Arama

EISSN 1305-3612

IMPACT FACTOR: 3.346 5 YEAR IF: 3.132 SCOPUS CITESCORE: 3.9

Diagnostic and Interventional Radiology

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EDITOR'S PICK

Future scenarios and opportunities for interventional radiology in the post COVID-19 era

Maria Abadal, Jose; Gonzalez-Nieto, Jimena; Lopez-Zarraga, Fernando; et al.

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019‎

Ito, Rintaro; Iwano, Shingo; Naganawa, Shinji

Technical background of a novel detector-based approach energy computed tomography‎

Hokamp, Nils Grosse; Maintz, David; Shapira, Nadav; et al.

Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma‎

Zhu, Hong-Bo; Zheng, Ze-Yu; Zhao, Heng; et al.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors‎

Kavur, A. Emre; Gezer, Naciye Sinem; Baris, Mustafa; et al.

Ethical considerations for artificial intelligence: an overview of the current radiology landscape

Tugba Akinci D'Antonoli

HIGHLY CITED ARTICLES

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT

By: Guneyli, S; Atceken, Z; Dogan, H; et al.

Interventional radiology and COVID-19: evidence-based measures to limit transmission

By: Chandy, PE; Nasir, MU; Srinivasan, S; et al.

Technical background of a novel detector-based approach energy computed tomography

By: Hokamp, NG; Maintz, D; Shapira, N; et al.

How imaging should properly be used in COVID-19 outbreak: an Italian experience

By: Sverzellati, N; Milone, F and Balbi, M

Management strategy of novel coronavirus (COVID-19) pneumonia in the radiology department: a Chinese experience

By: An, P; Ye, YJ; Chen, YT; et al.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors

By: Kavur, AE; Gezer, NS; Baris, M; et al.

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019

By: Ito, R; Iwano, S and Naganawa, S

COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging

By: Gezer, NS; Ergan, B; Baris, MM; et al.

CT findings of COVID-19 in follow-up: comparison between progression and recovery

By: Guan, CS; Wei, LG; Xie, RM; et al.

Rare CT feature in a COVID-19 patient: cavitation

By: Xu, ZF; Pan, AZ and Zhou, HL

COVID-19 ARTICLES

COVID-19 radiology CT personnel management

By: Özdemir et al.

Future of IR in the post COVID-19 era

By: Abadal et al.

CT findings to predict hospitalization and intensive care need for COVID-19

By: Tekcan Sanli et al.

Application of percutaneous transluminal sharp recanalization in TIPS

By: Li et al.

Comparison of laboratory-confirmed and clinically diagnosed COVID-19 patients

By: Kuzan et al.

Low-dose chest CT to diagnose COVID-19

By: Aslan et al.

Quantitative chest CT in COVID-19

By: Ufuk et al.

Vascular changes in COVID-19 pneumonia

By: Tekcan Şanlı and Yıldırım.

Use of deep learning in chest X-ray images of COVID-19 patients

By: Blain et al.

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Digital subtraction angiography of a persistent trigeminal artery variant

Osman Temizöz, Hakan Genchellac, Ercüment Ünlü, Bekir Çağlı, Hüseyin Özdemir, M. Kemal Demir

(Diagn Interv Radiol 2010; 16: 245-247) DOI: 10.4261/1305-3825.DIR.2020-08.1

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CT-angiographic demonstration of hepatic collateral pathways due to superior vena cava obstruction in Behçet disease

Osman Temizöz, Hakan Genchellac, Ensar Yekeler, Mustafa Kemal Demir, Ercüment Ünlü, Hüseyin Özdemir

(Diagn Interv Radiol 2010; 16: 302-305) DOI: 10.4261/1305-3825.DIR.2174-08.2

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