Tomoki Imokawa
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Explore the profile of Tomoki Imokawa including associated specialties, affiliations and a list of published articles.
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Articles
7
Citations
39
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Recent Articles
1.
Yokoyama K, Matsuki M, Isozaki T, Ito K, Imokawa T, Ozawa A, et al.
Jpn J Radiol
. 2025 Jan;
PMID: 39794659
Adrenal diseases pose significant diagnostic challenges due to the wide range of neoplastic and non-neoplastic pathologies. Radiologists have a crucial role in diagnosing and managing these conditions by, leveraging advanced...
2.
Imokawa T, Yokoyama K, Takahashi K, Oyama J, Tsuchiya J, Sanjo N, et al.
Jpn J Radiol
. 2024 Jun;
42(11):1215-1230.
PMID: 38888851
The findings of brain perfusion single-photon emission computed tomography (SPECT), which detects abnormalities often before changes manifest in morphological imaging, mainly reflect neurodegeneration and contribute to dementia evaluation. A major...
3.
Imokawa T, Satoh Y, Fujioka T, Takahashi K, Mori M, Kubota K, et al.
Breast Cancer
. 2023 Aug;
PMID: 37634221
Background: Dedicated breast positron emission tomography (dbPET) has high contrast and resolution optimized for detecting small breast cancers, leading to its noisy characteristics. This study evaluated the application of deep...
4.
Fujioka T, Satoh Y, Imokawa T, Mori M, Yamaga E, Takahashi K, et al.
Diagnostics (Basel)
. 2022 Dec;
12(12).
PMID: 36553120
This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full-...
5.
Ozaki J, Fujioka T, Yamaga E, Hayashi A, Kujiraoka Y, Imokawa T, et al.
Jpn J Radiol
. 2022 Mar;
40(8):814-822.
PMID: 35284996
Purpose: To investigate the ability of deep learning (DL) using convolutional neural networks (CNNs) for distinguishing between normal and metastatic axillary lymph nodes on ultrasound images by comparing the diagnostic...
6.
Satoh Y, Imokawa T, Fujioka T, Mori M, Yamaga E, Takahashi K, et al.
Ann Nucl Med
. 2022 Jan;
36(4):401-410.
PMID: 35084712
Objective: This study aimed to investigate and determine the best deep learning (DL) model to predict breast cancer (BC) with dedicated breast positron emission tomography (dbPET) images. Methods: Of the...
7.
Takahashi K, Fujioka T, Oyama J, Mori M, Yamaga E, Yashima Y, et al.
Tomography
. 2022 Jan;
8(1):131-141.
PMID: 35076612
Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has...
8.
Imokawa T, Ito K, Takemura N, Inagaki F, Mihara F, Kokudo N
Pancreas
. 2021 Oct;
50(7):1037-1041.
PMID: 34643610
Xanthogranulomatous pancreatitis (XGP) is extremely rare, with only 31 cases reported in the English literature to date. We reviewed previously reported 17 articles about XGP and report an additional case...