PURPOSE: To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). METHODS: Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. RESULTS: A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. CONCLUSION: We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.
PURPOSE: To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). METHODS: Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. RESULTS: A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. CONCLUSION: We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.
Authors: Max H C van Riel; Zidan Yu; Shota Hodono; Ding Xia; Hersh Chandarana; Koji Fujimoto; Martijn A Cloos Journal: NMR Biomed Date: 2021-04-26 Impact factor: 4.044
Authors: Thomaz R Mostardeiro; Ananya Panda; Robert J Witte; Norbert G Campeau; Kiaran P McGee; Yi Sui; Aiming Lu Journal: MAGMA Date: 2021-05-04 Impact factor: 2.310
Authors: Bjoern H Menze; Marion I Menzel; Juan A Hernandez-Tamames; Carolin M Pirkl; Laura Nunez-Gonzalez; Florian Kofler; Sebastian Endt; Lioba Grundl; Mohammad Golbabaee; Pedro A Gómez; Matteo Cencini; Guido Buonincontri; Rolf F Schulte; Marion Smits; Benedikt Wiestler Journal: Neuroradiology Date: 2021-04-09 Impact factor: 2.804