A smart-imaging system for in-vivo heart tissue characterization
Number of Students : 2
Guides : Hardik J Pandya
Cardiovascular diseases (CVD) are the leading cause of disease burden worldwide. After China, India is the second highest country to record deaths due to cardiovascular diseases. Alterations in tissue compositions generally characterize myocardial disorders. These extracellular matrix (ECM) alterations can cause heart systolic and/or diastolic dysfunction, increasing the risk of unfavourable cardiovascular events. As a result, early detection of structural cardiac abnormalities has major diagnostic and prognostic value. Fibrosis is the production of fibrous connective tissues in response to an injury, which is characterized by the accretion of ECM components, mainly collagen, near the site of damage. Fibrosis is an essential component of tissue restoration and wound healing. When fibrosis advances uncontrollably, the damaged tissue becomes permanently stiffened, leading to organ failure and death. Due to the unique morphological and physiological properties of the ventricular myocardium and the clinical significance of ventricular failure, there is a need to explore specific mechanisms of myocardial fibrosis and interstitial remodelling in the heart tissues. In this direction, a hand-held probe integrated with a smart imaging system would be beneficial in characterizing the heart tissues in-vivo and exploring the extent of fibrosis in heart tissues. This project involves developing an optical fiber technology-based intraoperative probe for in-vivo tissue characterization. The probe will consist of near-infrared light sources and detectors that use polarimetry-based diffuse reflectance spectroscopy to identify the presence of fibrosis. A graphical user interface that collects data from the probe for the surgeon to monitor the extent of fibrosis in the region of interest in the heart has also to be developed. The students are expected to superimpose the optical characterization data with the image of the heart tissue captured using a camera that is also available at the probe tip for better visualization by the surgeon. The project would also require machine learning augmented data processing to classify tissue regions as fibrotic and normal. At the end of the project, the students are expected to learn Biomedical optics and electronic system design and testing, API development for image/video acquisition and processing in real-time, Machine learning, and Android/Windows-based GUI development. The project provides unique exposure to working with a lead cardiothoracic surgeon.
