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Abstract #1975

Progressive Volumetrization for Data-Efficient Image Recovery in Accelerated Multi-Contrast MRI

Mahmut Yurt1,2, Muzaffer Ozbey1,2, Salman Ul Hassan Dar1,2, Berk Tinaz1,2,3, Kader Karlı Oğuz2,4, and Tolga Çukur1,2,5
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey, 3Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 4Department of Radiology, Hacettepe University, Ankara, Turkey, 5Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey

The gold-standard recovery models for accelerated multi-contrast MRI either involve volumetric or cross-sectional processing. Volumetric models offer elevated capture of global context, but may yield suboptimal training due to expanded model complexity. Cross-sectional models demonstrate improved training with reduced complexity, yet may suffer from loss of global consistency in the longitudinal dimension. We propose a novel progressively volumetrized generative model (ProvoGAN) for contextual learning of image recovery in accelerated multi-contrast MRI. ProvoGAN empowers capture of global and local context while maintaining lower model complexity by performing aimed volumetric mappings via a cascade of cross-sectional mappings task-optimally ordered across rectilinear orientations.

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