Keywords: Deep learning, image synthesis, proton MRI, functional imaging
TL;DR: We demonstrate that a combination of proton MRI ventilation modeling and multi-inflation proton MRI using a deep learning approach can generate realistic synthetic hyperpolarized gas MRI ventilation scans.
Abstract: Hyperpolarized (HP) gas MRI allows visualization and quantification of regional lung ventilation; however, there is limited clinical uptake due to the requirement for highly specialized equipment and exogenous contrast agents. Alternative, non-contrast, model-based proton ($^1$H)-MRI surrogates of ventilation, which correlate moderately with HP gas MRI, have been proposed. Recently, deep learning (DL)-based methods have been used for the synthesis of HP gas MRI from free-breathing $^1$H-MRI for a single 2D section. Here, we developed and evaluated a multi-channel 3D DL method that combines modeling and data-driven approaches to synthesize HP gas MRI ventilation scans from multi-inflation $^1$H-MRI.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Synthesis
Secondary Subject Area: Transfer Learning and Domain Adaptation
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