Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model

Leong, Chen Onn; Lim, Einly; Tan, Li Kuo; Aziz, Yang Faridah Abdul; Sridhar, Ganiga Srinivasaiah; Dokos, Socrates; Chee, Kok Han; Lee, Zhen-Vin; Liew, Yih Miin

doi:10.1002/mrm.27486

Cited by 15 publications

(12 citation statements)

References 48 publications

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“…These accurately integrated geometries were then used in our computational simulation and correlation study. Further details regarding the registration framework are outlined in Leong et al 19 Since the apex of the myocardium (starting from the tip of the LV cavity to the tip of the apex according to AHA 17‐segment model) for all patients were fully infarcted, this region was reconstructed to have transmural infarct.…”

Section: Methodsmentioning

confidence: 99%

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Leong

Liew

et al. 2021

Numer Methods Biomed Eng

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Infarct extension involves necrosis of healthy myocardium in the border zone (BZ), progressively enlarging the infarct zone (IZ) and recruiting the remote zone (RZ) into the BZ, eventually leading to heart failure. The mechanisms underlying infarct extension remain unclear, but myocyte stretching has been suggested as the most likely cause. Using human patient‐specific left‐ventricular (LV) numerical simulations established from cardiac magnetic resonance imaging (MRI) of myocardial infarction (MI) patients, the correlation between infarct extension and regional mechanics abnormality was investigated by analysing the fibre stress–strain loops (FSSLs). FSSL abnormality was characterised using the directional regional external work (DREW) index, which measures FSSL area and loop direction. Sensitivity studies were also performed to investigate the effect of infarct stiffness on regional myocardial mechanics and potential for infarct extension. We found that infarct extension was correlated to severely abnormal FSSL in the form of counter‐clockwise loop at the RZ close to the infarct, as indicated by negative DREW values. In regions demonstrating negative DREW values, we observed substantial fibre stretching in the isovolumic relaxation (IVR) phase accompanied by a reduced rate of systolic shortening. Such stretching in IVR phase in part of the RZ was due to its inability to withstand the high LV pressure that was still present and possibly caused by regional myocardial stiffness inhomogeneity. Further analysis revealed that the occurrence of severely abnormal FSSL due to IVR fibre stretching near the RZ‐BZ boundary was due to a large amount of surrounding infarcted tissue, or an excessively stiff IZ.

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Section: Methodsmentioning

confidence: 99%

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Leong

Liew

et al. 2021

Numer Methods Biomed Eng

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“…A two-stage approach to firstly co-registering anatomical segmentation from one modality to another (typically from bSSFP segmentation to LGE-MRI) and then segment scars based on the co-registered anatomy segmentation ( Leong et al, 2019 ).…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

“…(2) Combination of multiple paired sequences and modalities for segmentation by either cross-modality image style transfer [e.g., Cycle-GAN (Zhu et al, 2017) and UNIT style transfer (Huang et al, 2018;] or multi-input models [e.g., Multi-variable mixture model (MvMM) (Zhuang, 2019)]. (3) A two-stage approach to firstly co-registering anatomical segmentation from one modality to another (typically from bSSFP segmentation to LGE-MRI) and then segment scars based on the co-registered anatomy segmentation (Leong et al, 2019).…”

Section: Segment Lge Cmr Jointly With Other Modalitiesmentioning

confidence: 99%

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Tang

et al. 2021

Front. Physiol.

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Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

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“…To address this problem, researchers investigated three main families of solutions over the last decade. Initially, registration-based approaches were attempted, such that the LV boundaries extracted from the cine-MRI images were propagated onto the LGE-MRI images after non-rigidly aligning the corresponding images ( [4], [5], [6]). Such an approach, however, suffered from a lack of robustness due to inherent differences in appearance between the cine-and LGE-MRI images.…”

Section: Problem and Motivationmentioning

confidence: 99%

Domain generalization in deep learning for contrast-enhanced imaging

Sendra-Balcells,

Campello,

Martín-Isla

et al. 2021

Preprint

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Background: Accurate delineation of the left ventricular boundaries in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is an essential step for scar tissue quantification and patientspecific assessment of myocardial infarction. Many deep-learning techniques have been proposed to perform automatic segmentations of the left ventricle (LV) in LGE-MRI showing segmentations as accurate as those obtained by expert cardiologists. Thus far, the existing models have been overwhelmingly developed and evaluated with LGE-MRI datasets from single clinical centers with homogeneous imaging protocols and characteristics. However, in practice, LGE-MRI images vary significantly between clinical centers within and across countries, in particular due to differences in the MRI scanners, imaging conditions, contrast injection protocols and local clinical practise. Methods: This work investigates for the first time multi-center and multi-vendor LV segmentation in LGE-MRI, by proposing, implementing and evaluating in detail several strategies to enhance model generalizability across clinical sites. These include data augmentation to artificially augment the image variability in the training sample, image harmonization to align the distributions of LGE-MRI images across clinical sites, and transfer learning to adjust existing single-center models to unseen images from new clinical sites. Results: The results obtained based on a new multi-center LGE-MRI dataset acquired in four clinical centers in Spain, France and China, show that the combination of data augmentation and transfer learning can lead to single-center models that generalize well to new clinical centers not included in the original training. Conclusions:The proposed framework shows the potential for developing clinical tools for automated LV segmentation in LGE-MRI that can be deployed in multiple clinical centers across distinct geographical locations.

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Segmentation of left ventricle in late gadolinium enhanced MRI through 2D‐4D registration for infarct localization in 3D patient‐specific left ventricular model

Abstract: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.

Cited by 15 publications

References 48 publications

The role of regional myocardial topography post‐myocardial infarction on infarct extension

The role of regional myocardial topography post‐myocardial infarction on infarct extension

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Domain generalization in deep learning for contrast-enhanced imaging

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