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Abstract #0177

Rapid 3D Quantitative Mapping of Brain Metastases with Deep Learning-Based Phase-Sensitive MR fingerprinting

Victoria Y Yu1, Kathryn R Tringale2, Ricardo Otazo1, and Ouri Cohen1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States


In MR fingerprinting, quantitative maps are obtained by matching the measured signal to a pre-computed dictionary. However, a key constraint of dictionary matching is the exponential growth of the dictionary with the number of parameters. A deep learning method named DRONE overcomes this constraint by using deep learning to map the magnitude-valued signal to the underlying tissue parameters. Here we describe an extension of DRONE that jointly estimates a phase term to enable mapping complex-valued signals and improve the quantitative accuracy. We test the accuracy in the ISMRM NIST phantom and demonstrate the clinical utility in patients with brain metastases.

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