Search Results - "Tsujimoto, Masakazu"

Correction: Differentiation and correlation of regional uptake heterogeneity with cardiac dysfunction in biopsy-proven transthyretin amyloid cardiomyopathy using quantitative single-photon emission computed tomography/computed tomography: a single-center, cross-sectional study

Authors: Tsujimoto, Masakazu Kawai, Hideki Tanahashi, Shingo et al.

Source: EJNMMI Research. 3/19/2026, Vol. 16 Issue 1, p1-1. 1p.

Optimization of acquisition time and reconstruction parameters for quantitative single-photon emission computed tomography/computed tomography using iodine-123 metaiodobenzylguanidine.

Authors: Tsujimoto, Masakazu Watanabe, Masanori Nogami, Kenta et al.

Source: PLoS ONE. 9/30/2025, Vol. 20 Issue 9, p1-14. 14p.

Differentiation and correlation of regional uptake heterogeneity with cardiac dysfunction in biopsy-proven transthyretin amyloid cardiomyopathy using quantitative single-photon emission computed tomography/computed tomography: a single-center, cross-sectional study

Authors: Tsujimoto, Masakazu Kawai, Hideki Tanahashi, Shingo et al.

Source: EJNMMI Research. 8/1/2025, Vol. 15 Issue 1, p1-14. 14p.

Association of 18F-fluorodeoxyglucose uptake with cardiac events in cardiac sarcoidosis during outpatient follow-up after immunosuppression.

Authors: Kawai, Hideki Sarai, Masayoshi Kato, Yasuchika et al.

Source: PLoS ONE. 5/14/2026, Vol. 21 Issue 5, p1-15. 15p.

Harmonization of Quantitative Values in Bone SPECT/CT for Medication-Related Osteonecrosis of the Jaw.

Authors: Kuromori, Issei Daisaki, Hiromitsu Owaki, Yoshiki et al.

Source: Journal of Nuclear Medicine Technology. Dec2025, Vol. 53 Issue 4, p345-352. 8p.

Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks.

Authors: Onishi, Yuya Teramoto, Atsushi Tsujimoto, Masakazu et al.

Source: BioMed Research International. 1/2/2019, p1-9. 9p.

Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks.

Authors: Onishi Y Teramoto A Tsujimoto M et al.

Source: International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2020 Jan; Vol. 15 (1), pp. 173-178. Date of Electronic Publication: 2019 Nov 16.

Publication Type: Journal Article

Journal Info: Publisher: Springer Country of Publication: Germany NLM ID: 101499225 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1861-6429 (Electronic) Linking ISSN: 18616410 NLM ISO Abbreviation: Int J Comput Assist Radiol Surg Subsets: MEDLINE