no code implementations • 1 Mar 2024 • Kibo Ote, Fumio Hashimoto, Yuya Onishi, Yasuomi Ouchi
Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task.
no code implementations • 5 Dec 2023 • Fumio Hashimoto, Kibo Ote
Despite limited training on simulated data, the proposed ReconU-Net successfully reconstructed the real Hoffman brain phantom, unlike other deep learning-based direct reconstruction methods, which failed to produce a reconstructed image.
no code implementations • 27 Feb 2023 • Yuya Onishi, Fumio Hashimoto, Kibo Ote, Keisuke Matsubara, Masanobu Ibaraki
Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance.
no code implementations • 22 Dec 2022 • Fumio Hashimoto, Yuya Onishi, Kibo Ote, Hideaki Tashima, Taiga Yamaya
The simulation results showed that the proposed method improved the PET image quality by reducing statistical noise and preserved a contrast of brain structures and inserted tumor compared with other algorithms.
no code implementations • 28 Apr 2022 • Kibo Ote, Fumio Hashimoto, Yuya Onishi, Takashi Isobe, Yasuomi Ouchi
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).
no code implementations • 2 Sep 2021 • Yuya Onishi, Fumio Hashimoto, Kibo Ote, Hiroyuki Ohba, Ryosuke Ota, Etsuji Yoshikawa, Yasuomi Ouchi
Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs.
no code implementations • 2 Sep 2021 • Fumio Hashimoto, Kibo Ote
Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction.