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Abstract #3740

Algorithm for Automated Identification of Spectral Characteristics

Venkata Veerendranadh Chebrolu1, Michael Wullenweber2, Andreas Schaefer2, Johann Sukkau2, and Peter Kollasch1
1Siemens Medical Solutions USA, Inc., Rochester, MN, United States, 2Siemens Healthcare GmbH, Erlangen, Germany

Automated identification of proton spectral characteristics has potential utility in accurate spectral fat saturation, improving dynamic shim routines, and optimizing bandwidth of radiofrequency pulses used in multi-slice or multi-band excitation. In this work, we present an algorithm for automated identification of fat and water proton spectral characteristics and evaluate its performance in 30 proton spectra from breast (number of subjects: n=20), ankle (n=11), and knee (n=9) anatomical regions.

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