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Abstract #2838

Denoising Induced Iterative Reconstruction for Fast $$$T_{1\rho}$$$ Parameter Mapping

Qingyong Zhu1, Yuanyuan Liu2, Zhuo-Xu Cui1, Ziwen Ke1, and Dong Liang1,2
1Research Center for Medical AI, SIAT, Chinese Academy of Sciences, Shenzhen, China, 2Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen, China

We propose a novel DenOising induCed iTerative recOnstRuction framework (DOCTOR) to realize fast $$$T_{1\rho}$$$ parameter mapping from under-sampled k-space measurements. The proposed formulation constrains simultaneously intensity-based and orientation-based similarity between the reconstructed images and denoised prior images. Two state-of-art 3D denoising technologies are utilized including NLM3D and BM4D. The reconstruction alternates between two steps of denoising and a quadratic programming attacked by non-linear conjugate gradient method. The parameter maps are created from the reconstructed images using conventional fitting with an established relaxometry model. Through experiments in-vivo $$$T_{1\rho}$$$-weighted brain MRI datasets, we can observe superior image-quality of the proposed DOCTOR scheme.

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