Shahrzad Moinian

Impact: The ultra-fast, cross-platform, and user-friendly SMRI platform was developed for deep learning training sample generation, providing available and sufficient datasets for various deep learning-based MRI tasks within an acceptable time.

A. Costanzo, M. Pereira, Y. Modarai, A. Lim, D. Young, M. Wagner, L. Vidarsson, B. Ertl-Wagner, D. Sussman
Toronto Metropolitan University, Toronto, Canada

Impact: FetAS provides clinicians with advanced fetal MRI diagnostic tools, enhancing efficiency and patient outcomes while supporting limited fetal radiological expertise. It also accelerates fetal MRI research by enabling systematic data extraction. FetAS is currently in a multisite clinical validation study.

Y. Zhou, M. Huang, J. Chen, J. Zhou, T. Kang, J. Lin, L. Qian, S. Liu, Y. Long, Q. Hong, L. Zhu, J. Zhou, D. Guo, X. Qu
Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Institute of Artificial Intelligence, Xiamen University, Xiamen, China

Impact: The integration of direct k-space rawdata acquisition from MRI devices into CloudBrain-ReconAI enhances the efficiency and timeliness of MRI data processing, facilitating faster clinical decisions and research advancements.

Y. Hu, X. He, F. Liu
Athinoula A. Martinos Center for Biomedical Imaging, Boston, United States

Impact: Our training-free adaptation method circumvents the need for laborious data collection and labeling, providing a generalizable solution for applying vision foundation models to medical image segmentation.

Y. Liu, T. Chiang, H. Feng, S. Luo, J. Zhang, S. Li, M. Moseley, G. Zaharchuk
Stanford University, Palo Alto, United States

Impact: This foundation model represents the first AI method to standardize spatial normalization for a wide range of neuroimaging sequences, enabling real-time and consistent neuroimaging analyses for both clinical and research applications.

F. D'Antonio, S. Warrington, J. Manzano-Patron, T. Sprenger, J. Shin, P. Morgan, S. Sotiropoulos
Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom

Impact: Our results suggest that denoising in the complex domain compared to magnitude domain has the potential to lead to larger denoising benefits than any differences induced by the employed denoising approach (e.g. deep learning vs patch-based).

Impact: FetalSR minimizes data needed for high-isotropic-resolution fetal brain volume reconstruction, reduces scan time and increases reconstruction robustness. It enables quantification of brain morphological features of developmental and abnormal fetuses in large-scale and a wider range of clinical and neuroscientific studies.

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Impact: BboxHarmony proposes a novel concept of harmonizing data for a black-box model and may have an important impact in the real-world where most commercial networks are black-box.

J. Liu, F. Liu, K. Sun, C. Jiang, Y. Wang, T. Sun, F. Shi, D. Shen
ShanghaiTech University, Shanghai, China

Impact: We present BrainParc, the first lifespan brain parcellation framework using a single model, and evaluate it on the largest known lifespan sMRI dataset to date (over 91.3 thousand scans), achieving highest precision and consistency than other segmentation models.