Novel Indices for Left-Ventricular Dyssynchrony Characterization Based on Highly Automated Segmentation From Real-Time 3-D Echocardiography

Zhang, Honghai; Abiose, Ademola K.; Gupta, Dipti; Campbell, Dwayne N.; Martins, James B.; Sonka, Milan; Wahle, Andreas

doi:10.1016/j.ultrasmedbio.2012.08.019

Cited by 15 publications

(13 citation statements)

References 29 publications

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“…However, the combined flow method proposed in [19] is time-consuming. Most importantly, the hundred percent diagnosis accuracy of the proposed approach in this paper indicates that the selected four middle slices could reflect the mechanical dyssynchrony of LV adequately, which is an advantage in efficiency over the previous research [20] that utilizes basal, mid and apical segments. In addition, the proposed approach avoids processing the complex basal and apical images (signals) to solve the low reproducibility problem pointed out in [17].…”

Section: Automatic Boundary Identificationmentioning

confidence: 84%

“…5. To the extent LVD detection is concerned, the proposed approach in this paper is more efficient than the method in [20]. Although the imaging modality is different, the hundred percent diagnosis accuracy achieved in this paper indicates that all the 13 segments might not be required for the measurement of the mechanical dyssynchrony in LV.…”

Section: Discussionmentioning

confidence: 95%

“…Most importantly, the low reproducibility problem of measuring the mechanical dyssynchrony is mainly caused by the complex basal and apical images (signals) [17]. As a result, the proposed approach might be more robust than the proposed method in [20] outside …”

Section: Discussionmentioning

confidence: 99%

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Robust and automatic diagnosis of the intraventricular mechanical dyssynchrony for the left ventricle in cardiac magnetic resonance images

Wang

2017

Int J CARS

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Purpose Robust and automatic diagnosis of the mechanical dyssynchrony for the left ventricle is essential for the cardiac resynchronization therapy. However, no existing method could meet the desired accuracy yet. In this paper, a new approach is proposed for auto-diagnosis of the intraventricular mechanical dyssynchrony of the left ventricle based on a series of image processing and signal processing techniques. Methods Firstly, the boundary of the left ventricle is identified automatically by the segmentation method. Secondly, the correspondence trajectories are computed based on the tangent field to make sure that they are perpendicular to all the intercepted boundaries. Thirdly, the intercepted points are smoothed by the proposed Fourier shape filter to eliminate noise and increase the diagnosis accuracy. Fourthly, the mechanical dyssynchrony is defined as the lag times between the periodic change of the sampled boundary points and the periodic change of the area of the left ventricle. It is calculated by cross-correlation. Results The segmentation method is trained with 10 cases, the proposed approach is evaluated with other 40 tested cases (20 normal cases and 20 patient cases), and the diagnosis accuracy is 100%. Conclusions Experimental results showed that the proposed approach can accurately diagnose whether the left ventricle has mechanical dyssynchrony or not from the computed lag times. The proposed approach is robust in auto-diagnosis of the mechanical dyssynchrony for the left ventricle in cardiac magnetic resonance images.

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Section: Automatic Boundary Identificationmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 95%

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Robust and automatic diagnosis of the intraventricular mechanical dyssynchrony for the left ventricle in cardiac magnetic resonance images

Wang

2017

Int J CARS

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“…For each frame of all OCT pullbacks, luminal, intimal, and medial surfaces were automatically segmented using a fully three‐dimensional LOGISMOS graph‐based approach developed at the University of Iowa, as previously documented . Boundaries were identified as OCT brightness changes showing tissue interfaces between adjacent wall layers.…”

Section: Methodsmentioning

confidence: 99%

Heart rate and early progression of cardiac allograft vasculopathy: A prospective study using highly automated 3‐D optical coherence tomography analysis

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Chen

et al. 2020

Clinical Transplantation

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Introduction Heart rate slowing agents are frequently prescribed to manage heart transplant (HTx) patients with the assumption that higher heart rate is a risk factor in cardiovascular disease. Patients and Methods This prospective two‐center study investigated early progression of cardiac allograft vasculopathy (CAV) in 116 HTx patients. Examinations by coronary optical coherence tomography and 24‐hour ambulatory ECG monitoring were performed both at baseline (1 month after HTx) and during follow‐up (12 months after HTx). Results During the first post‐HTx year, we observed a significant reduction in the mean coronary luminal area from 9.0 ± 2.5 to 8.0 ± 2.4 mm2 (P < .001), and progression in mean intimal thickness (IT) from 106.5 ± 40.4 to 130.1 ± 53.0 µm (P < .001). No significant relationship was observed between baseline and follow‐up mean heart rates and IT progression (R = .02, P = .83; R = −.13, P = .18). We found a mild inverse association between beta‐blocker dosage at 12 months and IT progression (R = −.20, P = .035). Conclusion Our study did not confirm a direct association between mean heart rate and progression of CAV. The role of beta blockers warrants further investigation, with our results indicating that they may play a protective role in early CAV development.

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“…Graph search (optimal surface segmentation) was initially published by Wu et al in [68] where a graph based framework is proposed to solve for a multiple surfaces segmentation problem. Since then graph search and its variations have been widely used in many applications, especially various medical image segmentation applications [69][70][71][72][73][74][75][76][77][78][79][80][81][82]. In general, the graph search framework transforms a discrete image/label volume into a multi-column graph space where every voxel in the original volume corresponds to a node in the graph.…”

Section: Graph Seach Methodsmentioning

confidence: 99%

MRI fat-water separation using graph search based methods

Cui

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In vivo magnetic resonance imaging (MRI) has been widely used in the clinical setting and radiological research. Since it is radiation free and can provide images of high quality, it has played a promising and increasingly active role in medicine overall. Most clinical MRI applications collect signal from protons, either as part of water or fat. By applying different imaging schemes and acquisition times, MRI can provide excellent contrast between soft tissues based on whether the signal is from water protons or fat ones. Due to the difference between biochemical structures of fat and water, fat signal can appear bright in many important imaging scenarios and therefore obscure underlying pathology such as inflammation, edema or progression of certain tumor. Meanwhile, direct visualization of fat distribution is also desirable in various pathologies such as fatty tumors, including adrenal adenomas, angiomyolipomas and other fat-related mesenchymal tumors. Quantification of visceral adipose tissue is also enjoying a great interest in recent studies. In these cases, separation of water and fat signals can be highly useful by providing water-only and/or fat-only images. However, the separation of fat and water signals in MRI remains difficult due to multiple hurdles. The mathematical model for the fat water signal is flawed by nature in the presence of fat-or water-only locations. On the other hand, while technology advancement in the MRI scanner is bringing more promising diagnosis and treatment plans for patients, it also poses challenges in redesigning current fat water separation methods for the advancement. Large B0 magnetic field variations vii can make the separation problem more ill-conditioned. The increasing data size and resolution demand faster, simpler and more accurate approaches to solve the fat water separation problem. The goal of this thesis is to develop new fat water separation methods by employing graph search based models. The developed methods achieve global optimality of the newly designed problem formulations for fat water separation. New frameworks are also proposed in this work to reduce the computational time by an order of magnitude compared to state-of-the-art methods. With experiments on a variety of datasets, particularly with challenging anatomical regions and of large size, the proposed methods have demonstrated their great potential to improve the stateof-the-art methods in more applications of MRI fat water separation. viii

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Novel Indices for Left-Ventricular Dyssynchrony Characterization Based on Highly Automated Segmentation From Real-Time 3-D Echocardiography

Cited by 15 publications

References 29 publications

Robust and automatic diagnosis of the intraventricular mechanical dyssynchrony for the left ventricle in cardiac magnetic resonance images

Robust and automatic diagnosis of the intraventricular mechanical dyssynchrony for the left ventricle in cardiac magnetic resonance images

Heart rate and early progression of cardiac allograft vasculopathy: A prospective study using highly automated 3‐D optical coherence tomography analysis

MRI fat-water separation using graph search based methods

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