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Abstract #4480

An efficient CEST workflow using joint optimization of sampling, reconstruction and quantification

Chuyu Liu1, Zhongsen Li1, and Xiaolei Song1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

Synopsis

Keywords: CEST / APT / NOE, CEST & MT

Motivation: As an exciting ‘label-free' molecular imaging technique, CEST workflow is always time-consuming, because of the seconds-long TR and multiple frequency repetitions in acquisition, the iteration in reconstruction, and the pixel-by-pixel in B0 correction and quantification.

Goal(s): To achieve rapid and high-quality sampling, reconstruction and quantification of CEST-MRI.

Approach: We constructed a data-driven CEST framework, by joint optimization of k-space sampling, reconstruction and quantification.

Results: Retrospective experiments on human brain demonstrated the feasibility of combination with acceleration techniques including parallel imaging, compress sensing or deep learning, allowing 6X under-sampling rate and reconstruction of high-quality contrast maps in one second.

Impact: A data-driven CEST framework enabled joint optimization of k-space sampling,reconstruction and quantification. Retrospective experiments demonstrated that the the framework allows 6X under-sampling rate and reconstruction of high-quality contrast maps in one second. This one-stop workflow may facilitate more clinical needs.

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Keywords