Left atrial appendage automatic segmentation, in computed tomography images

The left atrial appendage is one of the anatomical places where most frequently blood thrombi occur. When migrating from the appendage, these thrombi, become blood emboli that, potentially, can compromise the physiology and/or anatomy of cardiac or cerebral blood vessels, being able to generate cerebrovascular events. The left atrial appendage segmentation is very difficult due, mainly, to its location and the identical densitometric information presents into of this appendage and around of the left atrium. In this paper, an automatic technique is proposed to segment this appendage with the purpose of generating important information to the procedure called left atrial appendage surgical closure. This information is linked to the volume and the diameters of the left atrial appendage. The technique consists of a digital pre-processing stage, based on filtering processes and definition of a region of interest and, of one segmentation stage that considers a clustering method. The results are promising and they allow us to calculate useful quantitative variables when characterizing the most lethal appendix of the human body represented by the mentioned appendage. These results are very important in clinical processes where both the shape and volume of this appendage are vital for detecting and monitoring some vascular diseases such as cardiac embolism, arterial hypertension and stroke, among others

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Performance assessment of displacement-field estimation of the human left atrium from 4D-CT images using the coherent point drift algorithm

Background: Cardiac four-dimensional computed tomography (4D-CT) imaging is a standard approach used to visualize left atrium (LA) deformation for clinical diagnosis. However, the quantitative evaluation of LA deformation from 4D-CT images is still a challenging task. We assess the performance of LA displacement-field estimation from 4D-CT images using the coherent point drift (CPD) algorithm, which is a robust point set alignment method based on the expectation–maximization (EM) algorithm. Method: Subject-specific LA surfaces at 20 phases/cardiac cycles were reconstructed from 4D-CT images and expressed as sets of triangular elements. The LA surface at the phase that maximized the LA surface area was assigned as the control LA surface and those at the other 19 phases were assigned as observed LA surfaces. The LA displacement-field was estimated by solving the alignment between the control and observation LA surfaces using CPD. Results: Global correspondences between the estimated and observed LA surfaces were successfully confirmed by quantitative evaluations using the Dice similarity coefficient and differences of surface area for all phases. The surface distances between the estimated and observed LA surfaces ranged within 2 mm, except at the left atrial appendage and boundaries, where incomplete data, such as missing or false detections, were included on the observed LA surface. We confirmed that the estimated LA surface displacement and its spatial distribution were anisotropic, which is consistent with existing clinical observations. Conclusion: These results highlight that the LA displacement field estimated by CPD robustly tracks global LA surface deformation observed in 4D-CT images

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

Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis