Introduction:
Echocardiography is a cornerstone imaging modality for assessing cardiac structure and function. Strain analysis, which quantifies myocardial deformation, is increasingly recognized as an essential tool for diagnosing and managing cardiovascular diseases [1]. Current strain estimation techniques, primarily based on speckle tracking [2], provide valuable insights into global myocardial function but often lack specificity for detecting regional abnormalities. Advancements in artificial intelligence (AI) and image segmentation present an opportunity to enhance regional strain analysis by focusing on specific myocardial regions.
Research Gap:
Previous research has focused on the development and validation of speckle tracking algorithms for strain estimation in echocardiography. While these methods offer valuable insights into global myocardial function, they often lack specificity for detecting regional abnormalities. Additionally, recent advances in AI and image segmentation techniques have shown promise in automating cardiac image analysis tasks, including left ventricle (LV) segmentation. However, few studies have explored the integration of AI-based segmentation with regional strain estimation in echocardiography.
Research Questions:
This PhD project will explore the following research questions:
- How can AI-based image segmentation algorithms be optimised for accurate and efficient delineation of the LV in echocardiographic images?
- What are the optimal approaches for incorporating regional strain estimation techniques with AI-based LV segmentation in echocardiography?
- How do AI-based regional strain estimation methods compare with traditional speckle tracking techniques in terms of accuracy, efficiency, and clinical relevance?
Aims and Objectives:
The primary objective of this PhD project is to leverage AI for regional strain estimation in echocardiography by developing automated image segmentation algorithms and evaluating their feasibility for replacing traditional speckle tracking methods. The project aims to improve the accuracy, efficiency, and clinical utility of strain analysis in cardiac imaging.
The objectives are:
To develop and optimise AI-based image segmentation algorithms for automated LV delineation in echocardiographic images.
To implement regional strain estimation techniques using AI-based segmentation and evaluate their accuracy, precision, and clinical relevance.
To compare the performance of AI-based regional strain estimation with traditional speckle tracking methods and assess their feasibility for clinical adoption.
To investigate the potential clinical applications and implications of AI-based regional strain analysis in cardiovascular imaging and patient care.
Methodology:
- Literature Review: Conduct a comprehensive review of existing literature on echocardiographic strain analysis, AI-based image segmentation, and regional strain estimation techniques. Identify gaps, challenges, and opportunities for integrating AI into regional strain analysis. Review relevant AI algorithms for image segmentation, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms.
- Data Collection: Acquire a diverse dataset of echocardiographic images, including both normal and pathological cases, with ground truth annotations for myocardial segmentation and strain measurements. Collaborate with clinical partners to ensure the availability of high-quality data representative of various cardiac conditions.
- AI-Based Image Segmentation: Develop and optimise AI-based image segmentation algorithms to automatically delineate the left ventricle (LV) from echocardiographic images. Explore deep learning architectures such as CNNs, with variations such as U-Net, Mask R-CNN, or attention mechanisms tailored to cardiac image segmentation. Augment the dataset with data augmentation techniques to improve model generalisation and robustness.
- Regional Strain Estimation: Implement algorithms for regional strain estimation by tracking myocardial deformation within segmented LV regions. Investigate different strain parameters (e.g., longitudinal, circumferential, radial) and their clinical significance for detecting regional abnormalities. Develop novel approaches for integrating regional strain information into clinical decision-making processes.
- Comparative Analysis: Evaluate the performance of AI-based regional strain estimation against traditional speckle tracking methods. Compare strain measurements derived from AI-based segmentation with ground truth values and assess accuracy, precision, and clinical relevance. Perform statistical analyses and cross-validation to validate the robustness and generalisation of the proposed methods.
- Feasibility Study: Conduct a feasibility study to assess the potential of replacing speckle tracking with AI-based segmentation for routine clinical practice. Evaluate factors such as computational efficiency, scalability, and user acceptance. Engage with clinicians and stakeholders to gather feedback and address usability considerations.
Clinical Partners:
The research team will collaborate closely with clinical partners from the Faculty of Medicine, Imperial College London, who will provide invaluable expertise, resources, and access to patient data. Their involvement ensures the clinical relevance and applicability of the research findings.
The clinical partners will facilitate access to echocardiographic datasets, including both normal and pathological cases, from the cardiology department at Imperial College Healthcare NHS Trust. These datasets will serve as the foundation for algorithm development, training, and validation. Additionally, the clinical partners will provide guidance on study design, data interpretation, and clinical relevance, ensuring that the research aligns with real-world clinical needs and challenges.
Furthermore, the clinical partners will offer clinical advice and feedback throughout the project, helping to refine research objectives, methodologies, and outcomes. They will contribute to the validation and clinical validation of the proposed AI-based segmentation and strain estimation techniques, ensuring that the research outcomes are robust, reliable, and clinically meaningful.
Novelty of the Study:
This study addresses a gap in existing literature by exploring the integration of AI-based image segmentation with regional strain estimation in echocardiography. By automating LV segmentation and focusing on specific myocardial regions, this approach has the potential to enhance the accuracy, efficiency, and clinical utility of strain analysis in echocardiography. The project's interdisciplinary nature, combining expertise in cardiac imaging, AI, and clinical cardiology, contributes to advancements in both research and clinical practice.
