Kang Wang1, Tao Zhang2, Philip J. Beatty3, Dan W. Rettmann4, Ersin Bayram5, James H. Holmes1
1Global Applied Science Laboratory, GE Healthcare, Madison, WI, United States; 2Electrical Engineering, Stanford University, Stanford, CA, United States; 3Sunnybrook Research Institute, Toronto, ON, Canada; 4Global Applied Science Laboratory, GE Healthcare, Rochester, MN, United States; 5GE Healthcare, Waukesha, WI, United States
Auto-calibrating parallel imaging (acPI) methods have advantages over physically-modeled methods in reduced FOV applications or when it is difficult to accurately measure coil sensitivity maps, such as breath-hold exams. However, for challenging clinical protocols that use large channel counts, big matrix sizes and high parallel imaging factors, conventional channel-by-channel acPI methods may still have long reconstruction latency. To address this issue, Coil Compression (CC) and Direct Virtual Coil (DVC) techniques have been proposed independently. This work is to demonstrate the feasibility of combining the two techniques to achieve even higher reduction in computation without compromise in image quality.