Ali Pirasteh1, Lloyd Estkowski2, Daniel Litwiller3, Ersin Bayram4, and Xinzeng Wang5
1Department of Radiology, UW Madison, Madison, WI, United States, 2Global MR Applications & Workflow, GE Healthcare, Madison, WI, United States, 3Global MR Applications & Workflow, GE Healthcare, Denver, CO, United States, 4Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States, 5GE Healthcare, Houston, TX, United States
1Department of Radiology, UW Madison, Madison, WI, United States, 2Global MR Applications & Workflow, GE Healthcare, Madison, WI, United States, 3Global MR Applications & Workflow, GE Healthcare, Denver, CO, United States, 4Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States, 5GE Healthcare, Houston, TX, United States
PROPELLER T2-weighted images of pelvis with DL
reconstruction resulted in improved image quality, including improved SNR,
in-plane resolution and robustness to motion.
Figure 4: FSE and PROPELLER images reconstructed using deep-learning
based reconstruction methods. Deep-learning based reconstruction method improved
the SNR and in-plane resolution of FSE images, but the motion artifacts were
not removed. Due to the robustness to motion and deep-learning based
reconstruction method, the PROPELLER images showed better SNR, in-plane
resolution and less motion artifacts.
Figure 5: High resolution rectal FSE and PROPELLER images of
a patient. Deep learning reconstruction method improved the SNR and in-plane
resolution. Due to the robustness to motion, PROPELLER further improved the
image sharpness.