scholarly journals Deep Learning‐Based Coronary Artery Motion Estimation and Compensation for Short‐Scan Cardiac CT

2021 ◽  
Author(s):  
Joscha Maier ◽  
Sergej Lebedev ◽  
Julien Erath ◽  
Elias Eulig ◽  
Stefan Sawall ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Motion Estimation ◽  
Cardiac Ct ◽  
Coronary Artery Motion

scholarly journals Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification—A Deep Learning Based Approach Using Fully Automated Body Composition Analysis

2021 ◽  
Vol 10 (2) ◽  
pp. 356
Author(s):  
Lennard Kroll ◽  
Kai Nassenstein ◽  
Markus Jochims ◽  
Sven Koitka ◽  
Felix Nensa
Keyword(s):  
Adipose Tissue ◽  
Deep Learning ◽  
Coronary Artery ◽  
Cardiac Ct ◽  
Coronary Calcification ◽  
Risk Scores ◽  
Agatston Score ◽  
Composition Analysis ◽  
Significance Level ◽  
Tissue Quantification

(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively. The Coronary Artery Calcium Score (CACS) was extracted and tissue quantification was performed by a deep learning network. (3) Results: The Computed Tomography (CT) segmentations predicted by the network indicated no significant correlation between EAT volume and EAT radiodensity when compared to Agatston score (r = 0.18, r = −0.09). CACS 0 category patients showed significantly lower levels of total EAT and PAT volumes and higher EAT and PAT densities than CACS 1–99 category patients (p < 0.01). Notably, this difference did not reach significance regarding EAT attenuation in male patients. Women older than 50 years, thus more likely to be postmenopausal, were shown to be at higher risk of coronary calcification (p < 0.01, OR = 4.59). CACS 1–99 vs. CACS ≥100 category patients remained below significance level (EAT volume: p = 0.087, EAT attenuation: p = 0.98). (4) Conclusions: Our study proves the feasibility of a fully automated adipose tissue analysis in clinical cardiac CT and confirms in a large clinical cohort that volume and attenuation of EAT and PAT are not correlated with CACS. Broadly available deep learning based rapid and reliable tissue quantification should thus be discussed as a method to assess this biomarker as a supplementary risk predictor in cardiac CT.

scholarly journals Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium

2020 ◽  
Vol 129 ◽  
pp. 109114
Author(s):  
Leonardus B. van den Oever ◽  
Ludo Cornelissen ◽  
Marleen Vonder ◽  
Congying Xia ◽  
Jurjen N. van Bolhuis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Cardiac Ct

Motion estimation and compensation for coronary artery and myocardium in cardiac CT

2015 ◽  
Author(s):  
Qiulin Tang ◽  
James Matthews ◽  
Marco Razeto ◽  
Jesper J. Linde ◽  
Satoru Nakanishi
Keyword(s):  
Coronary Artery ◽  
Motion Estimation ◽  
Cardiac Ct

scholarly journals Deep learning based keypoint rejection system for underwater visual ego-motion estimation

2020 ◽  
Vol 53 (2) ◽  
pp. 9471-9477
Author(s):  
Marco Leonardi ◽  
Luca Fiori ◽  
Annette Stahl
Keyword(s):  
Deep Learning ◽  
Motion Estimation

scholarly journals End-to-End, Pixel-Wise Vessel-Specific Coronary and Aortic Calcium Detection and Scoring Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215
Author(s):  
Gurpreet Singh ◽  
Subhi Al’Aref ◽  
Benjamin Lee ◽  
Jing Lee ◽  
Swee Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Clinical Importance ◽  
Learning Model ◽  
Mean Difference ◽  
Interquartile Range ◽  
History Of ◽  
End To End ◽  
Artery Disease ◽  
Deep Learning Model

Conventional scoring and identification methods for coronary artery calcium (CAC) and aortic calcium (AC) result in information loss from the original image and can be time-consuming. In this study, we sought to demonstrate an end-to-end deep learning model as an alternative to the conventional methods. Scans of 377 patients with no history of coronary artery disease (CAD) were obtained and annotated. A deep learning model was trained, tested and validated in a 60:20:20 split. Within the cohort, mean age was 64.2 ± 9.8 years, and 33% were female. Left anterior descending, right coronary artery, left circumflex, triple vessel, and aortic calcifications were present in 74.87%, 55.82%, 57.41%, 46.03%, and 85.41% of patients respectively. An overall Dice score of 0.952 (interquartile range 0.921, 0.981) was achieved. Stratified by subgroups, there was no difference between male (0.948, interquartile range 0.920, 0.981) and female (0.965, interquartile range 0.933, 0.980) patients (p = 0.350), or, between age <65 (0.950, interquartile range 0.913, 0.981) and age ≥65 (0.957, interquartile range 0.930, 0.9778) (p = 0.742). There was good correlation and agreement for CAC prediction (rho = 0.876, p < 0.001), with a mean difference of 11.2% (p = 0.100). AC correlated well (rho = 0.947, p < 0.001), with a mean difference of 9% (p = 0.070). Automated segmentation took approximately 4 s per patient. Taken together, the deep-end learning model was able to robustly identify vessel-specific CAC and AC with high accuracy, and predict Agatston scores that correlated well with manual annotation, facilitating application into areas of research and clinical importance.

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