Detection of low wall motion and comparison study with scar tissue using 4D left ventricle cardiac images

2018 ◽  
Author(s):  
Chien-Yi Lee ◽  
Yashbir Singh ◽  
Wei Chih Hu
Keyword(s):  
Left Ventricle ◽  
Wall Motion ◽  
Scar Tissue ◽  
Comparison Study ◽  
Cardiac Images

scholarly journals The influence of afterload on left ventricular contractility: 2D-strain and dyssynchrony in stress echocardiography

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
E Karev ◽  
S Verbilo ◽  
E Malev ◽  
M Prokudina ◽  
A Suvorov
Keyword(s):  
Coronary Artery ◽  
Left Ventricle ◽  
Ejection Fraction ◽  
Stress Echocardiography ◽  
Longitudinal Strain ◽  
Wall Motion ◽  
Wall Motion Score Index ◽  
Wall Motion Abnormalities ◽  
Response To Exercise ◽  
Score Index

Abstract Funding Acknowledgements Type of funding sources: None. Background Hypertensive response to exercise (HRE) has negative prognostic value but its impact on the  left ventricle (LV) contractility and on stress echocardiography (SE) results remains controversial. The global longitudinal strain (GLS) and LV dyssynchrony changes in response to afterload increase were shown even in patients with narrow QRS at rest, but not on exertion. Purpose We aimed to analyze the relation between the blood pressure (BP) during SE and LV GLS and dyssynchrony changes. Methods We performed exercise SE on treadmill in 96 patients without coronary artery stenosis (invasive or CT coronary angiography). Patients divided into two groups: HRE (n = 41) and normal response to exercise (NRE) (n = 55). We analyzed GLS and standard deviation of time between the onset of QRS and segmental longitudinal strain peaks (STE-TIME SD) using speckle tracking and 3d-ejection fraction (EF) at rest and on exertion. Results 2D-EF increase was higher in patients with NRE, but 3D-EF did not differ between groups. Wall motion abnormalities (WMA) on peak stress were detected more often in patients with HRE who had higher wall motion score index (WMSI). GLS on exertion and its increment were lower in HRE group (Fig. 1 - "Bull’s eye" diagrams of GLS at rest and on exertion in patient with NRE (upper panel) and HRE (lower panel)). Among dyssynchrony markers we revealed higher values of STE-TIME SD on exertion in HRE group (Table 1). Moreover the analysis showed positive correlations between BP level on exertion and peak GLS (r = 0.56, p < 0.0001), GLS increase (r = 0.54, p < 0.0001) and STE-TIME SD on exertion (r = 0.27, p < 0.02) Conclusions HRE is associated with less increment in GLS and 2D-EF on exertion. Besides LV dyssynchrony signs can appear in response to exaggerated afterload increase even in patients with narrow QRS complexes. Patients with HRE more often show stress-induced WMA and have greater WMSI on exertion in absence of coronary artery lesions, thus HRE can alter the specificity of the test in transient ischemia detection. Table 1 HRE NRE p Δ-2D ejection fraction 5.0 (4.0; 7.0) 10.0 (8.0; 12.5) <0.0000001 Δ-3D ejection fraction 8.25 (4.0; 8.25) 8.24 (8.15; 11.65) 0.09 Wall motion abnormalities on exertion 46.34% 1.8% <0.00001 Wall motion score index 1.0 (1.0; 1.18) 1.0 (1.0; 1.0) 0.00013 GLS on exertion -21.0 (-22.0; -19.0) -24.0 (-26.5; -23.0) <0.0000001 ΔGLS 0.0 (-1.0; 2.0) 4.0 (2.0; 6.0) <0.0000001 STE-TIME SD-IMPOST 42.0 (35.0; 53.0) 35.0 (27.5; 45.0) 0.012 Left ventricle systolic function and dyssynchrony in two groups. Abstract Figure 1.

scholarly journals Analysis of Segmental Strain for the Detection of Chronic Ischemic Scars in Non-Contrast Cardiac MRI Cine Images: A Feasibility Study

2020 ◽  
Author(s):  
Malgorzata Polacin ◽  
Mihaly Karolyi ◽  
Matthias Eberhard ◽  
Alexander Gotschy ◽  
Bettina Baessler ◽  
...  
Keyword(s):  
Wall Motion ◽  
Circumferential Strain ◽  
Radial Strain ◽  
Scar Tissue ◽  
Remote Myocardium ◽  
Healthy Individuals ◽  
Polar Plot ◽  
Global Strain ◽  
Motion Assessment ◽  
Global Peak

Abstract Aims Cardiac magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is considered the gold standard for scar detection after myocardial infarction. In times of increasing skepticism about gadolinium depositions in brain tissue and contraindications of gadolinium administration in some patient groups, tissue strain-based techniques for detecting ischemic scars should be further developed as part of clinical protocols. Therefore, the objective of the present work was to investigate the feasibility of scar detection in segmental strain calculations based on routinely acquired non-contrast cine images in patients with chronic infarcts.Methods Forty-six patients with chronic infarcts and scar tissue in LGE images (5 female, mean age 52 ± 19 years) and 24 gender- and age- matched healthy controls (2 female, mean age 47 ± 13 years) were included. Global (global peak circumferential [GPCS], global peak longitudinal [GPLS], global peak radial strain [GPRS]) and segmental (segmental peak circumferential [SPCS], segmental peak longitudinal [SPLS], segmental peak radial strain [SPRS]) strain parameters were calculated from standard balanced SSFP cine sequences using commercially available software (Segment CMR, Medviso, Sweden). Two independent blinded readers localized potentially infarcted segments in segmental circumferential strain calculations (endo-/epicardially contoured short axis cine and resulting polar plot strain map) and by visual wall motion assessment of cine images. Results Global strain values were reduced in patients compared to controls (GPCS p= 0.02; GPLS p= 0.04; GPRS p= 0.01). Patients with preserved ejection fraction showed also reduced GPCS compared to healthy individuals (p=0.04). In patients, mean SPCS was significantly impaired in subendocardially (- 5,4% +/- 2) and in transmurally infarcted segments (- 1,2% ± 3) compared to remote myocardium (-12,9% +/- 3, p= 0.02 and 0.03, respectively). ROC analysis revealed an optimal cut- off value for SPCS for discriminating infarcted from remote myocardium of - 7,2 % with a sensitivity of 89,4 % and specificity of 85,7%. Mean SPRS was impeded in transmurally infarcted segments (15,9 % +/- 6) compared to SPRS of remote myocardium (31,4% +/- 5; p= 0.02). The optimal cut-off value for SPRS for discriminating scar tissue from remote myocardium was 16,6% with a sensitivity of 83,3% and specificity of 76,5%. 80.3 % of all in LGE infarcted segments (118/147) were correctly localized in segmental circumferential strain calculations based on non-contrast cine images compared to 53.7% (79/147) of infarcted segments detected by visual wall motion assessment (p > 0.01). Conclusion Global strain parameters are impaired in patients with chronic infarcts compared to healthy individuals. Mean SPCS and SPRS in scar tissue is impeded compared to remote myocardium in infarcts patients. Blinded to LGE images, two readers correctly localized 80% of infarcted segments in segmental circumferential strain calculations based on non-contrast cine images, in contrast to only 54% of infarcted segments detected by visual wall motion assessment. Analysis of segmental circumferential strain shows a promising alternative for scar detection based on routinely acquired, non-contrast cine images for patients who cannot receive or decline gadolinium.

Stress Cardiac Magnetic Resonance Imaging

2018 ◽  
pp. 335-343
Author(s):  
Yeonyee E. Yoon ◽  
L. Samuel Wann
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Myocardial Perfusion ◽  
Cardiac Magnetic Resonance Imaging ◽  
Wall Motion ◽  
Scar Tissue ◽  
Resonance Imaging ◽  
Stress Cardiac Magnetic Resonance ◽  
Myocardial Thickening

The chapter Stress Cardiac Magnetic Resonance Imaging reviews how cardiovascular magnetic resonance imaging (CMR) has become a gold standard for evaluating stress induced wall motion abnormalities based on regional endocardial excursion and myocardial thickening. The high spatial and temporal resolution of CMR without limitations imposed by body habitus and acoustic windows allows outstanding visualization of myocardial function. CMR can also be combined with vasodilator stress to perform dynamic first-pass myocardial perfusion imaging. The addition of late gadolinium enhancement allows the accurate of nonviable scar tissue in combination with wall motion and myocardial perfusion assessment. Case studies highlight the opportunity provided by stress CMR.

Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images

2005 ◽  
pp. 553-565 ◽  
Author(s):  
W. Sun ◽  
M. Çetin ◽  
R. Chan ◽  
V. Reddy ◽  
G. Holmvang ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Images

4942Automated recognition of regional wall motion abnormalities by deep neural network interpretation of echocardiography

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M S Huang ◽  
M R Tsai
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Deep Neural Network ◽  
Wall Motion ◽  
Three Dimensional ◽  
Regional Wall Motion ◽  
Regional Wall ◽  
Relationship Analysis ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

Abstract Background The deep neural network assisted in automated echocardiography interpretation joint to cardiologist final confirmation has now been gradually emerging. There were applications applied in echocardiography views classification, chamber size and myocardium mass evaluation, and certain disease detections already published. Our aim, instead of frame-by-frame “image-level” interpretation in previous studies, is to apply deep neural network in echocardiography temporal relationship analysis – “video-level” – and applied in automated left ventricle myocardium regional wall motion abnormalities recognition. Methods We collected all echocardiography performed in 2017, and preprocessed them into numeric arrays for matrix computations. Regional wall motion abnormalities were approved by authorized cardiologists, and processed into labels whether regional wall motion abnormalities presented in anterior, inferior, septal, or lateral walls of the left ventricle, as the ground truth. We then first developed a convolutional neural network (CNN) model to do view selection, and gathered parasternal long/short views, and apical four/two chamber views from each exam, as well as developing view prediction confidence for strict image quality control. Within these images, we annotated part of images to develop the second CNN model, known as U-net, for image segmentation and mark each regional wall. Finally, we developed the major three-dimensional CNN model with the inputs composed of four views of echocardiography videos and then output the final label for motion abnormalities in each wall. Results In total we collected 13,984 series of echocardiography, and gathered four main views with quality confidence level above 90%, which resulted in 9,323 series for training. Within these images, we annotated 2,736 frames for U-net model and resulted in dice score of segmentation 73%. With the join of segmentation model, the final three-dimensional CNN model predict regional wall motion with accuracy of 83%. Conclusions Deep neural network application in regional wall motion recognition is feasible and should mandate further investigation for promoting performance. Acknowledgement/Funding None

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