TY - GEN
T1 - Denoising moving heart wall fibers using cartan frames
AU - Samari, Babak
AU - Aumentado-Armstrong, Tristan
AU - Strijkers, Gustav J.
AU - Froeling, Martijn
AU - Siddiqi, Kaleem
N1 - Funding Information:
Acknowledgments. This work was supported by research and training grants from the Natural Sciences and Engineering Research Council of Canada.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Current denoising methods for diffusion weighted images can obtain high quality estimates of local fiber orientation in static structures. However, recovering reliable fiber orientation from in vivo data is considerably more difficult. To address this problem we use a geometric approach, with a spatio-temporal Cartan frame field to model spatial (within time-frame) and temporal (between time-frame) rotations within a single consistent mathematical framework. The key idea is to calculate the Cartan structural connection parameters, and then fit probability distributions to these volumetric scalar fields. Voxels with low log-likelihood with respect to these distributions signal geometrical “noise” or outliers. With experiments on both simulated (canine) moving fiber data and on an in vivo human heart sequence, we demonstrate the promise of this approach for outlier detection and denoising via inpainting.
AB - Current denoising methods for diffusion weighted images can obtain high quality estimates of local fiber orientation in static structures. However, recovering reliable fiber orientation from in vivo data is considerably more difficult. To address this problem we use a geometric approach, with a spatio-temporal Cartan frame field to model spatial (within time-frame) and temporal (between time-frame) rotations within a single consistent mathematical framework. The key idea is to calculate the Cartan structural connection parameters, and then fit probability distributions to these volumetric scalar fields. Voxels with low log-likelihood with respect to these distributions signal geometrical “noise” or outliers. With experiments on both simulated (canine) moving fiber data and on an in vivo human heart sequence, we demonstrate the promise of this approach for outlier detection and denoising via inpainting.
UR - https://www.scopus.com/pages/publications/85029384771
U2 - 10.1007/978-3-319-66182-7_77
DO - 10.1007/978-3-319-66182-7_77
M3 - Conference contribution
AN - SCOPUS:85029384771
SN - 9783319661810
VL - 10433 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 672
EP - 680
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PB - Springer-Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -