Multiview Sequential Learning and Dilated Residual Learning for a Fully Automatic Delineation of the Left Atrium and Pulmonary Veins from Late Gadolinium-Enhanced Cardiac MRI Images

Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patient

Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation

Background: Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre. Methods: Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels. Results: Global normalized intensity threshold levels T PRE = 1 1/4 and T POST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre. Conclusions: The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies

Atrial Myopathy Underlying Atrial Fibrillation

While AF most often occurs in the setting of atrial disease, current assessment and treatment of patients with AF does not focus on the extent of the atrial myopathy that serves as the substrate for this arrhythmia. Atrial myopathy, in particular atrial fibrosis, may initiate a vicious cycle in which atrial myopathy leads to AF, which in turn leads to a worsening myopathy. Various techniques, including ECG, plasma biomarkers, electroanatomical voltage mapping, echocardiography, and cardiac MRI, can help to identify and quantify aspects of the atrial myopathy. Current therapies, such as catheter ablation, do not directly address the underlying atrial myopathy. There is emerging research showing that by targeting this myopathy we can help decrease the occurrence and burden of AF

Interventional techniques in the management of persistent atrial fibrillation

Atrial fibrillation (AF) is a common cardiac rhythm problem experienced by patients and comprises an increasing demand on healthcare systems. AF is characterised by advanced neurohormonal remodelling in the atria resulting in dilation and variable degree of atrial fibrosis that can be measured by imaging techniques with difficulty in developing methods of identifying and quantifying left atrial (LA) fibrosis. LA fibrosis can be estimated by measuring LA scar using non-invasive imaging methods such as strain imaging in advanced echocardiography and in cardiac magnetic resonance (CMR) imaging. Achieving rhythm control strategy utilising catheter ablation (CA) has shown to be advantageous in improving quality of life (QOL) in patients with paroxysmal AF. The most effective method in management of AF has remained elusive in non-paroxysmal AF. Thoracoscopic surgical ablation (TSA) has been developed over the last decade by experienced surgeons with some promising early results but has not been investigated in long-standing persistent AF (LSPAF). I have attempted to answer some of the relevant questions that have remained in management of LSPAF by conducting a multicentre randomised control trial comparing efficacy between CA and TSA (CASA-AF RCT) and improvements in quality of life indices. In a sub-study, I measured LA volumes using echocardiography and CMR to determine reverse remodelling and LA function using tissue Doppler imaging and strain imaging to predict AF recurrence. In a CMR sub-study, a novel automatic LA segmentation algorithm was used to quantify LA fibrosis before and after ablation. I was able to quantify the response of the autonomic nervous system to targeted ganglionic plexi (GP) ablation as part of TSA compared to CA by measuring heart rate variability. I am hopeful that the knowledge gained from this thesis will help with an appropriate selection that will improve the management of patients with LSPAF.Open Acces