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Abstract #3568

Rapid Multiparametric Quantitative Bilateral Breast MR Fingerprinting Using a Phase-Sensitive Deep Learning Network.

Jessica A. Martinez1, Elizabeth J. Sutton2, Ricardo Otazo1, and Ouri Cohen1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York,, NY, United States, 2Radiology, Memorial Sloan Kettering Cancer Center, New York,, NY, United States

Synopsis

Keywords: MR Fingerprinting, MR Fingerprinting, Quantitative Susceptibility Mapping

Motivation: To rapidly obtain MRF-based multiparametric quantitative maps in the breast.

Goal(s): To explore the feasibility of using PS-DRONE and an EPI-MRF sequence in breast imaging to simultaneously quantify T1, T2, PD, B1, phase, and QSM maps.

Approach: MRF data were acquired at 3T. Tissue parameters were reconstructed using PS-DRONE, including QSM maps computed from the estimated phase.

Results: Bilateral breast T1, T2, PD, B1 and phase (for QSM analysis) maps were obtained using PS-DRONE and an EPI-MRF sequence. Scan time was 2 minutes and 30 seconds. Parameter reconstruction time was one second. Maps presented differences between the two breasts consistent with diffusion images.

Impact: Comprehensive quantitative T1, T2, PD, B1 and phase (QSM) bilateral breast imaging in under 2.5 minutes can potentially improve the detection and characterization of breast cancer and treatment response in a clinical setting without the use of a contrast agent.

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