Abstract #1975

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

Mahmut Yurt^1,2, Muzaffer Ozbey^1,2, Salman Ul Hassan Dar^1,2, Berk Tinaz^1,2,3, Kader Karlı Oğuz^2,4, and Tolga Çukur^1,2,5

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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