نتائج البحث - "Bitarafan-Rajabi, Ahmad"

المصدر: Taleie , H , Hajianfar , G , Sabouri , M , Parsaee , M , Houshmand , G , Bitarafan-Rajabi , A , Zaidi , H & Shiri , I 2023 , ' Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms ' , JOURNAL OF DIGITAL IMAGING , vol. 36 , no. 6 , pp. 2494-2506 . https://doi.org/10.1007/s10278-023-00891-0Test

مصطلحات موضوعية: Cardiac magnetic resonance imaging, Echocardiography, Machine learning, Radiomics, Thalassemia

وصف الملف: application/pdf

العلاقة: https://research.rug.nl/en/publications/8661e5a1-14ac-4437-a2dc-6600c9b6aed0Test

الإتاحة: https://doi.org/10.1007/s10278-023-00891-0Test
https://hdl.handle.net/11370/8661e5a1-14ac-4437-a2dc-6600c9b6aed0Test
https://research.rug.nl/en/publications/8661e5a1-14ac-4437-a2dc-6600c9b6aed0Test
https://pure.rug.nl/ws/files/878362042/s10278-023-00891-0.pdfTest
http://www.scopus.com/inward/record.url?scp=85171661137&partnerID=8YFLogxKTest

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4

دورية أكاديمية

Artificial intelligence-based analysis of whole-body bone scintigraphy:The quest for the optimal deep learning algorithm and comparison with human observer performance

المؤلفون: Hajianfar, Ghasem, Sabouri, Maziar, Salimi, Yazdan, Amini, Mehdi, Bagheri, Soroush, Jenabi, Elnaz, Hekmat, Sepideh, Maghsudi, Mehdi, Mansouri, Zahra, Khateri, Maziar, Hosein Jamshidi, Mohammad, Jafari, Esmail, Bitarafan Rajabi, Ahmad, Assadi, Majid, Oveisi, Mehrdad, Shiri, Isaac, Zaidi, Habib

المصدر: Hajianfar , G , Sabouri , M , Salimi , Y , Amini , M , Bagheri , S , Jenabi , E , Hekmat , S , Maghsudi , M , Mansouri , Z , Khateri , M , Hosein Jamshidi , M , Jafari , E , Bitarafan Rajabi , A , Assadi , M , Oveisi , M , Shiri , I & Zaidi , H 2023 , ' Artificial intelligence-based analysis of whole-body bone scintigraphy : The quest for the optimal deep learning algorithm and comparison with human observer performance ' , Zeitschrift fur medizinische ....

مصطلحات موضوعية: Artificial intelligence, Bone, Deep learning, Scintigraphy, Whole-body

وصف الملف: application/pdf

العلاقة: https://research.rug.nl/en/publications/86814b52-c08b-4d21-a688-86309bac1851Test

الإتاحة: https://doi.org/10.1016/j.zemedi.2023.01.008Test
https://hdl.handle.net/11370/86814b52-c08b-4d21-a688-86309bac1851Test
https://research.rug.nl/en/publications/86814b52-c08b-4d21-a688-86309bac1851Test
https://pure.rug.nl/ws/files/690345113/1_s2.0_S0939388923000089_main.pdfTest

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5

دورية أكاديمية

Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition

المؤلفون: Sabouri, Maziar, Hajianfar, Ghasem, Hosseini, Zahra, Amini, Mehdi, Mohebi, Mobin, Ghaedian, Tahereh, Madadi, Shabnam, Rastgou, Fereydoon, Oveisi, Mehrdad, Bitarafan Rajabi, Ahmad, Shiri, Isaac, Zaidi, Habib

المصدر: Sabouri , M , Hajianfar , G , Hosseini , Z , Amini , M , Mohebi , M , Ghaedian , T , Madadi , S , Rastgou , F , Oveisi , M , Bitarafan Rajabi , A , Shiri , I & Zaidi , H 2023 , ' Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition ' , JOURNAL OF DIGITAL IMAGING , vol. 36 , no. 2 , pp. 497-509 . https://doi.org/10.1007/s10278-022-00705-9Test

مصطلحات موضوعية: CRT, GSPECT MPI, Machine learning, Quantitative features, Radiomics

وصف الملف: application/pdf

العلاقة: https://research.rug.nl/en/publications/5e379895-cb1a-4897-afea-ff7926f97c2dTest

الإتاحة: https://doi.org/10.1007/s10278-022-00705-9Test
https://hdl.handle.net/11370/5e379895-cb1a-4897-afea-ff7926f97c2dTest
https://research.rug.nl/en/publications/5e379895-cb1a-4897-afea-ff7926f97c2dTest
https://pure.rug.nl/ws/files/257593639/Myocardial_Perfusion_SPECT_Imaging_Radiomic_Features_and_Machine_Learning_Algorithms_for_Cardiac_Contractile_Pattern_Recognition.pdfTest

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6

دورية أكاديمية

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance

المؤلفون: Hajianfar, Ghasem, Sabouri, Maziar, Salimi, Yazdan, Amini, Mehdi, Bagheri, Soroush, Jenabi, Elnaz, Hekmat, Sepideh, Maghsudi, Mehdi, Mansouri, Zahra, Khateri, Maziar, Hosein Jamshidi, Mohammad, Jafari, Esmail, Bitarafan Rajabi, Ahmad, Assadi, Majid, Oveisi, Mehrdad, Shiri Lord, Isaac, Zaidi, Habib

المصدر: ISSN: 0939-3889 ; Zeitschrift für medizinische Physik, (2023.

مصطلحات موضوعية: info:eu-repo/classification/ddc/616.8, info:eu-repo/classification/ddc/616.0757, Artificial intelligence, Bone, Deep learning, Scintigraphy, Whole-body

العلاقة: info:eu-repo/semantics/altIdentifier/pmid/36932023; https://archive-ouverte.unige.ch/unige:169335Test; unige:169335

الإتاحة: https://doi.org/10.1016/j.zemedi.2023.01.008Test
https://archive-ouverte.unige.ch/unige:169335Test

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7

دورية أكاديمية

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance.

المؤلفون: Hajianfar, Ghasem, Sabouri, Maziar, Salimi, Yazdan, Amini, Mehdi, Bagheri, Soroush, Jenabi, Elnaz, Hekmat, Sepideh, Maghsudi, Mehdi, Mansouri, Zahra, Khateri, Maziar, Hosein Jamshidi, Mohammad, Jafari, Esmail, Bitarafan Rajabi, Ahmad, Assadi, Majid, Oveisi, Mehrdad, Shiri, Isaac, Zaidi, Habib

المصدر: Zeitschrift für Medizinische Physik; May2024, Vol. 34 Issue 2, p242-257, 16p

النص الكامل

8

دورية أكاديمية

Role of CT attenuation correction gated SPECT MPI in prediction of pulmonary hypertension

المؤلفون: Yaghoobi, Nahid, Maghsudi, Mehdi, Malek, Hadi, Bakhshandeh, Hooman, Bitarafan-Rajabi, Ahmad, Baay, Mohammadreza

المصدر: Journal of Nuclear Cardiology ; volume 31, page 101777 ; ISSN 1071-3581

مصطلحات موضوعية: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and imaging

الإتاحة: https://doi.org/10.1016/j.nuclcard.2023.101777Test
https://api.elsevier.com/content/article/PII:S1071358123081060?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1071358123081060?httpAccept=text/plainTest

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9

دورية أكاديمية

Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

المؤلفون: Avard, Elham, Shiri, Isaac, Hajianfar, Ghasem, Abdollahi, Hamid, Kalantari, Kiara Rezaei, Houshmand, Golnaz, Kasani, Kianosh, Bitarafan-rajabi, Ahmad, Deevband, Mohammad Reza, Oveisi, Mehrdad, Zaidi, Habib

المصدر: Avard , E , Shiri , I , Hajianfar , G , Abdollahi , H , Kalantari , K R , Houshmand , G , Kasani , K , Bitarafan-rajabi , A , Deevband , M R , Oveisi , M & Zaidi , H 2022 , ' Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection ' , Computers in biology and medicine , vol. 141 , 105145 . https://doi.org/10.1016/j.compbiomed.2021.105145Test

مصطلحات موضوعية: Cine-CMR, Radiomics, Machine learning, Myocardial infarction, LEFT-VENTRICLE, AUTOMATIC SEGMENTATION, TEXTURE ANALYSIS, HEART-DISEASE, MRI, VISUALIZATION