  Objective: Acquiring fully sampled training data is challenging for many MRI
applications. We present a self-supervised image reconstruction method, termed
ReSiDe, capable of recovering images solely from undersampled data.
  Materials and Methods: ReSiDe is inspired by plug-and-play (PnP) methods, but
unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe
iteratively trains the denoiser on the image or images that are being
reconstructed. We introduce two variations of our method: ReSiDe-S and
ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled
measurements, while ReSiDe-M operates on multiple sets of undersampled
measurements and provides faster inference. Studies I, II, and III compare
ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods
using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion
phantom, and first-pass cardiac perfusion, respectively.
  Results: ReSiDe-S and ReSiDe-M outperform other methods in terms of
reconstruction signal-to-noise ratio and structural similarity index measure
for Studies I and II, and in terms of expert scoring for Study III.
  Discussion: We present a self-supervised image reconstruction method and
validate it in both static and dynamic MRI applications. These developments can
benefit MRI applications where the availability of fully sampled training data
is limited.
