Shaping Next-Generation MRI Systems with Deep Learning

Abstract: MRI offers an unrivaled opportunity to noninvasively examine the morphology of the human body under a rich set of tissue contrasts. Yet, slow imaging speed in MR scanners impose limitation on the quality and diversity of diagnostic images that can be acquired. Classical approaches to reconstruction and analysis of imaging data fail to address this limitation effectively. In this talk, I will share an overview of recent efforts from my lab on devising deep learning approaches to surpass fundamental barriers to the diagnostic utility of MR exams. I will showcase neural network architectures and data-driven learning strategies that empower rapid, high-quality and high-sensitivity assessments.

 

Bio: Dr. Çukur received his B.S. degree from Bilkent University in 2003, and his Ph.D. degree from Stanford University in 2009, both in Electrical Engineering. He was a postdoctoral fellow at Helen Wills Neuroscience Institute at University of California, Berkeley till 2013. Currently, he is an Associate Professor in the Department of Electrical and Electronics Engineering, UMRAM, and Neuroscience Program at Bilkent University. His lab develops computational imaging methods for understanding the anatomy and function of biological systems in normal and disease states. He is the recipient of TUBITAK Career Award (2015), TUBA-GEBIP Outstanding Young Scientist Award (2015), BAGEP Young Scientist Award (2017), IEEE Turkey Research Encouragement Award (2017), Science Heroes Association Young Scientist of the Year Award (2017), METU Prof. Dr. Mustafa Parlar Foundation Research Incentive Award (2019), and he is a senior member of IEEE (2017).