Zhangxing Bian is a third-year PhD candidate at Johns Hopkins University. Fresh from winning the 2024 Image Processing Best Student Paper Award at the SPIE Medical Imaging conference, he is here to tell us more about his work on tag fading, a post-processing complication that affects tagged MRI. Computer Vision News 40 Image Processing Best Student Paper Is registering raw tagged-MR enough for strain estimation in the era of deep learning? Tagged MRI (tMRI) is a specialized technique that adds specific patterns to tissues, similar to temporary tattoos. When the tissue moves, the tag moves with it, and clinicians can track these movements to better understand cardiac, muscular, and speechrelated functions post-injury or in a disease context. However, a challenge within this domain is the phenomenon of tag fading, where the visibility of tags diminishes over time, complicating accurate motion tracking and analysis. In this paper, Zhangxing wants to understand what causes tag fading and what can be done post-processing to estimate tissue motion better. “Two decades ago, researchers proposed some classic signal processing methods, which extract the material’s phase information through a Fourier transform for tracking the motion of the tissue,” he explains. “It can be seen as a special type of phase-based optical flow approach. The benefit is it circumvents the tag fading problem.” Recent advancements in deep learning have sparked a revolution across various fields, presenting alternative methods that do not use those classic techniques to preprocess the image but directly process raw tMRI inputs to estimate the motion or strain fields of the moving tissues.
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