Abstract #1315

Fractal Analysis of Real-Time BOLD Data from Healthy Kidneys

Marla Shaver¹, Michael Noseworthy²

¹School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada; ²School of Biomedical Engineering, Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada

In this study, real-time BOLD data from healthy kidneys was analyzed to determine whether signal variations are fractal, and if so whether they can be characterized as fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Images were acquired using a T2* weighted GRE EPI sequence. Subjects were instructed to breath quietly during image acquisition. Rapidly acquired BOLD data from both kidney cortex and medulla behave fractally, where the majority of the data is characterized as fGn.