TY - GEN
T1 - Accelerated Free-Breathing 5D Multi-Echo Respiratory Motion-Resolved R2*, PDFF, and QSM Using Novel Composite Total Variation
AU - Kang, Mung Soo
AU - Alus, Or
AU - Kee, Youngwook
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - We introduce a novel composite total variation (TV) and its solution algorithm with their application to multi-echo, respiratory motion-resolved 5D (3D space + 1D respiratory motion + 1D echo signal evolution) compressed sensing (CS) abdominal MR image reconstruction. The proposed formalism ensures a sparse representation between multi-echo images with varying contrast—a vital feature that needs to be preserved—making it highly suitable for applications in multi-dimensional computational/quantitative imaging. The key idea of the proposed composite TV and its formal definition were inspired by the observation that the spatial gradient of difference images in multi-echo MRI appears sparse. Throughout extensive experiments on a small number of healthy volunteers, we have demonstrated improved performance of the proposed method in 5D motion-resolved CS reconstruction of multi-echo MRI data compared to the state-of-the-art method. We have also demonstrated improved performance of the proposed method in quantitative tissue parameter mapping (such as R2*, proton density fat fraction, and quantitative susceptibility mapping) across a wide range of undersampling factors. In conclusion, the proposed method enables vastly accelerated motion-resolved multi-echo CS-MRI minimally impacting the quantification of downstream tissue parameters.
AB - We introduce a novel composite total variation (TV) and its solution algorithm with their application to multi-echo, respiratory motion-resolved 5D (3D space + 1D respiratory motion + 1D echo signal evolution) compressed sensing (CS) abdominal MR image reconstruction. The proposed formalism ensures a sparse representation between multi-echo images with varying contrast—a vital feature that needs to be preserved—making it highly suitable for applications in multi-dimensional computational/quantitative imaging. The key idea of the proposed composite TV and its formal definition were inspired by the observation that the spatial gradient of difference images in multi-echo MRI appears sparse. Throughout extensive experiments on a small number of healthy volunteers, we have demonstrated improved performance of the proposed method in 5D motion-resolved CS reconstruction of multi-echo MRI data compared to the state-of-the-art method. We have also demonstrated improved performance of the proposed method in quantitative tissue parameter mapping (such as R2*, proton density fat fraction, and quantitative susceptibility mapping) across a wide range of undersampling factors. In conclusion, the proposed method enables vastly accelerated motion-resolved multi-echo CS-MRI minimally impacting the quantification of downstream tissue parameters.
KW - Compressed sensing
KW - Model-based MR image reconstruction
KW - Non-Cartesian multi-echo MRI
KW - Quantitative imaging
UR - https://www.scopus.com/pages/publications/105017859231
U2 - 10.1007/978-3-032-04947-6_3
DO - 10.1007/978-3-032-04947-6_3
M3 - Conference contribution
AN - SCOPUS:105017859231
SN - 9783032049469
T3 - Lecture Notes in Computer Science
SP - 24
EP - 34
BT - Medical Image Computing and Computer Assisted Intervention , MICCAI 2025 - 28th International Conference, 2025, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Y2 - 23 September 2025 through 27 September 2025
ER -