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Abstract #4413

ASIC Model of SENSE to Accelerate MR Image Reconstruction

Sohaib A Qazi1, Faisal Siddiqui1, J Jacob Wikner2, and Hammad Omer1

1COMSATS Institute of Information Technology, Islamabad, Islamabad, Pakistan, 2Linkoping University, Linkoping, Linkoping, Sweden

In Parallel MRI (pMRI), imaging process is accelerated by acquiring less data using multiple receiver coils and offline reconstruction algorithms (e.g. SENSitivity Encoding (SENSE)) are applied to reconstruct fully sampled data. We present a synthesizable high-description language (HDL) model of SENSE algorithm where the reconstruction can be performed within signal processing chain of MRI scanner. The proposed architecture is tested using simulated human brain data with 8 channel receiver coils and quality of reconstructed images is analyzed using artifact power. The results show that the proposed reconstruction model achieves 0.014 artifact power and is 700 times faster than the CPU based SENSE reconstruction.

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