Dynamic contrast optical coherence tomography (DyC-OCT) is an emerging imaging technique that enhances contrast in optical coherence tomography (OCT) by analyzing fluctuations in OCT signals. This method enables non-invasive, label-free volumetric live cell imaging. DyC-OCT uses dynamic fluctuations in OCT signals to highlight cellular motion, metabolic processes, and other activities, improving image contrast. It has been applied to visualize cellular and tissue morphology, monitor cellular activity, and assess cell viability. DyC-OCT employs various algorithms, including standard deviation (STD), OCT correlation decay speed (OCDS), and power spectral density (PSD), to extract dynamic contrast from OCT signals. These algorithms help distinguish active regions from stationary ones, providing detailed information about tissue dynamics. DyC-OCT can be implemented using different OCT modalities, such as full-field OCT (FF-OCT), swept-source OCT (SS-OCT), and spectral-domain OCT (SD-OCT), each with its own trade-offs in imaging speed, resolution, and depth. DyC-OCT has been used to study various biological samples, including retinal explants, airway tissues, and cancerous tissues, and has shown potential in disease diagnosis and drug screening. Challenges remain in interpreting DyC-OCT signals without validation from other imaging techniques, and further advancements in multi-modal imaging systems and machine learning algorithms are needed to fully exploit DyC-OCT's capabilities.Dynamic contrast optical coherence tomography (DyC-OCT) is an emerging imaging technique that enhances contrast in optical coherence tomography (OCT) by analyzing fluctuations in OCT signals. This method enables non-invasive, label-free volumetric live cell imaging. DyC-OCT uses dynamic fluctuations in OCT signals to highlight cellular motion, metabolic processes, and other activities, improving image contrast. It has been applied to visualize cellular and tissue morphology, monitor cellular activity, and assess cell viability. DyC-OCT employs various algorithms, including standard deviation (STD), OCT correlation decay speed (OCDS), and power spectral density (PSD), to extract dynamic contrast from OCT signals. These algorithms help distinguish active regions from stationary ones, providing detailed information about tissue dynamics. DyC-OCT can be implemented using different OCT modalities, such as full-field OCT (FF-OCT), swept-source OCT (SS-OCT), and spectral-domain OCT (SD-OCT), each with its own trade-offs in imaging speed, resolution, and depth. DyC-OCT has been used to study various biological samples, including retinal explants, airway tissues, and cancerous tissues, and has shown potential in disease diagnosis and drug screening. Challenges remain in interpreting DyC-OCT signals without validation from other imaging techniques, and further advancements in multi-modal imaging systems and machine learning algorithms are needed to fully exploit DyC-OCT's capabilities.