List-Mode PET Image Reconstruction Using Deep Image Prior
Kibo Ote, Fumio Hashimoto, Yuya Onishi, Takashi Isobe, Yasuomi Ouchi

TL;DR
This paper introduces a novel unsupervised deep image prior method for list-mode PET image reconstruction, effectively improving image quality by integrating CNN techniques with traditional algorithms.
Contribution
It is the first to combine list-mode PET reconstruction with deep image prior, enhancing image sharpness and quantitative accuracy over existing methods.
Findings
Achieved sharper images and better contrast-noise tradeoff.
Outperformed LM-DRAMA, MR-DIP, and sinogram-based DIPRecon in tests.
Potential for improved 4D PET imaging and motion correction.
Abstract
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…
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Taxonomy
TopicsMedical Imaging Techniques and Applications · Advanced MRI Techniques and Applications · Advanced X-ray and CT Imaging
