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Abstract #2122

Accelerated High-Resolution T1- and T2-weighted Breast MRI with Deep Learning Super-Resolution Reconstruction

Narine Mesropyan1, Christoph Katemann2, Alexander Isaak 1, Oliver M. Weber 2, Johannes M. Peeters3, Asadeh Lakghomi 4, and Julian A. Luetkens 4
1Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany, 2Philips Healthcare, Hamburg, Germany, 3Philips Healthcare, Philips Healthcare, Best, Netherlands, 4University Hospital Bonn, Bonn, Germany

Synopsis

Keywords: Breast, Breast, Breast MRI, Cancer, Deep Learning

Motivation: High spatial and temporal resolutions are required for breast MRI.

Goal(s): This study aimed to assess the performance of a deep-learning(DL) algorithm to reconstruct low-resolution Cartesian T1-weighted DCE(T1w) and T2-weighted TSE(T2w) sequences.

Approach: In this prospective study, patients underwent 1.5T breast MRI. The study protocol included T1w and T2w, acquired in standard resolution (T1S,T2S) and in low-resolution with following DL reconstructions (T1DL,T2DL). For DL reconstruction, two CNNs were used: (1)Adaptive-CS-Net and (2)Precise-Image-Net. Image quality was analysed qualitatively and quantitatively. BI-RADS agreement between sequences was assessed.

Results: Deep-learning for denoising and resolution upscaling reduces acquisition time and improves image quality for breast MRI.

Impact: Deep learning reconstruction algorithm for denoising with compressed sensing and resolution upscaling reduces acquisition time and improves image quality for dynamic contrast-enhanced T1w and T2w breast MRI.

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Keywords