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Abstract #2438

A 3D Parametric Model for Imaging Normalization

Tiejun Zhao1, Kwan-Jin Jung2

1Siemens Healthcare USA; Siemens Medical Solutions USA, Inc., Pittsburgh, PA, United States; 2Psychology, Carnegie-Mellon University, Pittsburgh, PA, United States

Multi-channel receiving (Rx) coils have become a standard asset of routine MR imaging due to its improved signal-to-noise ratio and the parallel imaging capabilities. However, the image intensity variations from the receiving profile could be problematic for tissue segmentations and various quantification analyses. While the pre-scan normalization that acquires additional data using body coil provided a popular approach for removing this imaging shading artifact, a retrospective normalization can still be invaluable especially when the extra data is not available due to various reasons (e.g., the original protocol did not include a pre-scan for normalization or a uniform body coil is not available for some head only scanners or current most 7T scanners.) In this abstract, we proposed and demonstrated a simple 3D parametric model for modeling and removing the smooth image intensity variations presented in images acquired with multi-channel Rx coil.