Evidence map›Paper›PMID 42113440›Full record

ArticlePhysical and engineering sciences in medicine2026

Evaluation of the time‑of‑flight-enhanced deep learning image reconstruction method in

Shohei Fukai, Hiromitsu Daisaki, Ryogo Onda, Manami Shiga, Honoka Yoshida, Kazuki Motegi, Naoki Shimada, Kazuaki Takatsu, Masataka Nakagawa, Takashi Terauchi

Abstract readEvaluation Study
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In one paragraph

Article in Physical and engineering sciences in medicine, 2026. 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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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Shohei FukaiDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan. syohei.fukai@jfcr.or.jp.ORCID http://orcid.org/0000-0002-2321-2984
Hiromitsu DaisakiGraduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamioki-machi, Maebashi, Gunma, 371-0052, Japan.
Ryogo OndaDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Manami ShigaDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Honoka YoshidaDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Kazuki MotegiDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Naoki ShimadaDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Kazuaki TakatsuDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Masataka NakagawaDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Takashi TerauchiDepartment of Nuclear Medicine, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer is the most frequently diagnosed cancer among women. Accurate diagnosis and effective management rely heavily on high-quality positron emission tomography (PET) imaging. A novel time-of-flight (TOF)-enhanced deep learning reconstruction (DLR) technique has recently been introduced for the Omni Legend (GE Healthcare) PET/CT system. However, its clinical utility in breast cancer imaging has not yet been fully established. This study aims to assess the impact of the DLR method on

methodsThis retrospective study included 30 female breast cancer patients who underwent

resultsNoise values for BPL, L-DLR, M-DLR, and H-DLR were 0.08, 0.06, 0.06, and 0.08, respectively (P < 0.001), whereas SBR values were 3.75, 3.85, 4.09, and 4.39, respectively (P < 0.001). Compared with BPL, L-DLR and M-DLR significantly reduced Noise by 33.20% (P < 0.001) and 22.21% (P < 0.001), respectively, whereas M-DLR and H-DLR significantly improved SBR by 8.96% (P < 0.001) and 16.79% (P < 0.001), respectively.

conclusionsThe TOF-enhanced DLR method improves PET image quality metrics compared with the BPL method and has the potential to enhance image quality in

Indexed as

Breast NeoplasmsDeep LearningFluorodeoxyglucose F18Image Processing, Computer-AssistedPositron Emission Tomography Computed TomographyAdultAgedFemaleHumansMiddle AgedRetrospective StudiesSignal-To-Noise RatioTime FactorsFluorodeoxyglucose F18Breast cancerDeep learning reconstructionPET/CTTime‑of‑flight enhancement

Identifiers

PMID42113440

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.