  Image denoising is of great importance for medical imaging system, since it
can improve image quality for disease diagnosis and downstream image analyses.
In a variety of applications, dynamic imaging techniques are utilized to
capture the time-varying features of the subject, where multiple images are
acquired for the same subject at different time points. Although
signal-to-noise ratio of each time frame is usually limited by the short
acquisition time, the correlation among different time frames can be exploited
to improve denoising results with shared information across time frames. With
the success of neural networks in computer vision, supervised deep learning
methods show prominent performance in single-image denoising, which rely on
large datasets with clean-vs-noisy image pairs. Recently, several
self-supervised deep denoising models have been proposed, achieving promising
results without needing the pairwise ground truth of clean images. In the field
of multi-image denoising, however, very few works have been done on extracting
correlated information from multiple slices for denoising using self-supervised
deep learning methods. In this work, we propose Deformed2Self, an end-to-end
self-supervised deep learning framework for dynamic imaging denoising. It
combines single-image and multi-image denoising to improve image quality and
use a spatial transformer network to model motion between different slices.
Further, it only requires a single noisy image with a few auxiliary
observations at different time frames for training and inference. Evaluations
on phantom and in vivo data with different noise statistics show that our
method has comparable performance to other state-of-the-art unsupervised or
self-supervised denoising methods and outperforms under high noise levels.
