活体分子代谢影像在结构、功能影像之上增添了更为本质的分子水平信息,从全局动态角度对疾病发生发展中代谢异常进行可视化和量化,对新药研发、机制研究和早期精准诊疗有重大意义。
课题组立足国家重大需求,抓住我国高端医疗设备和人工智能的发展机遇,紧密围绕“活体代谢磁共振和多模影像”科学问题和临床需求的一体两翼,开展临床前动物成像到临床转化的全链条研究。
研究领域包括:
1)面向临床应用的磁共振分子代谢影像 (CEST,31P-MRSI/MRI, 19F-MRI等);
2)小动物预临床磁共振及多模态分子影像,融合多模态、多组学的智能诊疗平台;
3)预临床和临床应用:肿瘤分子影像,脑和其他疾病代谢异常的时空分布,辅助药物和新疗法研发。
部分人体多色CEST成像方法代码已开源共享部分(https://github.com/easyCEST )
1. Chen W, Chen Z, Ma L, Wang Y, Song X*. Rapid and quantitative CEST-MRI sequence using water presaturation. Magn Reson Med. 2025 Feb;93(2):730-740.
2. Chen W, Wu S, Wang S, Li Z, Yang J, Yao H, Tian Q, Song X*. Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI. Med Image Anal. 2025 Jan;99:103359.
3. Wu S, Chen W, Li Z, Wang S, Sun H, Song X*. COMET: Cross-space Optimization-based Mutual learning network for super-resolution of CEST-MRI. IEEE J Biomed Health Inform. 2023 Oct 17: 309
4. Liu C, Li Z, Chen Z, Zhao B, Zheng Z, Song X*. Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction. Magn Reson Med. 2024 Aug;92(2):688-701.
5. Song X, Airan RD, Arifin DR, Bar-Shir A, Kadayakkara DK, Liu G, Gilad AA, van Zijl PC, McMahon MT, Bulte JW*. Label-free in vivo molecular imaging of underglycosylated mucin-1 expression in tumor cells. Nature Communications 2015 Mar 27;6:6719.