Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Fumio Hashimoto, Yuya Onishi, Kibo Ote, Hideaki Tashima, Taiga Yamaya

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Article in Radiological physics and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Fumio HashimotoCentral Research Laboratory, Hamamatsu Photonics K. K, 5000 Hirakuchi, Hamana-Ku, Hamamatsu, 434-8601, Japan. fumio.hashimoto@crl.hpk.co.jp.ORCID http://orcid.org/0000-0003-2352-0538

Yuya OnishiCentral Research Laboratory, Hamamatsu Photonics K. K, 5000 Hirakuchi, Hamana-Ku, Hamamatsu, 434-8601, Japan.

Kibo OteCentral Research Laboratory, Hamamatsu Photonics K. K, 5000 Hirakuchi, Hamana-Ku, Hamamatsu, 434-8601, Japan.

Hideaki TashimaNational Institutes for Quantum Science and Technology, 4-9-1, Anagawa,Inage-Ku, Chiba, 263-8555, Japan.

Taiga YamayaGraduate School of Science and Engineering, Chiba University, 1-33, Yayoicho,Inage-Ku, Chiba, 263-8522, Japan.

Funding

JSPS JP22K07762

6 · The paper itself

Abstract

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.

Indexed as

Deep LearningImage Processing, Computer-AssistedMonte Carlo MethodPositron-Emission TomographyHumansPhantoms, Imaging3D PET image reconstructionDeep image priorEnd-to-end reconstructionPositron emission tomography (PET)

Identifiers

PMID39096446

DOI10.1007/s12194-024-00831-9

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