scholarly journals Assessment of Left Ventricular Myocardial Diseases with Cardiac Computed Tomography

2019 ◽  
Vol 20 (3) ◽  
pp. 333
Author(s):  
Sung Min Ko ◽  
Tae Hoon Kim ◽  
Eun Ju Chun ◽  
Jin Young Kim ◽  
Sung Ho Hwang
Keyword(s):  
Computed Tomography ◽  
Cardiac Computed Tomography ◽  
Left Ventricular ◽  
Myocardial Diseases

3255Characterization of the arrhythmogenic substrate with multimodality imaging in ischemic patients undergoing VT ablation: relationship between cardiac computed tomography and magnetic resonance

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
G Zucchelli ◽  
D Soto Iglesias ◽  
B Jauregui ◽  
C Teres ◽  
D Penela ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Magnetic Resonance ◽  
Cardiac Ct ◽  
Low Voltage ◽  
Multimodality Imaging ◽  
Cardiac Computed Tomography ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Arrhythmogenic Substrate

Abstract Background Cardiac magnetic resonance (CMR)-aided ventricular tachycardia (VT) substrate ablation has shown to improve VT recurrence-free survival, through a better identification of the arrhythmogenic substrate. However, the access to CMR may be limited in certain centers or sometimes Its use can be contraindicated in patients with cardiac implantable electronic device. Cardiac computed tomography (CT) has shown to improve the results of substrate ablation, correlating with low-voltage areas and local abnormal ventricular activity, and identifying ridges of myocardial tissue (CT-channels) that may be appropriate target sites for ablation. Purpose To evaluate the correlation between CT and CMR imaging in identifying anatomical heterogeneous tissue channels (CMR-channels) or CT-channels in ischemic patients undergoing VT substrate ablation. Methods The study included 30 post-myocardial infarction (MI) patients (mean age 69±10; 94% male, left ventricular ejection fraction 35±10%), who underwent both CMR and cardiac CT before VT substrate ablation. Using a dedicated post-processing software, the myocardium was segmented in 10 layers from endocardium to epicardium both for the CMR and CT, characterizing the presence of CMR-channels and CT-channels, respectively, by two blinded operators, assigned either to CMR or CT analysis. CMR-channels were classified as endocardial (CMR-channels in layer <50%), epicardial (CMR-channels in layers ≥50%) or transmural (in both endo and epicardial layers). Presence and location of CT and CMR-channels were compared. Results In 26/30 patients (86.7%) 91 CT-channels (mean 3.0±1.9 per patient) were identified while 30/30 (100%) showed CMR-channels (n=76; mean 2.4±1.2 per patient). We found 190 CT-channel entrances (mean 6.3±4.1 per patient), and 275 CMR-channel entrances (mean 8.9±4.9 per patient) on cardiac CT and CMR, respectively. There were 47/91 (51.6%) true positive CT-channels. On the contrary, 44/91 (48.4%) CT-channels were considered false positives [19/91 (20.9%) identified out of CMR scar], and 29/76 (38.2%) CMR-channels could not be identified on CT. Thirty-six out of 76 (47.4%) CMR-channels were considered as non-endocardial (epi- or transmural). Twenty-nine out of 36 (80.5%) non-endocardial CMR-channels were coincident with CT-channels. CT and CMR Channels Conclusion CT shows a modest sensitivity in identifying CMR-channels and fails in ascertain their complexity, underestimating the number of entrances; however, channels location at CT fit well with CMR for those classified as transmural or epicardial.

scholarly journals Diagnostic criteria for left ventricular non-compaction in cardiac computed tomography

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235751
Author(s):  
Tobias A. Fuchs ◽  
Ladina Erhart ◽  
Jelena R. Ghadri ◽  
Bernhard A. Herzog ◽  
Andreas Giannopoulos ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Diagnostic Criteria ◽  
Cardiac Computed Tomography ◽  
Left Ventricular

Non-ischemic endocardial scar geometric remodeling toward topological machine learning

2020 ◽  
Vol 234 (9) ◽  
pp. 1029-1035
Author(s):  
Yashbir Singh ◽  
Deepa Shakyawar ◽  
Weichih Hu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Cardiac Computed Tomography ◽  
Scar Tissue ◽  
Left Ventricular ◽  
Machine Learning Algorithms ◽  
Myocardial Diseases ◽  
Morphological Image Processing ◽  
Computed Tomography Images ◽  
Morphological Image

Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and better evaluation of cardiovascular diseases. Our goal is to apply topology data analysis toward machine learning algorithms to confirm the geometry of scar tissue, in addition to gaining better visualization and quantification of the scar tissue present. We have introduced architecture for integrating geometry in the form of topology toward machine learning. Morphological image processing was carried out to define the regions of the endocardial wall. We implemented convolutional neural networks on delayed enhancement cardiac computed tomography images for the recognition of scar tissue. Segmented two-dimensional images were stacked up to build the geometry of the scar area for visualization purposes. Mathematical calculations were executed for the validation of the scar tissue in addition to performing morphological image processing and marking the scar tissue present on the endocardial wall of the left ventricular. We applied convolutional neural network over convolution and pooling the layers with small sizes; we achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity, and found the dissimilarity distance between the normal endocardial tissue distances to be 9.37. This new concept in this study contributes toward a better understanding of scar structure and transmural variation of the endocardial wall of the left ventricular.

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