Deep Learning based de-noising and segmentation of Real-Time 3D Kinematic Imaging of the knee for modeling patellofemoral bone kinematics

Motivation: Joint Maltracking or improper loading cannot be assessed with conventional, static MRI. Goal(s): Demonstrate the feasibility of using images without motion to de-noise and segment real-time 4D images and generate 4D moving models. Approach: In 31 subjects, a fully sampled image and many highly-undersampled images reconstructed from the same data acquired without motion are used to train a neural network to generate artifact-free images and bone segmentations for images acquired with motion. Results: The resulting real-time images are recognizable however more work is needed to improve the reliability of the segmentation, especially in cases of large-scale or fast motion. Impact: Deep learning based de-noising and segmentation of real-time 3D kinematic MR imaging make it possible to model knee kinematics and open the doors for the study of the knee in motion and under load for improved identification of pain generators.