K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets

Understand

Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle.

• This is significant in high-dimensional (dynamic/volumetric) magnetic resonance imaging (MRI), where acquisition of high-resolution fully sampled k-space data is impractical.
• We introduce a novel mathematical framework, dubbed k-band, that enables training DL models using only partial, limited-resolution k-space data.
• Specifically, we introduce training with stochastic gradient descent (SGD) over k-space subsets.