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Abstract #3875

Joint Reconstruction of Multi-Contrast Images: Compressive Sensing Reconstruction using both Joint and Individual Regularization Functions

Emre Kopanoglu1, Alper Gungor1, Toygan Kilic2,3, Emine Ulku Saritas2,3, Tolga Cukur2,3, and H. Emre Guven1

1Aselsan Research Center, Ankara, Turkey, 2Electrical and Electronics Engineering, Bilkent University, Turkey, 3National Magnetic Resonance Research Center (UMRAM), Bilkent University, Turkey

In many clinical settings, multi-contrast images of a patient are acquired to maximize complementary information. With the underlying anatomy being the same, the mutual information in multi-contrast data can be exploited to improve image reconstruction, especially in accelerated acquisition schemes such as Compressive Sensing (CS). This study proposes a CS-reconstruction algorithm that uses four regularization functions; joint L1-sparsity and TV-regularization terms to exploit the mutual information, and individual L1-sparsity and TV-regularization terms to recover unique features in each image. The proposed method is shown to be robust against leakage-of-features across contrasts, and is demonstrated using simulations and in-vivo experiments.

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