scholarly journals Analysis of Coronary Angiography Video Interpolation Methods to Reduce X-ray Exposure Frequency Based on Deep Learning

2021 ◽  
Author(s):  
Dong-xue Liang
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Heart Surgery ◽  
Frame Rate ◽  
Video Frame ◽  
X Rays ◽  
Frame Interpolation ◽  
X Ray ◽  
Video Frame Rate

Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery. Under X-ray irradiation, the physician injects a contrast agent through a catheter and determines the coronary arteries’ state in real time. However, to obtain a more accurate state of the coronary arteries, physicians need to increase the frequency and intensity of X-ray exposure, which will inevitably increase the potential for harm to both the patient and the surgeon. In the work reported here, we use advanced deep learning algorithms to find a method of frame interpolation for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video, thereby reducing X-ray-induced damage to physicians. We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos. Each group includesthree consecutive images, which are used to train the video interpolation network model. We apply six popular frameinterpolation methods to this dataset to confirm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.

scholarly journals Deep learning segmentation of major vessels in X-ray coronary angiography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Su Yang ◽  
Jihoon Kweon ◽  
Jae-Hyung Roh ◽  
Jae-Hwan Lee ◽  
Heejun Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Coronary Vessels ◽  
Quantitative Coronary Angiography ◽  
Vessel Segmentation ◽  
Diagnostic Methods ◽  
X Ray ◽  
Fully Convolutional Networks ◽  
Major Vessel

AbstractX-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

scholarly journals Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography

2020 ◽  
Vol 39 (5) ◽  
pp. 1545-1557 ◽  
Author(s):  
Majd Zreik ◽  
Robbert W. van Hamersvelt ◽  
Nadieh Khalili ◽  
Jelmer M. Wolterink ◽  
Michiel Voskuil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Ct Angiography ◽  
Coronary Arteries ◽  
Cardiac Ct ◽  
Invasive Coronary Angiography ◽  
Cardiac Ct Angiography ◽  
Learning Analysis

Content driven QoE assessment for video frame rate and frame resolution reduction

2011 ◽  
Vol 61 (3) ◽  
pp. 769-786 ◽  
Author(s):  
Lucjan Janowski ◽  
Piotr Romaniak ◽  
Zdzisław Papir
Keyword(s):  
Frame Rate ◽  
Video Frame ◽  
Video Frame Rate

Effect of Video Frame Rate on Subjects’ Ability to Shadow One of Two Competing Verbal Passages

1994 ◽  
Vol 37 (5) ◽  
pp. 1204-1210 ◽  
Author(s):  
Melanie Vitkovitch ◽  
Paul Barber
Keyword(s):  
Visual Cues ◽  
Visual Image ◽  
Frame Rate ◽  
Video Frame ◽  
Communication Link ◽  
Subject Group ◽  
Video Telephone ◽  
Improved Performance ◽  
Lower Frame ◽  
Video Frame Rate

In a study addressing future use of video-telephone systems, the ability of 52 young adults with normal hearing to shadow verbal passages was assessed when they could both hear and observe the speaker. This performance was compared to performance in an audio-alone condition. The passages were presented against an irrelevant background message. Effects of varying the video frame rate (i.e., the rate at which frames were sampled) were examined, using rates of 8.3, 12.5, 16.7, and 25 Hz. The presence of the visual image of the relevant speaker always improved performance when compared with a baseline audio-alone condition. The motion of the speaker’s face may generally support the focusing of attention on the target message. However, effects of video frame rate were also apparent, suggesting that specific visual cues became available as the temporal resolution improved. When frame rates of 8.3 Hz and the maximum available rate of 25 Hz were compared, shadowing performance was significantly better across the subject group at the higher frame rate. The comparison of frame rates of 12.5 and 25 Hz did not show reliably improved performance across the whole subject group at 25 Hz, although a small number of subjects seemed to benefit. This suggests there may be some differences in the visual cues used by subjects and consequent differences in the way individuals perform under different frame rates. Performance at 16.7 and 25 Hz did not differ, and this is consistent with previous research that tested people with hearing loss. A frame rate of 16.7 Hz may therefore be adequate for the transmission of facial images via a video communication link to a broad range of users; at the lower frame rates, the performance of users is likely to suffer.

Upconversion dark-field imaging with extended field of view at video frame rate

2020 ◽  
Vol 59 (7) ◽  
pp. 2157
Author(s):  
Saher Junaid ◽  
Peter Tidemand-Lichtenberg ◽  
Christian Pedersen ◽  
Peter John Rodrigo
Keyword(s):  
Dark Field ◽  
Field Of View ◽  
Frame Rate ◽  
Video Frame ◽  
Dark Field Imaging ◽  
Extended Field ◽  
Field Imaging ◽  
Video Frame Rate ◽  
Extended Field Of View
Sign in / Sign up

Export Citation Format

Share Document