  Currently, the deep neural network is the mainstream for machine learning,
and being actively developed for biomedical imaging applications with an
increasing emphasis on tomographic reconstruction for MRI, CT, and other
imaging modalities. Multiple deep-learning-based approaches were applied to MRI
image reconstruction from k-space samples to final images. Each of these
studies assumes a given pulse sequence that produces incomplete and/or
inconsistent data in the Fourier space, and targets a trained neural network
that recovers an underlying image as close as possible to the ground truth. For
the first time, in this paper we view data acquisition and the image
reconstruction as the two key parts of an integrated MRI process, and optimize
both the pulse sequence and the reconstruction scheme seamlessly in the machine
learning framework. Our pilot simulation results show an exemplary embodiment
of our new MRI strategy. Clearly, this work can be extended to other imaging
modalities and their combinations as well, such as ultrasound imaging, and also
potentially simultaneous emission-transmission tomography aided by polarized
radiotracers.
