  List-mode positron emission tomography (PET) image reconstruction is an
important tool for PET scanners with many lines-of-response and additional
information such as time-of-flight and depth-of-interaction. Deep learning is
one possible solution to enhance the quality of PET image reconstruction.
However, the application of deep learning techniques to list-mode PET image
reconstruction has not been progressed because list data is a sequence of bit
codes and unsuitable for processing by convolutional neural networks (CNN). In
this study, we propose a novel list-mode PET image reconstruction method using
an unsupervised CNN called deep image prior (DIP) which is the first trial to
integrate list-mode PET image reconstruction and CNN. The proposed list-mode
DIP reconstruction (LM-DIPRecon) method alternatively iterates the regularized
list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and
magnetic resonance imaging conditioned DIP (MR-DIP) using an alternating
direction method of multipliers. We evaluated LM-DIPRecon using both simulation
and clinical data, and it achieved sharper images and better tradeoff curves
between contrast and noise than the LM-DRAMA, MR-DIP and sinogram-based
DIPRecon methods. These results indicated that the LM-DIPRecon is useful for
quantitative PET imaging with limited events while keeping accurate raw data
information. In addition, as list data has finer temporal information than
dynamic sinograms, list-mode deep image prior reconstruction is expected to be
useful for 4D PET imaging and motion correction.
