Computer Vision News 42 traditional methods to try to solve the tag fading issue. Zhangxing thinks this is why it piqued the interest of the SPIE Medical Imaging Best Paper Awards judges. “The conference typically has different specialization tracks,” he tells us. “The two major tracks are called Medical Imaging and Image Processing. I think one reason this paper drew the committee’s attention is that it provides both sides with an understanding of the tag fading issue. Also, our work is like a whistleblower to remind people that there is something that deep learning currently can’t handle well and that, two decades ago, the traditional signal processing method did pretty well and elegantly. This observation provides some promising directions for the field going forward.” Looking ahead, Zhangxing is working on extending the research by delving deep similarity metric learning. This technique has shown some promise in the inter-modality image registration task and is presumed to be robust against the challenges posed by tag fading. Although this work is limited to using the classic 1:1 SPAMM tagging sequence, the potential of using ComplementarySPAMM (CSPAMM) sequences to The classic 1:1 SPAMM sequence is used for acquiring taggedMRI. Each tagging step is followed by a series of imaging sequences. “TF” stands for timeframe. During each TR interval, the spin system is tipped by alpha degree and multiple line segments in k-space are captured using gradient echoes. The “tagging-imaging" cycle is repeated until sufficient k-space coverage is achieved. Based on this imaging sequence, a mathematical model has been built in this research for better understanding the tag fading process. “The aim of this research is to better understand the tag fading phenomenon itself and evaluate the effectiveness of both traditional harmonic phase-based methods and deep learning-based registration methods (trained with diverse similarity objectives) when estimating motion with the presence of tag fading.” Image Processing Best Student Paper
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