277 research outputs found
Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI
Automated extraction of quantitative parameters from Cardiac Magnetic
Resonance Images (CMRI) is crucial for the management of patients with
myocardial infarct. This work proposes a post-processing procedure to jointly
analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an
automatic quantification of myocardial contraction and enhancement parameters
and a study of their relationship. For that purpose, the following processes
are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D
DE/Cine rigid registration in a region about the heart, 3) segmentation of the
myocardium on Cine MRI and superimposition of the epicardial and endocardial
contours on the DE images, 4) quantification of the Myocardial Infarct Extent
(MIE), 5) study of the regional contractile function using a new index, the
Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients
with clinically proven myocardial infarction. The comparison between the MIE
and the visually assessed regional function scores demonstrated that the MIE is
highly related to the severity of the wall motion abnormality. In addition, it
was shown that the newly developed regional myocardial contraction parameter
(ATR) decreases significantly in delayed enhanced regions. This largely
automated approach enables a combined study of regional MIE and left
ventricular function
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
© Springer Nature Switzerland AG 2020. Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGE-MRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGE-MRI challenging even for human experts. As part of the Multi-Sequence Cardiac MR Segmentation Challenge, we propose a solution for LGE-MRI segmentation based on two components. First, a generative adversarial network is trained for the task of modality-to-modality translation between cine and LGE-MRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multi-sequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGE-MRI images can be improved by using complementary information provided by non-contrast MRI sequences
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