Defining myocardial contours is often the most time-consuming portion of dynamic cardiac MRI image analysis. Displacement encoding with stimulated echoes (DENSE) is a quantitative MRI technique that encodes tissue displacement into the phase of the complex MRI images. Cine DENSE provides a time series of these images, thus facilitating the non-invasive study of myocardial kinematics. Epicardial and endocardial contours need to be defined at each frame on cine DENSE images for the quantification of regional displacement and strain as a function of time. This work presents a reliable and effective two-dimensional semi-automated segmentation technique that uses the encoded motion to project a manually-defined region of interest through time. Contours can then easily be extracted for each cardiac phase. This method boasts several advantages, including, (1) parameters are based on practical physiological limits, (2) contours are calculated for the first few cardiac phases, where it is difficult to visually distinguish blood from myocardium, and (3) the method is independent of the shape of the tissue delineated and can be applied to short- or long-axis views, and on arbitrary regions of interest. Motion-guided contours were compared to manual contours for six conventional and six slice-followed mid-ventricular short-axis cine DENSE datasets. Using an area measure of segmentation error, the accuracy of the segmentation algorithm was shown to be similar to inter-observer variability. In addition, a radial segmentation error metric was introduced for short-axis data. The average radial epicardial segmentation error was 0.36+/-0.08 and 0.40+/-0.10 pixels for slice-followed and conventional cine DENSE, respectively, and the average radial endocardial segmentation error was 0.46+/-0.12 and 0.46+/-0.16 pixels for slice following and conventional cine DENSE, respectively. Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.
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http://dx.doi.org/10.1016/j.media.2008.06.016 | DOI Listing |
Diagnostics (Basel)
November 2024
Department of Radiology, Stanford University, Palo Alto, CA 94305, USA.
In boys with Duchenne muscular dystrophy (DMD), cardiomyopathy has become the primary cause of death. Although both positive late gadolinium enhancement (LGE) and reduced left ventricular ejection fraction (LVEF) are late findings in a DMD cohort, LV end-systolic circumferential strain at middle wall (E) serves as a biomarker for detecting early impairment in cardiac function associated with DMD. However, E derived from cine Displacement Encoding with Stimulated Echoes (DENSE) has not been quantified in boys with DMD.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
May 2024
Department of Electrical and Computer Engineering, University of Virginia, USA.
This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs.
View Article and Find Full Text PDFBiomedica
May 2024
Biomedical Imaging, Vision, and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander, Bucaramanga, Colombia.
Heart Rhythm
January 2025
Department of Cardiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain; Bioheart-Cardiovascular Diseases Group, Cardiovascular, Respiratory and Systemic Diseases and Cellular Aging Program, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain. Electronic address:
Background: ADAS-3D software elaborates cardiac magnetic resonance (CMR) images to obtain a quantitative evaluation of dense scar and border zone (BZ), including BZ channels, which can be useful for ventricular tachycardia ablation and risk stratification. However, most prior reports with ADAS-3D used flexible thresholds (60% ± 5% and 40% ± 5% of maximum pixel signal intensity) to define dense scar and BZ. The impact of such variations of the threshold values on the measurements obtained with ADAS-3D is unknown.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
September 2024
School of Biomedical Engineering and Imaging Sciences, King's College London, 1 Lambeth Palace Rd, London SE1 7EU, UK.
Aims: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank.
Methods And Results: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank.
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