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2 April 2024 Is registering raw tagged-MR enough for strain estimation in the era of deep learning?
Zhangxing Bian, Ahmed Alshareef, Shuwen Wei, Junyu Chen, Yuli Wang, Jonghye Woo, Dzung L. Pham, Jiachen Zhuo, Aaron Carass, Jerry L. Prince
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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260A (2024) https://doi.org/10.1117/12.3006906
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between T1 relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zhangxing Bian, Ahmed Alshareef, Shuwen Wei, Junyu Chen, Yuli Wang, Jonghye Woo, Dzung L. Pham, Jiachen Zhuo, Aaron Carass, and Jerry L. Prince "Is registering raw tagged-MR enough for strain estimation in the era of deep learning?", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260A (2 April 2024); https://doi.org/10.1117/12.3006906
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KEYWORDS
Image registration
Deformation
Anatomy
Deep learning
Magnetic resonance imaging
Motion estimation
Motion measurement
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