אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Magnetic resonance imaging (MRI) is a powerful, ionizing-radiation-free medical imaging modality. The vast physical and physiological parameters, which MRI is sensitive to, makes it possible to visualize both structure and function in the body. However the prolonged time necessary to capture the information in this large parameter space remains a major limitation of this phenomenal modality, which the field of computational MRI aims to address. By computational MRI we refer to the joint optimization of the imaging system hardware, the data encoding, the data acquisition and the image reconstruction together. In this talk I will describe some of the efforts my group has been engaged in towards mitigating with motion and dynamics that occurs during MRI scanning, in particular when performing body imaging of pediatric patients. Specifically I will focus on unsupervised and supervised methods for dynamic 2D and 3D imaging and learning based high fidelity reconstructions of fine structures and textures. Short bio: Michael (Miki) Lustig is a Professor in Electrical Engineering and Computer Science. He joined the faculty of the EECS Department at UC Berkeley in Spring 2010. He received his B.Sc. in Electrical Engineering from the Technion, Israel Institute of Technology in 2002. He received his MSc and Ph.D. in Electrical Engineering from Stanford University in 2004 and 2008, respectively. His research focuses on computational imaging methods in medical imaging, particularly Magnetic Resonance Imaging (MRI)— these include a spectrum of work ranging from Hardware, through MRI pulse sequences and acquisitions, Image reconstruction and clinical applications of MRI. Miki is a Fellow of the Society of Magnetic Resonance in Medicine.