  Recently deep neural networks have been widely and successfully applied in
computer vision tasks and attracted growing interests in medical imaging. One
barrier for the application of deep neural networks to medical imaging is the
need of large amounts of prior training pairs, which is not always feasible in
clinical practice. In this work we propose a personalized representation
learning framework where no prior training pairs are needed, but only the
patient's own prior images. The representation is expressed using a deep neural
network with the patient's prior images as network input. We then applied this
novel image representation to inverse problems in medical imaging in which the
original inverse problem was formulated as a constraint optimization problem
and solved using the alternating direction method of multipliers (ADMM)
algorithm. Anatomically guided brain positron emission tomography (PET) image
reconstruction and image denoising were employed as examples to demonstrate the
effectiveness of the proposed framework. Quantification results based on
simulation and real datasets show that the proposed personalized representation
framework outperform other widely adopted methods.
