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Abstract #0067

Accelerated MR Parameter Mapping Exploiting Model-Based Simultaneous Multi-Slice Reconstruction with Hankel Subspace Learning: Application to T1 Quantification

Sugil Kim1,2, Suhyung Park2, and Jaeseok Park2

1Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of, 2Biomedical Imaging and Engineering Lab, Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

MR parameter mapping has been potentially of great value in diagnosing pathological diseases, but is difficult to be translated to clinical applications due to prohibitively long imaging time. It was recently shown in [1-4] that simultaneous multi-slice (SMS) imaging is highly efficient in reducing imaging time while well maintaining SNR. In this work, we propose a novel, model-based SMS reconstruction approach with Hankel subspace learning (Model-based SMS-HSL) for highly accelerated MR parameter mapping under the hypothesis that the null space in the spatial dimension, which filters out slices of no interest, is time-invariant in the parameter dimension while the dimension of temporal basis, which is found from signal evolution models, is limited.

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