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Tytuł:
Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction.
Autorzy:
Ma Y; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Cao H; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Li J; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Lin M; Infervision Technology Co. Ltd., Beijing, 100010, China.
Gong X; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Lin Y; Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China. .
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Źródło:
Scientific reports [Sci Rep] 2024 Feb 07; Vol. 14 (1), pp. 3109. Date of Electronic Publication: 2024 Feb 07.
Typ publikacji:
Journal Article
MeSH Terms:
Radiographic Image Interpretation, Computer-Assisted*/methods
Lung Neoplasms*/diagnostic imaging
Humans ; Retrospective Studies ; Tomography, X-Ray Computed/methods ; Lung ; Radiation Dosage ; Algorithms
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Dynamic Computed Tomography Angiography for capturing vessel wall motion: A phantom study for optimal image reconstruction.
Autorzy:
Stam LB; Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.
Linden SML; Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.; Technical Medical Center, University of Twente, Enschede, The Netherlands.
Oostveen LJ; Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
Hansen HHG; Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
Aquarius R; Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.
Slump CH; Technical Medical Center, University of Twente, Enschede, The Netherlands.
de Korte CL; Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
Bartels RHMA; Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.
Prokop M; Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
Boogaarts HD; Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.
Meijer FJA; Department of Medical Imaging, Radboudumc, Nijmegen, The Netherlands.
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Źródło:
PloS one [PLoS One] 2023 Dec 22; Vol. 18 (12), pp. e0293353. Date of Electronic Publication: 2023 Dec 22 (Print Publication: 2023).
Typ publikacji:
Journal Article
MeSH Terms:
Computed Tomography Angiography*/methods
Radiographic Image Interpretation, Computer-Assisted*/methods
Tomography, X-Ray Computed/methods ; Angiography/methods ; Algorithms ; Image Processing, Computer-Assisted ; Radiation Dosage
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Development of automatic generation system for lung nodule finding descriptions.
Autorzy:
Momoki Y; Medical Systems Research & Development Center, FUJIFILM Corporation, Minato, Tokyo, Japan.
Ichinose A; Medical Systems Research & Development Center, FUJIFILM Corporation, Minato, Tokyo, Japan.
Nakamura K; Medical Systems Research & Development Center, FUJIFILM Corporation, Minato, Tokyo, Japan.
Iwano S; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Kamiya S; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Yamada K; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Naganawa S; Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
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Źródło:
PloS one [PLoS One] 2024 Mar 21; Vol. 19 (3), pp. e0300325. Date of Electronic Publication: 2024 Mar 21 (Print Publication: 2024).
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/diagnostic imaging
Solitary Pulmonary Nodule*/diagnostic imaging
Humans ; Artificial Intelligence ; Tomography, X-Ray Computed/methods ; Lung ; Radiologists ; Radiographic Image Interpretation, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.
Autorzy:
Yoo SJ; Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine, Seoul, Republic of Korea.
Park YS; Department of Internal Medicine, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea.
Choi H; Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea.
Kim DS; Departments of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea.
Goo JM; Department of radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea.
Yoon SH; Department of radiology, Seoul National University Hospital, Seoul National College of Medicine, Seoul, Korea.
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Źródło:
PloS one [PLoS One] 2024 Feb 22; Vol. 19 (2), pp. e0297390. Date of Electronic Publication: 2024 Feb 22 (Print Publication: 2024).
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/diagnostic imaging
Deep Learning*
Humans ; Female ; Middle Aged ; Aged ; Tomography, X-Ray Computed/methods ; Prospective Studies ; Radiographic Image Interpretation, Computer-Assisted/methods ; Radiation Dosage ; Lung/diagnostic imaging ; Image Processing, Computer-Assisted
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Effective lung nodule detection using deep CNN with dual attention mechanisms.
Autorzy:
UrRehman Z; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
Qiang Y; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.; School of Software, North University of China, Taiyuan, China.
Wang L; Jinzhong College of Information, Jinzhong, China.
Shi Y; NHC Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Yang Q; Jinzhong College of Information, Jinzhong, China.
Khattak SU; Centre of Biotechnology and Microbiology, University of Peshawar, Peshawar, 25120, Pakistan.
Aftab R; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China.
Zhao J; College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China. .; Jinzhong College of Information, Jinzhong, China. .
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Źródło:
Scientific reports [Sci Rep] 2024 Feb 16; Vol. 14 (1), pp. 3934. Date of Electronic Publication: 2024 Feb 16.
Typ publikacji:
Journal Article
MeSH Terms:
Solitary Pulmonary Nodule*/diagnostic imaging
Lung Neoplasms*/diagnostic imaging
Humans ; Neural Networks, Computer ; Lung/diagnostic imaging ; Tomography, X-Ray Computed/methods ; Radiographic Image Interpretation, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions.
Autorzy:
Kazimierczak W; Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland. .; Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland. .; University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland. .
Kazimierczak N; Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009, Bydgoszcz, Poland.
Wilamowska J; Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.; University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
Wojtowicz O; Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.; University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
Nowak E; University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
Serafin Z; Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067, Bydgoszcz, Poland.; University Hospital No 1 in Bydgoszcz, Marii Skłodowskiej - Curie 9, 85-094, Bydgoszcz, Poland.
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Źródło:
Scientific reports [Sci Rep] 2024 Feb 15; Vol. 14 (1), pp. 3845. Date of Electronic Publication: 2024 Feb 15.
Typ publikacji:
Journal Article
MeSH Terms:
Endoleak*/diagnostic imaging
Radiography, Dual-Energy Scanned Projection*/methods
Humans ; Radiographic Image Interpretation, Computer-Assisted/methods ; Retrospective Studies ; Tomography, X-Ray Computed/methods ; Signal-To-Noise Ratio
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Comparing x-ray phase-contrast imaging using a Talbot array illuminator to propagation-based imaging for non-homogeneous biomedical samples.
Autorzy:
Riedel M; Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany. .; Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany. .; Institute of Materials Physics, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany. .
Taphorn K; Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany.; Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.
Gustschin A; Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany.; Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.
Busse M; Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany.; Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.
Hammel JU; Institute of Materials Physics, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany.
Moosmann J; Institute of Materials Physics, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany.
Beckmann F; Institute of Materials Physics, Helmholtz-Zentrum Hereon, 21502, Geesthacht, Germany.
Fischer F; Institute of Forensic Medicine, Ludwig-Maximilians Universitaet, Munich, Germany.
Thibault P; Department of Physics, University of Trieste, Trieste, Italy.
Herzen J; Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany.; Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.
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Źródło:
Scientific reports [Sci Rep] 2023 Apr 28; Vol. 13 (1), pp. 6996. Date of Electronic Publication: 2023 Apr 28.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Tomography, X-Ray Computed*/methods
Radiographic Image Interpretation, Computer-Assisted*/methods
X-Rays ; Microscopy, Phase-Contrast
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Influence of Pitch on Surface Dose Distribution and Image Noise of Computed Tomography Scans.
Autorzy:
Yamada K; Division of Clinical Technology, Tokushima University Hospital, Tokushima 7708503, Japan.
Kawata Y; Institute of Post-LED Photonics, Tokushima University, Tokushima 7708506, Japan.
Amano M; Division of Clinical Technology, Tokushima University Hospital, Tokushima 7708503, Japan.
Suzuki H; Institute of Post-LED Photonics, Tokushima University, Tokushima 7708506, Japan.
Tominaga M; Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 7708503, Japan.
Sasaki M; Department of Therapeutic Radiology, Graduate School of Biomedical Sciences, Tokushima University, Tokushima 7708503, Japan.
Nishiyama H; Department of Radiological Technology, Ehime University Hospital, Toon 7910295, Japan.
Harada M; Department of Radiology and Radiation Oncology, Tokushima University, Tokushima 7708503, Japan.
Niki N; Faculty of Science and Technology, Tokushima University, Tokushima 7708506, Japan.
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Źródło:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Mar 26; Vol. 23 (7). Date of Electronic Publication: 2023 Mar 26.
Typ publikacji:
Journal Article
MeSH Terms:
Radiographic Image Interpretation, Computer-Assisted*/methods
Tomography, X-Ray Computed*/methods
Prospective Studies ; Phantoms, Imaging ; Radiation Dosage ; Algorithms ; Image Processing, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension.
Autorzy:
Mamalakis M; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK. .; Department of Computer Science, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK. .; Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK. .
Dwivedi K; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.; Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK.
Sharkey M; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.
Alabed S; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.; Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK.
Kiely D; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK.; Department of Cardiology, University of Sheffield, Sheffield Teaching Hospitals Sheffield, Sheffield, S5 7AU, UK.
Swift AJ; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Beech Hill Rd, Sheffield, S10 2RX, UK. .; Insigneo Institute for in silico Medicine, University of Sheffield, The Pam Liversidge Building, Sheffield, S1 3JD, UK. .
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Źródło:
Scientific reports [Sci Rep] 2023 Mar 07; Vol. 13 (1), pp. 3812. Date of Electronic Publication: 2023 Mar 07.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Hypertension, Pulmonary*/diagnostic imaging
Hypertension, Pulmonary*/etiology
Radiographic Image Interpretation, Computer-Assisted*
Lung Diseases*/complications
Lung Diseases*/diagnosis
Humans ; Artificial Intelligence ; Models, Anatomic ; Software ; Tomography, X-Ray Computed/methods ; Lung/diagnostic imaging
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.
Autorzy:
Shi Y; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Li J; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Li K; Xi'an Key Laboratory for Prevention and Treatment of Common Aging Diseases, Translational and Research Centre for Prevention and Therapy of Chronic Disease, Institute of Basic and Translational Medicine, Xi'an Medical University, Xi'an, China.
Zhang X; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Chang P; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Huang Z; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Liu Y; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Wang Y; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Zhan Y; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Cao X; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
Zhu S; School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China.; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, China.
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Źródło:
Journal of X-ray science and technology [J Xray Sci Technol] 2023; Vol. 31 (5), pp. 1047-1066.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Heart*/diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted*/methods
Mice ; Animals ; X-Ray Microtomography/methods ; Prospective Studies ; Phantoms, Imaging ; Artifacts
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Application of double low-dose mode in left atrial-pulmonary venous computed tomography angiography.
Autorzy:
Zhang C; Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Zhang W; Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Shi K; Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China. .
Chen J; Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China. .
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Źródło:
Scientific reports [Sci Rep] 2023 Dec 07; Vol. 13 (1), pp. 21563. Date of Electronic Publication: 2023 Dec 07.
Typ publikacji:
Journal Article
MeSH Terms:
Computed Tomography Angiography*/methods
Iodine*
Humans ; Tomography, X-Ray Computed ; Veins ; Radiation Dosage ; Algorithms ; Radiographic Image Interpretation, Computer-Assisted/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions.
Autorzy:
Chun SH; Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Suh YJ; Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea. .
Han K; Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Kwon Y; Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea.
Kim AY; Weill Cornell Medicine-Qatar, Doha, Qatar.
Choi BW; Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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Źródło:
Scientific reports [Sci Rep] 2022 Sep 07; Vol. 12 (1), pp. 15171. Date of Electronic Publication: 2022 Sep 07.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Deep Learning*
Radiographic Image Interpretation, Computer-Assisted*/methods
Humans ; Retrospective Studies ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Low-dose abdominopelvic computed tomography in patients with lymphoma: An image quality and radiation dose reduction study.
Autorzy:
Yoon S; Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
Yoo KH; Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Park SH; Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.
Kim H; Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Lee JH; Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Park J; Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea.
Park SH; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Kim HJ; Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
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Źródło:
PloS one [PLoS One] 2022 Aug 11; Vol. 17 (8), pp. e0272356. Date of Electronic Publication: 2022 Aug 11 (Print Publication: 2022).
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Lymphoma*/diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted*/methods
Drug Tapering ; Humans ; Radiation Dosage ; Retrospective Studies ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.
Autorzy:
Miyata T; Department of Future Diagnastic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka Suita-City, Osaka, 565-0871, Japan.
Yanagawa M; Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan. .
Kikuchi N; Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan.
Yamagata K; Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan.
Sato Y; Department of Radiology, Suita Municipal Hospital, 5-7 Kishibeshinmati, Suita-city, Osaka, 564-8567, Japan.
Yoshida Y; Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan.
Tsubamoto M; Department of Radiology, Nishinomiya Municipal Central Hospital, 8-24 Hayashidacho, Nishinomiya City, Hyogo, 663-8014, Japan.
Tomiyama N; Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-city, Osaka, 565-0871, Japan.
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Źródło:
Scientific reports [Sci Rep] 2022 Jul 20; Vol. 12 (1), pp. 12422. Date of Electronic Publication: 2022 Jul 20.
Typ publikacji:
Journal Article
MeSH Terms:
Deep Learning*
Radiographic Image Interpretation, Computer-Assisted*/methods
Algorithms ; Cadaver ; Humans ; Lung/diagnostic imaging ; Radiation Dosage ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.
Autorzy:
Chun M; Department of Radiation Oncology, Chung-Ang University Gwang Myeong Hospital, Gyeonggi-do, Republic of Korea.; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
Choi JH; Department of Radiation Oncology, Chung-Ang University College of Medicine, Seoul, Republic of Korea.
Kim S; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
Ahn C; Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.; ClariPi Research, Seoul, Republic of Korea.
Kim JH; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.; Department of Transdisciplinary Studies, Program in Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.; ClariPi Research, Seoul, Republic of Korea.; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.; Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, Republic of Korea.
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Źródło:
PloS one [PLoS One] 2022 Jul 20; Vol. 17 (7), pp. e0271724. Date of Electronic Publication: 2022 Jul 20 (Print Publication: 2022).
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
Radiographic Image Interpretation, Computer-Assisted*/methods
Tomography, X-Ray Computed*/methods
Algorithms ; Humans ; Radiation Dosage
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Cascaded-Recalibrated Multiple Instance Deep Model for Pathologic-Level Lung Cancer Prediction in CT Images.
Autorzy:
Wang Q; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Zhou Y; Department of Radiology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China.; NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital), Mianyang, China.
Huang J; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Liu Z; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Zhang W; School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Liu Q; Department of Radiology, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China.; NHC Key Laboratory of Nuclear Technology Medical Transformation (Mianyang Central Hospital), Mianyang, China.
Cheng JZ; Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
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Źródło:
Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 13; Vol. 2022, pp. 9469234. Date of Electronic Publication: 2022 Jun 13 (Print Publication: 2022).
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted*/methods
Databases, Factual ; Humans ; Lung ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Finding the optimal tube current and iterative reconstruction strength in liver imaging; two needles in one haystack.
Autorzy:
Martens B; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
Bosschee JGA; Maastricht University, Maastricht, The Netherlands.
Van Kuijk SMJ; Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands.
Jeukens CRLPN; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Brauer MTH; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Wildberger JE; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
Mihl C; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
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Źródło:
PloS one [PLoS One] 2022 Apr 07; Vol. 17 (4), pp. e0266194. Date of Electronic Publication: 2022 Apr 07 (Print Publication: 2022).
Typ publikacji:
Journal Article; Validation Study; Research Support, Non-U.S. Gov't
MeSH Terms:
Radiographic Image Interpretation, Computer-Assisted*/instrumentation
Radiographic Image Interpretation, Computer-Assisted*/methods
Liver/*diagnostic imaging
Phantoms, Imaging/*classification
Algorithms ; Humans ; Needles ; Phantoms, Imaging/standards ; Radiation Dosage ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.
Autorzy:
Miyazaki A; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Ikejima K; Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan.
Nishio M; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan. .
Yabuta M; Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan.
Matsuo H; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Onoue K; Department of Radiology, Kobe City Medical Center General Hospital, 2-1-1 Minatojimaminamimachi, Chuo-Ku, Kobe, 650-0047, Japan.; Department of Diagnostic Imaging and Interventional Radiology, Kyoto Katsura Hospital, 17 Yamada-Hirao, Nishikyo-Ku, Kyoto, 615-8256, Japan.
Matsunaga T; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Nishioka E; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Kono A; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
Yamada D; Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan.
Oba K; Department of Radiology, St. Luke's International Hospital, 9-1 Akashi-Cho, Chuo-Ku, Tokyo, 104-8560, Japan.
Ishikura R; Department of Radiology, Kobe City Medical Center General Hospital, 2-1-1 Minatojimaminamimachi, Chuo-Ku, Kobe, 650-0047, Japan.
Murakami T; Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-Cho, Chuo-Ku, Kobe, 650-0017, Japan.
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Źródło:
Scientific reports [Sci Rep] 2023 Oct 16; Vol. 13 (1), pp. 17533. Date of Electronic Publication: 2023 Oct 16.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:
COVID-19*/diagnostic imaging
Deep Learning*
Pneumonia*/diagnostic imaging
Humans ; COVID-19 Testing ; X-Rays ; Tomography, X-Ray Computed/methods ; Radiographic Image Interpretation, Computer-Assisted/methods ; Radiologists ; Computers ; Retrospective Studies
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
Tytuł:
Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.
Autorzy:
Zhang R; Department of Medical Physics, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53705, USA.
Griner D; Department of Medical Physics, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53705, USA.
Garrett JW; Department of Medical Physics, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53705, USA.; Department of Radiology, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53792, USA.
Qi Z; Department of Radiology, Henry Ford Health, Detroit, MI, 48202, USA.
Chen GH; Department of Medical Physics, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53705, USA. .; Department of Radiology, School of Medicine and Public Health, The University of Wisconsin in Madison, Madison, WI, 53792, USA. .
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Źródło:
Scientific reports [Sci Rep] 2023 Aug 04; Vol. 13 (1), pp. 12690. Date of Electronic Publication: 2023 Aug 04.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't; Research Support, N.I.H., Extramural
MeSH Terms:
COVID-19*
Deep Learning*
Humans ; Radiography, Thoracic/methods ; X-Rays ; Radiographic Image Interpretation, Computer-Assisted/methods
Czasopismo naukowe
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Tytuł:
Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net.
Autorzy:
Zhang N; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Lin J; Department of Dermatology, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Hui B; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Qiao B; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Yang W; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Shang R; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Wang X; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
Lei J; Department of Thoracic Surgery, Tangdu Hospital, Air Force Military Medical University, Xi'an, China.
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Źródło:
Computational and mathematical methods in medicine [Comput Math Methods Med] 2022 Mar 23; Vol. 2022, pp. 5112867. Date of Electronic Publication: 2022 Mar 23 (Print Publication: 2022).
Typ publikacji:
Journal Article
MeSH Terms:
Lung Neoplasms*/diagnostic imaging
Lung Neoplasms*/pathology
Radiographic Image Interpretation, Computer-Assisted*/methods
Algorithms ; Humans ; Lung/diagnostic imaging ; Lung/pathology ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
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