Meeting Banner
Abstract #4039

Improved, Real-Time Artifact Detection and Reacquisiton for Diffusion Tensor Imaging (DTI)

Yue Li1,2, Steven M. Shea2,3, Hangyi Jiang3, Christine H. Lorenz2,3, Susumu Mori3

1Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States; 2Center for Applied Medical Imaging, Siemens Corporate Research, Baltimore, MD, United States; 3Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Subpixel motion artifacts caused by pulsation often introduces severe artifacts in diffusion weighted images and incorrect tensor estimation. Previously fitting-based outlier rejection methods have been proposed to obtain robust tensor estimation. This presentation extended the past efforts from two aspects. First, a new non-fitting-based quality criterion was added, which outperforms the existing method when fitting becomes unstable due to multiple outliers. Second, we implemented this algorithm into Siemens Image Calculation Environment such that reacquisition of corrupted slices can occur inline. Preliminary test results showed improvements with our method and reacquisition of data in real-time reduced the presence of artifacts.