Deep-learning-based fast TOF-PET image reconstruction using direction information von Ote, Kibo, Hashimoto, Fumio

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4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network von Hashimoto, Fumio, Ohba, Hiroyuki, Ote, Kibo, Kakimoto, Akihiro, Tsukada, Hideo, Ouchi, Yasuomi

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Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm von Hashimoto, Fumio, Onishi, Yuya, Ote, Kibo, Tashima, Hideaki, Yamaya, Taiga

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Performance evaluation of a high-resolution brain PET scanner using four-layer MPPC DOI detectors von Watanabe, Mitsuo, Saito, Akinori, Isobe, Takashi, Ote, Kibo, Yamada, Ryoko, Moriya, Takahiro, Omura, Tomohide

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Dynamic PET Image Denoising Using Deep Convolutional Neural Networks Without Prior Training Datasets von Hashimoto, Fumio, Ohba, Hiroyuki, Ote, Kibo, Teramoto, Atsushi, Tsukada, Hideo

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Deep learning-based attenuation correction for brain PET with various radiotracers von Hashimoto, Fumio, Ito, Masanori, Ote, Kibo, Isobe, Takashi, Okada, Hiroyuki, Ouchi, Yasuomi

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Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy von Obana, Akira, Ote, Kibo, Gohto, Yuko, Yamada, Hidenao, Hashimoto, Fumio, Okazaki, Shigetoshi, Asaoka, Ryo

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Deep learning-based PET image denoising and reconstruction: a review von Hashimoto, Fumio, Onishi, Yuya, Ote, Kibo, Tashima, Hideaki, Reader, Andrew J., Yamaya, Taiga

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PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model von Hashimoto, Fumio, Ote, Kibo, Onishi, Yuya

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Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents von Tomonari, Yuki, Onishi, Yuya, Hashimoto, Fumio, Ote, Kibo, Okamoto, Takashi, Ohba, Hiroyuki

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Two-step optimization for accelerating deep image prior-based PET image reconstruction von Hashimoto, Fumio, Onishi, Yuya, Ote, Kibo, Tashima, Hideaki, Yamaya, Taiga

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Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising von Onishi, Yuya, Hashimoto, Fumio, Ote, Kibo, Matsubara, Keisuke, Ibaraki, Masanobu

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Correction to: Deep learning-based PET image denoising and reconstruction: a review von Hashimoto, Fumio, Onishi, Yuya, Ote, Kibo, Tashima, Hideaki, Reader, Andrew J., Yamaya, Taiga

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Compressed-Sensing Magnetic Resonance Image Reconstruction Using an Iterative Convolutional Neural Network Approach von Hashimoto, Fumio, Ote, Kibo, Oida, Takenori, Teramoto, Atsushi, Ouchi, Yasuomi

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Direct Annihilation Position Classification Based on Deep Learning Using Paired Cherenkov Detectors: A Monte Carlo Study von Ote, Kibo, Ota, Ryosuke, Hashimoto, Fumio, Hasegawa, Tomoyuki

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ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection von Hashimoto, Fumio, Ote, Kibo

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Unsupervised Dynamic PET Image Denoising with Anatomical Information von HASHIMOTO, Fumio, OHBA, Hiroyuki, OTE, Kibo, TERAMOTO, Atsushi

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Correction for the Influence of Cataract on Macular Pigment Measurement by Autofluorescence Technique Using Deep Learning von Obana, Akira, Ote, Kibo, Hashimoto, Fumio, Asaoka, Ryo, Gohto, Yuko, Okazaki, Shigetoshi, Yamada, Hidenao

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Emphasizing Cherenkov Photons From Bismuth Germanate by Single Photon Response Deconvolution von Ota, Ryosuke, Ote, Kibo

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

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