>","DOI":"10.3390\/s20030724","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T09:37:09Z","timestamp":1580204229000},"page":"724","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards a Simplified Estimation of Muscle Activation Pattern from MRI and EMG Using Electrical Network and Graph Theory"],"prefix":"10.3390","volume":"20","author":[{"given":"Enrico","family":"Piovanelli","sequence":"first","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, Hongo, Tokyo 113-8656, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9258-8985","authenticated-orcid":false,"given":"Davide","family":"Piovesan","sequence":"additional","affiliation":[{"name":"Department of Biomedical, Industrial and Systems Engineering, Gannon University, Erie, PA 16541, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3102-5118","authenticated-orcid":false,"given":"Shouhei","family":"Shirafuji","sequence":"additional","affiliation":[{"name":"Research Into Artifacts Center for Engineering (RACE), School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan"},{"name":"Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-0031, Japan"}]},{"given":"Becky","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Precision Engineering, The University of Tokyo, Hongo, Tokyo 113-8656, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7495-9113","authenticated-orcid":false,"given":"Natsue","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan"},{"name":"Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-0031, Japan"},{"name":"Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Kawaguchi, Saitama 332-0012, Japan"}]},{"given":"Yousuke","family":"Ogata","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan"},{"name":"Integrative Brain Imaging Center, National Center of Neurology and Psychiatry (NCNP), Kodaira, Tokyo 187-0031, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4738-2275","authenticated-orcid":false,"given":"Jun","family":"Ota","sequence":"additional","affiliation":[{"name":"Research Into Artifacts Center for Engineering (RACE), School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"896","DOI":"10.2519\/jospt.2011.3586","article-title":"Muscle functional MRI as an imaging tool to evaluate muscle activity","volume":"41","author":"Cagnie","year":"2011","journal-title":"J. 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