Computer Number: 1

Y. Meng, D. Qiu
Emory University, Atlanta, United States

Impact: This work provides a fast robust QSM acquisition protocol for reducing measurement sensitivity to motion.

Computer Number: 2

Y. Zheng, H. Liu, Z. Liu, Y. Jin, Z. Li, W. Cui, Y. Wu, Z. Hu, D. Luo
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China, Shenzhen, China

Impact: The proposed method enhances the reliability and precision of CEST-MRI, facilitating its applications in clinical and research settings. 

Computer Number: 3

Y. Li, C-C Cheng, J. Dubey, J. Guenette, L. Qin, B. Madore
Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, China

Impact: The proposed approach is effective at reducing motion artifacts, and it was designed to be translatable to clinical practice, as it does not affect workflow.

Computer Number: 4

M. T. Arslan, F. Calakli, S. Warfield
Computational Radiology Laboratory, Boston Children's Hospital, Boston, United States

Impact: We developed computationally cheap novel algorithms that utilize a low-cost wearable inertial sensor to deliver accurate motion information with very high temporal resolution without requiring modifications to the sequence.

Computer Number: 5

B. Li, H. She
Shanghai Jiao Tong University, Shanghai, China

Impact: A flexible and time-efficient method based on aligned reconstruction framework was developed for rigid-body motion correction in accelerated brain MRI, which may be beneficial to the exams of clinical uncooperative patients as well as brain MRI research community.

Computer Number: 6

R. Andujar Lugo, Y. Juli, D. Nickel, B. Clifford, D. Splitthoff, W-C Lo, S. Y. Huang, J. Conklin, L. Wald, S. Cauley, D. Polak
Friedrich-Alexander University, Erlangen, Germany

Impact: We integrate retrospective motion correction into a data-driven deep learning network to facilitate fast and motion-robust 2D TSE imaging in the brain.

Computer Number: 7

P. Xu, S. Fujita, Y. Jun, B. Gagoski, O. Afacan, H. Liu, B. Bilgic
Zhejiang University, Hangzhou, China

Impact: We presented a motion-robust 3D multiparametric brain mapping technique that requires no external hardware for motion tracking and minimal increase (~9%) of the overall acquisition time.

Computer Number: 8

Y. Lin, D. Abraham, N. Wang, Z. Zhou, X. Cao, A. Nurdinova, K. Setsompop
Stanford University, Stanford, United States

Impact: Motion-iGROG enables rapid and accurate motion-corrected and background-phase-changed reconstruction, which could be employed synergistically with emerging high-temporal motion-tracking methods, such as pilot tone and advanced motion navigators, where 100s of motion states are obtained across each imaging scan.

Computer Number: 9

Z. Zariry, F. Lamberton, R. Frost, T. Gaass, T. Troalen, H. Rayson, J. Slipsager, N. Richard, J. Bonaiuto, A. Van Der Kouwe, B. Hiba
Institut des Sciences Cognitives - Marc Jeannerod / CNRS UMR5229, Bron, France

Impact:

The proposed approach enables in-vivo evaluation of head-motion tracking in MRI and, consequently, could contribute to improving motion artifact-correction for brain-MRI. Its impact will be substantial with the advent of ultra-high magnetic-field scanners and the widespread use of high-resolution brain-MRI.

Computer Number: 10

Q. Shen, W. Wu, M. Chiew, Y. Ji, J. Woods, T. Okell
University of Oxford, Oxford, United Kingdom

Impact: This work enhances the motion robustness of ASL imaging by improving navigator reconstruction under varying contrast and subtraction-based reconstruction with mismatched k-space. These improvements pave the way for broader clinical applications and more reliable diagnostic imaging. 

Computer Number: 11

X. Chao, X. Ma, K. Zhang, N. Balu, L. Han, P. Wu, H. Wang, Z. Chen
Fudan University, Shanghai, China

Impact: The proposed retrospective motion correction approach improves image quality of iSNAP, and enhances its clinical value.

Computer Number: 12

L. Ma, Z. He, W. Lin, L. Gang
The University of North Carolina at Chapel Hill, Chapel Hill, United States

Impact: The proposed fetal brain MRI 3D volume reconstruction method based on CNN and Transformer can solve arbitrary motion correction of 2D slices and reconstruct high-resolution fetal brain MRI 3D volumes effectively and efficiently.

Computer Number: 13

A. Hassan, M. Yaser, I. Mohamed, M. Medhat, M. Ismail, M. M. Makary, M. A. Al-masni
Cairo University, Cairo, Egypt

Impact: Correcting motion artifacts in MRI scans enhances image quality, making them more reliable for clinical diagnosis. Additionally, using this approach as a preprocessing step for tasks like registration and segmentation boosts model accuracy and supports improved diagnostic outcomes.

Computer Number: 14

H. Zhuang, Z. Ke, Y. Zhao, R. Guo, Y. Li, W. Jin, Z. Cheng, Y. Zhang, W. Tang, M. Zhang, Z-P Liang, Y. Li
Shanghai Jiao Tong University, Shanghai, China

Impact: A novel motion correction method has been developed for high-resolution, non-water-suppressed MRSI of the brain. This method has the potential to significantly improve the robustness and clinical applicability of non-water-suppressed MRSI.

Computer Number: 15

S. Bhattacharya, A. Price, A. Uus, H. S. Sousa, M. Marenzana, K. Colford, M. Lee, L. Cordero-Grande, S. Malik, M. Deprez
King's College London, London, United Kingdom

Impact: This should improve the usability of the previously proposed T₂ measurement pipeline and allow for faster scan times as less data is required as well as improve robustness to motion when all nine stacks are used. 

Computer Number: 16

Y. Brackenier, R. P. Teixeira, L. Cordero-Grande, E. Ljunberg, N. Bourke, T. Arichi, S. Deoni, S. Williams, J. V. Hajnal
King's College London, London, United Kingdom

Impact: alignedSENSE, combined with phase correction, can effectively improve image quality without increasing scan time in ULF MRI systems without increasing scan time, making them more valuable for clinical use.