bibtext: @InProceedings{ He_F2TNet_MICCAI2024,
                   author = { He, Zhibin and Li, Wuyang and Jiang, Yu and Peng, Zhihao and Wang, Pengyu and Li, Xiang and Liu, Tianming and Han, Junwei and Zhang, Tuo and Yuan, Yixuan },
                   title = { { F2TNet: FMRI to T1w MRI Knowledge Transfer Network for Brain Multi-phenotype Prediction } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Using brain imaging data to predict the non-neuroimaging phenotypes at the individual level is a fundamental goal of system neuroscience. Despite its significance, the high acquisition cost of functional Magnetic Resonance Imaging (fMRI) hampers its clinical translation in phenotype prediction, while the analysis based solely on cost-efficient T1-weighted (T1w) MRI yields inferior performance than fMRI. The reasons lie in that existing works ignore two significant challenges. 1) they neglect the knowledge transfer from fMRI to T1w MRI, failing to achieve effective prediction using cost-efficient T1w MRI. 2) They are limited to predicting a single phenotype and cannot capture the intrinsic dependence among various phenotypes, such as strength and endurance, preventing comprehensive and accurate clinical analysis. To tackle these issues, we propose an FMRI to T1w MRI knowledge transfer Network (F2TNet) to achieve cost-efficient and effective analysis on brain multi-phenotype, representing the first attempt in this field, which consists of a Phenotypes-guided Knowledge Transfer (PgKT) module and a modality-aware Multi-phenotype Prediction (MpP) module. Specifically, PgKT aligns brain nodes across modalities by solving a bipartite graph-matching problem, thereby achieving adaptive knowledge transfer from fMRI to T1w MRI through the guidance of multi-phenotype. Then, MpP enriches the phenotype codes with cross-modal complementary information and decomposes these codes to enable accurate multi-phenotype prediction. Experimental results demonstrate that the F2TNet significantly improves the prediction of brain multi-phenotype and outperforms state-of-the-art methods. The code is available at https://github.com/CUHK-AIM-Group/F2TNet.
title: F2TNet: FMRI to T1w MRI Knowledge Transfer Network for Brain Multi-phenotype Prediction
authors: He, Zhibin Li, Wuyang Jiang, Yu Peng, Zhihao Wang, Pengyu Li, Xiang Liu, Tianming Han, Junwei Zhang, Tuo Yuan, Yixuan