Yong Chen

Yong Chen, PhD is an Assistant Professor in the Department of Radiology at the Case Western Reserve University School of Medicine. He serves as Co-Director of the Imaging Research Core at the School of Medicine and is a Member of the Cancer Imaging Program at the Case Comprehensive Cancer Center. His academic career is centered around advancing quantitative magnetic resonance imaging techniques for clinical applications.

Dr. Chen's research primarily focuses on the development, validation, and application of novel quantitative magnetic resonance imaging techniques for diagnosis and characterization of tumors in multiple organs including kidney, breast, and liver. His work has made significant contributions to the development and optimization of Magnetic Resonance Fingerprinting (MRF) technique, covering multiple aspects from data acquisition to image reconstruction and post-processing. Over the years, he has built extensive experience in developing and testing advanced MRI techniques for quantification of various tissue properties from T1 and T2 relaxation times to water diffusion and perfusion.

Dr. Chen has broad experience in designing and conducting MR experiments in different organs, including brain, liver, kidney, breast, and heart. His research portfolio demonstrates a strong integration of artificial intelligence and deep learning with medical imaging, particularly in accelerating MRI acquisition and improving image reconstruction. Recent publications indicate a growing emphasis on applying machine learning approaches to neuroimaging, cancer diagnostics, and medical image harmonization, with significant contributions across multiple high-impact journals in radiology, medical physics, and neuroimaging.

Dr. Chen has been actively involved in collaborative research, particularly through his Clinical Translational Science Award (KL2) training, which has prepared him well for collaborations with researchers, physicians, and scientists from multiple perspectives. His work spans both technical MRI development and clinical applications, bridging the gap between engineering and medicine to improve diagnostic capabilities and patient outcomes.