561-04-006 ISMRM Abstract

From Thick to Thin: a High-fidelity and Robust Reconstruction Framework for Brain Tumor MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Transferable Skills

561-04-006 · Deep Learning Meets k-Space: New Frontiers in Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row B

Keywords: Accelerated MRI reconstruction Brain Tumor MRI Multi-View Interpolation Self- Consistency Robust reconstruction

Accepted

Liqin Yang¹, Caohui Duan², Xin Lou^2,3, Dinggang Shen⁴, Kaicong Sun ^4,5

¹School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

²Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing, China

³The First Medical Center, Chinese PLA General Hospital, Beijing, China

⁴School of BME, ShanghaiTech University, United States of America

⁵School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

Presenting Author: Kaicong Sun

Synopsis

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References

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11. Lu Z, Wang J, Li Z, Ying S, Wang J, Shi J, Shen D. Two-Stage Self-Supervised Cycle-Consistency Transformer Network for Reducing Slice Gap in MR Images. IEEE J Biomed Health Inform. 2023;27(7):3337-3348. doi:10.1109/JBHI.2023.3271815. [doi]

12. Ibáñez L, Schroeder W, Ng L, et al. “The Insight Toolkit (ITK) Software Guide.” Kitware, Inc. 2015. (Or see: Yoo T S., Metaxas D. “Engineering and algorithm design for an image processing API: a technical report on ITK—the Insight Toolkit.” Stud. Health Technol. Inform. 2004;107(Pt 2):242-250.)

13. Zhao C, Dewey B E, Pham D L, et al. “SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning.” IEEE Trans. Med. Imag. 2021;40(3):805-817

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http://echo.ismrm.org/p/ISMRM2026/561-04-006