41 Computer Vision News Zhangxing Bian Zhangxing revisits both methodologies in this work, highlighting the current limitations of deep learning in biomedical applications and emphasizing that it is not a universal solution to this problem. Instead, he advocates for a balanced approach that integrates classic signal processing. “People’s understanding of tag fading is currently not very complete,” he advises. “Our first contribution is to model the tag fading by considering factors that previous research ignored. The interplay between the T1 relaxation and the repeated application of radio frequency pulses during the imaging sequences was overlooked in previous research on tMRI post-processing. We build a mathematical model to factor that interplay into the equation.” The findings of this work are derived from both simulated images and an actual phantom scan. Experiments on synthetic and real tMRI reveal the limitations of widely used similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time. While not proposing a new algorithm, this multidimensional work encompasses a thorough comparative analysis between deep learning and traditional The left image is a sagittal view of a head. The video on the right shows the tagged-MRI acquired during speech when the tongue is moving. The tagged-MRI has a significant phenomenon called, tag fading, which a gradual decrease in tag visibility over time. The brightness constancy assumption used in optical flow or image registration does not hold, which leads to inaccurate motion estimation.
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