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Magnetic resonance imaging (MRI) offers the flexibility to image a givenanatomic volume under a multitude of tissue contrasts. Yet, scan timeconsiderations put stringent limits on the quality and diversity of MRI data.The gold-standard approach to alleviate this limitation is to recoverhigh-quality images from data undersampled across various dimensions, mostcommonly the Fourier domain or contrast sets. A primary distinction amongrecovery methods is whether the anatomy is processed per volume or percross-section. Volumetric models offer enhanced capture of global contextualinformation, but they can suffer from suboptimal learning due to elevated modelcomplexity. Cross-sectional models with lower complexity offer improvedlearning behavior, yet they ignore contextual information across thelongitudinal dimension of the volume. Here, we introduce a novel progressivevolumetrization strategy for generative models (ProvoGAN) that seriallydecomposes complex volumetric image recovery tasks into successivecross-sectional mappings task-optimally ordered across individual rectilineardimensions. ProvoGAN effectively captures global context and recoversfine-structural details across all dimensions, while maintaining low modelcomplexity and improved learning behaviour. Comprehensive demonstrations onmainstream MRI reconstruction and synthesis tasks show that ProvoGAN yieldssuperior performance to state-of-the-art volumetric and cross-sectional models.

Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery