MRI offers an unprecedented opportunity to noninvasively examine the morphology and function of the human body in vivo. Yet, the quest for higher diagnostic utility by increasing image quality and diversity is often countered by limitations due to experimental and economic concerns. This talk will convey an overview of research at ICON Lab at Bilkent University towards addressing fundamental limitations to enable favorable trade-offs among imaging parameters. Technological innovations include high-resolution targeted pulse sequences, compressive sensing algorithms, as well as deep learning and other machine learning techniques for image processing and statistical modeling. These strategies can achieve substantial improvements in image quality for both structural and functional MRI. Challenging applications that involve the inverse problems of image reconstruction and image synthesis will be showcased.
Biography: 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.