Samsung Digital Radiography systems employ various image processing and advanced applications that help improve diagnostic confidence, operator workflow, and patient dose. Clinical research has been conducted to validate the efficacy of these technologies. Learn more below:
Dose optimization for pediatric radiographic exams using S-VueTM>
Pediatric Radiography Using Reduced Radiation Dose in Neonatal Intensive Care Unit>
Reducing X-ray dose to improve pediatric patient care with S-VueTM>
Advanced Stitching with Camera View in Samsung GC85A>
Using Samsung Bone Suppression in Clinical Routine>
Usefulness of Samsung's S-Enhance Post-Processed Digital Kidney Ureter Bladder for Detection of Ureteral Stones>
Workflow Improvement with Samsung SimGrid Software>
Improved non-grid image quality by utilizing simulated grid (SimGridTM) in obese patient chest X-ray>
Chest Nodule Detection Using Samsung Bone Suppression Software>
Development of Quality-Controlled Low-Dose Protocols for Radiography in the Neonatal ICU Using a New Mobile Digital Radiography System
Gayoung Choi, Jung-Eun Cheon, Seunghyun Lee, Young Hun Choi, Seung Han Shin, Yeon Jin Cho, Sun Won Park. American Journal of Roentgenology 0 0:0, 1-
Published: Pre-Print. Read on American Journal of Roentgenology>
Abdominal Digital Radiography with a Novel Post-Processing Technique: Phantom and Patient Studies
Kang H, Lee ES, Park HJ, Park BK, Park JY, Suh SW. J Korean Soc Radiol. 2020 Jan;81:e31.
Published: May 08, 2020. Read on Journal of the Korean Society of Radiology>
Application of an advanced noise reduction algorithm for imaging of hands in rheumatic diseases: evaluation of image quality compared to standard-dose images
Ziegeler, K., Siepmann, S., Hermann, S. et al. Rheumatol Int 40, 893–899 (2020).
Published: April 02, 2020. Read on Rheumatology International>
Radiation dose reduction and improvement of image quality in digital chest radiography by new spatial noise reduction algorithm
Lee W, Lee S, Chong S, Lee K, Lee J, Choi, JC, et al. (2020). PLoS ONE 15(2): e0228609
Published: February 21, 2020. Read on PLOS ONE>
Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs
Yongsik Sim, Myung Jin Chung, Elmar Kotter, Sehyo Yune, Myeongchan Kim, Synho Do, Kyunghwa Han, Hanmyoung Kim, Seungwook Yang, Dong-Jae Lee, Byoung Wook Choi. Radiology; Vol. 294, No. 1
Published: January 2020. Read on Radiology>
Added Value of Bone Suppression Image in the Detection of Subtle Lung Lesions on Chest Radiographs with Regard to Reader's Expertise
Hong GS, Do KH, Lee CW. J Korean Med Sci. 2019 Oct;34(38:e250.
Published: September 16, 2019. Read on Journal of Korean Medical Science>
The Potential Role of Grid-Like Software in Bedside Chest Radiography in Improving Image Quality and Dose Reduction: An Observer Preference Study
Ahn SY, Chae KJ, Goo JM. Korean J Radiol. 2018 May-June;19(3):526-533.
Published: April 06, 2018. Read on Korean Journal of Radiology>