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Abstract #3084

Dual Domain Deep Learning Framework for Cardiac MR Image Reconstruction

Faisal Najeeb1, Madiha Arshad 1, Muhammad Shafique1, and Hammad Omer1
1Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan

Synopsis

Keywords: Image Reconstruction, Artifacts, Deep Learning, Compressed Sensing , Parallel MRIMost deep learning methods apply U-Net either in image or k-space domain. Nevertheless, these methods have limitations: (1) Directly applying U-Net in k-space domain is not optimal for extracting features; (2) conventional image-domain oriented U-Net does not fully utilize the information of encoder part of the network for extracting features in the decoder part. In this paper, a dual-domain deep learning-based approach is presented, incorporating multi-coil data consistency layers for the reconstruction of cardiac MR images from 1-D Variable Density (VD) under-sampled data. Experiments show superior reconstruction results of the proposed method than conventional Compressed Sensing (CS) method.

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Keywords