Real-time algorithm for generating color Doppler ultrasound images on commercially available microprocessors

1997 ◽  
Author(s):  
Chris Basoglu ◽  
Yongmin Kim
Keyword(s):  
Real Time ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Time Algorithm ◽  
Color Doppler Ultrasound ◽  
Ultrasound Images

scholarly journals Color doppler ultrasound images and their types in liver tumors.

Kanzo ◽  
1989 ◽  
Vol 30 (11) ◽  
pp. 1637-1638 ◽  
Author(s):  
Yousuke ARITA ◽  
Kazuaki YASUHARA ◽  
Jyunji FURUSE ◽  
Shoichi MATSUTANI ◽  
Masaaki EBARA ◽  
...  
Keyword(s):  
Doppler Ultrasound ◽  
Liver Tumors ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Ultrasound Images

scholarly journals Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

Prenatal diagnosis of thrombosis of the dural sinuses with real-time and color Doppler ultrasound

2001 ◽  
Vol 17 (4) ◽  
pp. 322-325 ◽  
Author(s):  
A. Visentin ◽  
P. Falco ◽  
G. Pilu ◽  
A. Perolo ◽  
B. Valeri ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
Real Time ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Dural Sinuses
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