TY - GEN
T1 - Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
AU - Eichhorn, Hannah
AU - Spieker, Veronika
AU - Hammernik, Kerstin
AU - Saks, Elisa
AU - Weiss, Kilian
AU - Preibisch, Christine
AU - Schnabel, Julia A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://github.com/compai-lab/2024-miccai-eichhorn.
AB - We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consistent reconstruction. We demonstrate the potential of PHIMO for the application of T2* quantification from gradient echo MRI, which is particularly sensitive to motion due to its sensitivity to magnetic field inhomogeneities. A state-of-the-art technique for motion correction requires redundant acquisition of the k-space center, prolonging the acquisition. We show that PHIMO can detect and exclude intra-scan motion events and, thus, correct for severe motion artifacts. PHIMO approaches the performance of the state-of-the-art motion correction method, while substantially reducing the acquisition time by over 40%, facilitating clinical applicability. Our code is available at https://github.com/compai-lab/2024-miccai-eichhorn.
KW - Data-Consistent Reconstruction
KW - Gradient Echo MRI
KW - Motion Detection
KW - Self-Supervised Learning
KW - T2 Quantification
UR - http://www.scopus.com/inward/record.url?scp=85212526361&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-72104-5_54
DO - 10.1007/978-3-031-72104-5_54
M3 - Conference contribution
AN - SCOPUS:85212526361
SN - 9783031721038
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 562
EP - 571
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Y2 - 6 October 2024 through 10 October 2024
ER -