We are excited to share that our work “Adaptation of Prompt-enabled Segment-Anything-Model Enhance the Accuracy and Generalizability of Cine Cardiac Magnetic Resonance Segmentation” by Zhennong Chen, Sekeun Kim, Hui Ren and Xiang Li has been accepted for presentation at American
CAMCA’s Latest Research Paper “Motion Correction and Super-Resolution for Multi-slice Cardiac Magnetic Resonance Imaging via an End-to-End Deep Learning Approach”
Are you encountering challenges when using 2D Cardiac MR slices for 3D Cardiac analysis? Here are two major hurdles: 1. Slice thickness (usually 8-10mm) results in missing data between neighboring slices.2. Misalignment between slices due to motion. These issues impact
CAMCA Paper Published in the American Association of Physicists in Medicine
Our latest study “Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning” led by Zhennong Chen and Dufan Wu takes on the challenge of imperfections in head motion artifacts in head CT!