Abstract #1173

DSC-derived perfusion map generation from DCE MRI using deep learning

Haoyang Pei^1,2, Yixuan Lyu^2,3, Sebastian Lambrecht^4,5,6, Doris Lin⁵, Li Feng¹, Fang Liu⁷, Paul Nyquist⁸, Peter van Zijl^5,9, Linda Knutsson^5,9,10, and Xiang Xu^1,5

¹Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ²Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York City, NY, United States, ³Image Processing Center, School of Astronautics, Beihang University, Beijing, China, ⁴Department of Neurology, Technical University of Munich, Munich, Germany, ⁵Department of Radiology, Johns Hopkins University, Baltimore, MD, United States, ⁶Institute of Neuroradiology, Ludwig-Maximilians-Universität, Munich, Germany, ⁷Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, ⁸Department of Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁹F.M Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ¹⁰Department of Medical Radiation Physics, Lund University, Lund, Sweden

Synopsis

Keywords: Machine Learning/Artificial Intelligence, PerfusionThis study built a deep-learning-based method to directly extract DSC MRI perfusion and perfusion related parameters from DCE MRI. A conditional generative adversarial network was modified to solve the pixel-to-pixel perfusion map generation problem. We demonstrate that in both healthy and brain tumor patients, highly realistic perfusion and perfusion related parameter maps can be synthesized from the DCE MRI using this deep-learning method. In healthy controls, the synthesized parameters had distribution similar to the ground truth DSC MRI values. In tumor regions, the synthesized parameters correlated linearly with the ground truth values.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.