Abstract #3894

Statistical Comparison of DT-MRI Interpolation Methods using Cardiac DT-MRI Data

Jin Kyu Gahm^1,2, Nicholas Wisniewski³, William S. Klug⁴, Alan Garfinkel^3,5, Daniel B. Ennis^1,6

¹Department of Radiological Sciences, University of California, Los Angeles, CA, United States; ²Department of Computer Science, University of California, Los Angeles, CA, United States; ³Department of Medicine, University of California, Los Angeles, CA, United States; ⁴Department of Mechanical & Aerospace Engineering, University of California, Los Angeles, CA; ⁵Department of Physiological Science, University of California, Los Angeles, CA, United States; ⁶Biomedical Engineering Interdepartmental Program, University of California, Los Angeles, CA, United States

DT-MRI interpolation is the process of estimating diffusion tensors at arbitrary points in space from regularly sampled tensor data. Tensor interpolation is important for tensor-based fiber tractography, registration, volume rendering, and computational model building. In this work, we use bootstrap statistical methods to compare four different DT-MRI interpolation methods accuracies for recovering the tensor shape (invariants) and orientation of unknown tensors from known tensor data. By using a cardiac DT-MRI dataset, we show the statistical bootstrapping results for the paired comparisons, and present recommendations for the selection of the DT-MRI interpolation method.