Abstract #3201

Convolutional Forward Modeling for Actual Slice Profile Estimation

Xiaoguang Lu¹, Peter Speier², and Ti-chiun Chang³

¹Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, United States, ²Siemens Healthcare, Erlangen, Germany, ³Siemens Corporate Technology, Princeton, NJ, United States

Resolving slice thickness for better MR reconstruction is desirable, where actual slice profile plays a crucial role. Conventional blind deconvolution formulation includes both original signals and slice profile as unknowns, which is an ill-posed problem with high complexity. We propose a convolutional forward model (CFM), leveraging additional orthogonal stack(s) with an added convolution process in the formulation to fit actual forward imaging process accurately, resulting in a significantly simplified slice profile estimation problem. The actual slice profile is calculated through a data-driven approach. Experimental results demonstrate that the proposed method is robust to handle various challenges.

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