Magnus Herberthson1, Tom Dela Haije2, Deneb Boito3,4, Aasa Feragen5, Carl-Fredrik Westin6, and Evren Özarslan3,4
1Dept. of Mathematics, Linköping University, Linköping, Sweden, 2Dept.of Computer Science, University of Copenhagen, Copenhagen, Denmark, 3Dept. of Biomedical Engineering, Linköping University, Linköping, Sweden, 4Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden, 5Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark, 6Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Boston, MA, United States
1Dept. of Mathematics, Linköping University, Linköping, Sweden, 2Dept.of Computer Science, University of Copenhagen, Copenhagen, Denmark, 3Dept. of Biomedical Engineering, Linköping University, Linköping, Sweden, 4Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden, 5Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark, 6Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Boston, MA, United States
We introduce
the QTI+ framework, which guarantees that the two first moments of
a diffusion tensor distribution characterizing tissue
fulfill appropriate mathematical conditions. QTI+
exhibits improved accuracy and precision under noisy conditions.
Figure 1. The estimation framework used in QTI+.
Figure 5. Maps of parameters estimated through all conventional (top two rows) and QTI+ methods. Red voxels in $$$\mu$$$FA depict complex values.
