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Abstract #0334

Bidirectional Translation Between Multi-Contrast Images and Multi-Parametric Maps Using Deep Learning

Shihan Qiu1,2, Yuhua Chen1,2, Sen Ma1, Zhaoyang Fan1,2, Anthony G. Christodoulou1,2, Yibin Xie1, and Debiao Li1,2
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Department of Bioengineering, UCLA, Los Angeles, CA, United States

Multi-contrast MRI and multi-parametric maps provide complementary qualitative and quantitative information for disease diagnosis. However, due to limited scan time, a full array of images is often unavailable in practice. To provide both qualitative weighted images and quantitative maps using a weighted-only or mapping-only acquisition, in this work, we propose to perform bidirectional translation between conventional weighted images and parametric maps. We developed a combined training strategy of two convolutional neural networks with cycle consistency loss. Our preliminary results show that the proposed method can translate between contrast-weighted images and quantitative maps with high quality and fidelity.

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