Arama

EISSN 1305-3612

IMPACT FACTOR: 2.63 5 YEAR IF: 2.59

Diagnostic and Interventional Radiology

PDF

HIGHLY CITED ARTICLES

Radiomics with artificial intelligence

Burak Koçak, Emine Şebnem Durmaz, Ece Ateş, Özgür Kılıçkesmez

Artificial intelligence in prostate cancer detection

Stephanie A. Harmon, Sena Tuncer, Thomas Sanford, Peter L. Choyke, Barış Türkbey

Predictors of post liver biopsy bleeding

Mehran Midia, Devang Odedra, Anatoly Shuster, Ramin Midia, Jeffrey Muir

Spectral detector computed tomography

Nils Große Hokamp, David Maintz, Nadav Shapira, De Hua Chang, Peter B Noël

Liver segmentation and volume determination using automatic vs. semi-automatic methods

A. Emre Kavur, Naciye Sinem Gezer, Mustafa Barış, Yusuf Şahin, Savaş Özkan, Bora Baydar, Ulaş Yüksel, Çağlar Kılıkçıer, Şahin Olut, Gözde Bozdağı Akar, Gözde Ünal, Oğuz Dicle, M. Alper Selver

High-intensity focused ultrasound

Nguyen Minh Duc, Bilgin Keserci

Differentiation of cytotoxic and vasogenic edema in a patient with reversible posterior leukoencephalopathy syndrome using diffusion-weighted MRI

By: Doelken, Marc; Lanz, Stefan; Rennert, Janine; et al.

Diagnosis and management of hemoptysis

By: Larici, Anna Rita; Franchi, Paola; Occhipinti, Mariaelena; et al.

Prostate cancer detection with MRI: is dynamic contrast-enhanced imaging necessary in addition to diffusion-weighted imaging?

By: Iwazawa, Jin; Mitani, Takashi; Sassa, Seitaro; et al.

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

IMAGES FROM THE LAST ISSUE

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.

All Title Keyword Full Text DOI

Authors

Article Type

Article Category

Year

Search Results

Total of 2 results

Ranking

Release Date Sort Sort Article Type

Increased x-ray attenuation in malignant vs. benign mediastinal nodes in an orthotopic model of lung cancer

Paul Flechsig, Peter Choyke, Clemens Kratochwil, Arne Warth, Gerald Antoch, Tim Holland-Letz, Daniel Rath, Viktoria Eichwald, Peter E. Huber, Hans-Ulrich Kauczor, Uwe Haberkorn, Frederik L. Giesel

(Diagn Interv Radiol 2016; 22: 35-39) DOI: 10.5152/dir.2015.15220

Abstract Full Text (PDF) Related Articles

Semi-automatic 3D-volumetry of liver metastases from neuroendocrine tumors to improve combination therapy with 177Lu-DOTATOC and 90Y-DOTATOC

Matthaeus Cieciera, Clemens Kratochwil, Jan Moltz, Hans-Ulrich Kauczor, Tim Holland-Letz, Peter Choyke, Walter Mier, Uwe Haberkorn, Frederik L. Giesel

(Diagn Interv Radiol 2016; 22: 201-206) DOI: 10.5152/dir.2015.15304

Abstract Full Text (PDF) Related Articles