@Article{RSM:Mil2014,
  author =       "M. Milanic and V. Jazbinsek and R.S. Macleod and D.H.
                 Brooks and R. Hren",
  title =        "Assessment of regularization techniques for
                 electrocardiographic imaging.",
  journal =      j-JE,
  year =         "2014",
  month =        "Jan-Feb",
  volume =       "47",
  number =       "1",
  pages =        "20--28",
  robnote =      "A widely used approach to solving the inverse problem in
                 electrocardiography involves computing potentials on the
                 epicardium from measured electrocardiograms (ECGs) on the
                 torso surface. The main challenge of solving this
                 electrocardiographic imaging (ECGI) problem lies in its
                 intrinsic ill-posedness. While many regularization
                 techniques have been developed to control wild
                 oscillations of the solution, the choice of proper
                 regularization methods for obtaining clinically acceptable
                 solutions is still a subject of ongoing research. However
                 there has been little rigorous comparison across methods
                 proposed by different groups. This study systematically
                 compared various regularization techniques for solving the
                 ECGI problem under a unified simulation framework,
                 consisting of both 1) progressively more complex idealized
                 source models (from single dipole to triplet of dipoles),
                 and 2) an electrolytic human torso tank containing a live
                 canine heart, with the cardiac source being modeled by
                 potentials measured on a cylindrical cage placed around
                 the heart. We tested 13 different regularization
                 techniques to solve the inverse problem of recovering
                 epicardial potentials, and found that non-quadratic
                 methods (total variation algorithms) and first-order and
                 second-order Tikhonov regularizations outperformed other
                 methodologies and resulted in similar average
                 reconstruction errors.",
  bibdate =      "Sun Sep 21 21:53:35 2014",
  pmcid =        "PMC4154607",
}

{"_id":"vZL3BwkgSCxhdWxf9","bibbaseid":"milanic-jazbinsek-macleod-brooks-hren-assessmentofregularizationtechniquesforelectrocardiographicimaging-2014","downloads":0,"creationDate":"2016-07-01T21:38:37.627Z","title":"Assessment of regularization techniques for electrocardiographic imaging.","author_short":["Milanic, M.","Jazbinsek, V.","Macleod, R.","Brooks, D.","Hren, R."],"year":2014,"bibtype":"article","biburl":"http://www.sci.utah.edu/~macleod/Bibtex/biglit.bib","bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["M."],"propositions":[],"lastnames":["Milanic"],"suffixes":[]},{"firstnames":["V."],"propositions":[],"lastnames":["Jazbinsek"],"suffixes":[]},{"firstnames":["R.S."],"propositions":[],"lastnames":["Macleod"],"suffixes":[]},{"firstnames":["D.H."],"propositions":[],"lastnames":["Brooks"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Hren"],"suffixes":[]}],"title":"Assessment of regularization techniques for electrocardiographic imaging.","journal":"j-JE","year":"2014","month":"Jan-Feb","volume":"47","number":"1","pages":"20–28","robnote":"A widely used approach to solving the inverse problem in electrocardiography involves computing potentials on the epicardium from measured electrocardiograms (ECGs) on the torso surface. The main challenge of solving this electrocardiographic imaging (ECGI) problem lies in its intrinsic ill-posedness. While many regularization techniques have been developed to control wild oscillations of the solution, the choice of proper regularization methods for obtaining clinically acceptable solutions is still a subject of ongoing research. However there has been little rigorous comparison across methods proposed by different groups. This study systematically compared various regularization techniques for solving the ECGI problem under a unified simulation framework, consisting of both 1) progressively more complex idealized source models (from single dipole to triplet of dipoles), and 2) an electrolytic human torso tank containing a live canine heart, with the cardiac source being modeled by potentials measured on a cylindrical cage placed around the heart. We tested 13 different regularization techniques to solve the inverse problem of recovering epicardial potentials, and found that non-quadratic methods (total variation algorithms) and first-order and second-order Tikhonov regularizations outperformed other methodologies and resulted in similar average reconstruction errors.","bibdate":"Sun Sep 21 21:53:35 2014","pmcid":"PMC4154607","bibtex":"@Article{RSM:Mil2014,\n author = \"M. Milanic and V. Jazbinsek and R.S. Macleod and D.H.\n Brooks and R. Hren\",\n title = \"Assessment of regularization techniques for\n electrocardiographic imaging.\",\n journal = j-JE,\n year = \"2014\",\n month = \"Jan-Feb\",\n volume = \"47\",\n number = \"1\",\n pages = \"20--28\",\n robnote = \"A widely used approach to solving the inverse problem in\n electrocardiography involves computing potentials on the\n epicardium from measured electrocardiograms (ECGs) on the\n torso surface. The main challenge of solving this\n electrocardiographic imaging (ECGI) problem lies in its\n intrinsic ill-posedness. While many regularization\n techniques have been developed to control wild\n oscillations of the solution, the choice of proper\n regularization methods for obtaining clinically acceptable\n solutions is still a subject of ongoing research. However\n there has been little rigorous comparison across methods\n proposed by different groups. This study systematically\n compared various regularization techniques for solving the\n ECGI problem under a unified simulation framework,\n consisting of both 1) progressively more complex idealized\n source models (from single dipole to triplet of dipoles),\n and 2) an electrolytic human torso tank containing a live\n canine heart, with the cardiac source being modeled by\n potentials measured on a cylindrical cage placed around\n the heart. We tested 13 different regularization\n techniques to solve the inverse problem of recovering\n epicardial potentials, and found that non-quadratic\n methods (total variation algorithms) and first-order and\n second-order Tikhonov regularizations outperformed other\n methodologies and resulted in similar average\n reconstruction errors.\",\n bibdate = \"Sun Sep 21 21:53:35 2014\",\n pmcid = \"PMC4154607\",\n}\n\n","author_short":["Milanic, M.","Jazbinsek, V.","Macleod, R.","Brooks, D.","Hren, R."],"key":"RSM:Mil2014","id":"RSM:Mil2014","bibbaseid":"milanic-jazbinsek-macleod-brooks-hren-assessmentofregularizationtechniquesforelectrocardiographicimaging-2014","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0,"html":""},"search_terms":["assessment","regularization","techniques","electrocardiographic","imaging","milanic","jazbinsek","macleod","brooks","hren"],"keywords":[],"authorIDs":[],"dataSources":["5HG3Kp8zRwDd7FotB"]}