Abstract #1559

Adaptive background phase removal using knowledge-based region detection for quantitative susceptibility mapping

Taichiro Shiodera¹, Takamasa Sugiura¹, Yuko Hara¹, Yasunori Taguchi¹, Tomoyuki Takeguchi¹, Masao Yui², Naotaka Sakashita², Yasutaka Fushimi³, Takuya Hinoda³, Tomohisa Okada³, Aki Kido³, and Kaori Togashi³

¹Toshiba Corporation, Kawasaki, Japan, ²Toshiba Medical Systems Corporation, Otawara, Japan, ³Kyoto University Graduate School of Medicine, Kyoto, Japan

We propose a background phase removal method for quantitative susceptibility mapping using adaptive kernels depending on brain region. Conventional methods use distance adaptive kernel spherical mean values (SMV) to estimate background phase. However, artifacts occur where kernel sizes are not optimal for certain brain regions. Here, we adapt SMV kernel sizes depending on brain regions which are automatically detected by machine learning methods. The proposed method eliminates tissue phase artifacts near air-tissue interfaces in more central areas such as the sinus. The proposed method also eliminates streak artifacts in susceptibility images.

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