Abstract
Cardiovascular diseases (CVDs) remain the leading cause of death worldwide. An effective management and treatment of CVDs highly relies on accurate diagnosis of the disease. As the most common imaging technique for clinical diagnosis of the CVDs, US imaging has been intensively explored. Especially with the introduction of deep learning (DL) techniques, US imaging has advanced tremendously in recent years. Photoacoustic imaging (PAI) is one of the most promising new imaging methods in addition to the existing clinical imaging methods. It can characterize different tissue compositions based on optical absorption contrast and thus can assess the functionality of the tissue. This paper reviews some major technological developments in both US (combined with deep learning techniques) and PA imaging in the application of diagnosis of CVDs.
Highlights
Cardiovascular diseases (CVDs) are a class of diseases affecting the heart and/or the blood vessels
We reviewed some typical work of these learning-based US image analysis methods ranging from selecting a view, performing the required segmentation, and the application in point of care ultrasound imaging
Current learning-based US segmentation methods are mainly based on Convolutional neural networks (CNN) models
Summary
Cardiovascular diseases (CVDs) are a class of diseases affecting the heart and/or the blood vessels. For an effective management and treatment of CVDs, accurate diagnosis of the disease and real-time interventional guidance is critical Various imaging techniques such as X-ray-based imaging (cardiac CT, coronary angiogram), magnetic resonance imaging (MRI) and ultrasound (US) imaging are currently commonly applied in clinics for the diagnosis of CVDs [3]. In PA imaging, short pulses of laser light are transmitted to irradiate the tissue, and are absorbed in the tissue, generating ultrasound signals due to the thermo-elastic expansion.
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