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Abstract #3749

Optimal Channel Selection for Respiratory Self-Gating Signals

Robert Grimm1, Simon Bauer2, Berthold Kiefer2, Joachim Hornegger1, Kai Tobias Block3

1Pattern Recognition Lab, FAU Erlangen-Nuremberg, Erlangen, Germany; 2Siemens Healthcare, Erlangen, Germany; 3Department of Radiology, NYU Langone Medical Center, New York City, NY, United States

In most respiratory self-gating techniques, the self-gating signal can be derived from all acquired channels. However, only coil elements close to the diaphragm deliver a reliable signal. Therefore, previous approaches usually required manual selection of an appropriate coil element or fixed scan protocols, both of which is infeasible for clinical routine applications.

Keywords

abdomen acceptance accepting according acquisition acquisitions affected afterwards amplitude applications applied applying automated automatic bellows best body breathing cardiac central channel channels city clinical coil compensate components computed consuming contrast contributions coronal curve dataset datasets denotes described detected detection deviation devices dimensional directly elements eliminates enabled enables energy equally every exemplary exhaled expected extraction favorable favors filter filtering finally fluctuations free frequencies frequency fully gated gating generate golden gradient highest includes indicates inhaled intermediate interrupt known local lowest lung magnetization magnitude magnitudes manually materials measure modified motion navigation navigators optimal orientation overall particularly partition peak poses procedure pronounced proper proposed quality radial radiology rating readout readouts reasonable receive recognition reconstruction reconstructions recorded reduced reliable respiration respiratory routine samples score selected selection self sensor separate shape significantly smooth smoothen smoothing sorting space spectral stable stack stage stages stars steady subject suitable suited system systems taking temporal trajectories transversal validate varying visually volunteers