Model-based image reconstruction with a hybrid regularizer

Jingyan Xu; Frédéric Noo

doi:10.1117/12.2293781

9 March 2018 Model-based image reconstruction with a hybrid regularizer

Jingyan Xu, Frédéric Noo

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 1057338 (2018) https://doi.org/10.1117/12.2293781
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Model based image reconstruction often includes regularizers to encourage a priori image information and stabilize the ill-posed inverse problem. Popular edge preserving regularizers often penalize the first order differences of image intensity values. In this work, we propose a hybrid regularizer that additionally penalizes the gradient of an auxiliary variable embedded in the half-quadratic reformulation of some popular edge preserving functions. As the auxiliary variable contain the gradient information, the hybrid regularizer penalizes both the first order and the second order image intensity differences, hence encourages both piecewise constant and piecewise linear image intensity values. Our experimental data using combined physical data acquisition and computer simulations demonstrate the effectiveness of the hybrid regularizer in reducing the stair-casing artifact of the TV penalty, and producing smooth intensity variations.

Citation Download Citation

Jingyan Xu and Frédéric Noo "Model-based image reconstruction with a hybrid regularizer", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057338 (9 March 2018); https://doi.org/10.1117/12.2293781

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