Recent developments in advanced imaging modalities such as magnetic resonance imaging (MRI), multidetector computed tomography (CT) and hybrid modalities such as positron emission tomography-CT (PET-CT) bring new dimensions to diagnostic investigations of cardiovascular disease. These modalities extend the existing wide range of image-based diagnostic procedures that are commonly used in cardiology. More than in any other discipline, cardiologists rely extensively on imaging techniques for decision support and for patient management. In addition, unlike other disciplines, cardiovascular investigations require specialised image analysis techniques for extraction of quantitative data for accurate evaluation of cardiac and vascular physiological and functional alterations. With the increase in accuracy of the information provided by new imaging modalities, the complexity of image analysis and quantitative measurement tools has also increased significantly.
With the growing complexity of image-based diagnostic techniques comes the challenge of designing adequate processing and analysis tools that match the needs and requirements of cardiologists for their daily clinical practice. Not only do these tools have to be accurate and simple to operate by non-computer-savvy users, but they must be implemented in ways that ensure fast and efficient processing of very large data sets. Cardiac images tend to come in high spatial and temporal resolution, where the time dimension is essential for the assessment of the heart in motion and time-varying phenomena such as blood flow and perfusion. This fourth dimension of the data results in very large volumes of image data that require extensive storage and processing capabilities. Furthermore, with the ability to visualise and extract metabolic and functional phenomena with molecular imaging techniques such as PET, it is now possible to add a fifth dimension to the data representing regional biological parameters measured in vivo for each segment of tissue. The graphical representation of such complex multi-dimensional data also represents an important challenge for the designers of new computer-aided diagnostic platforms.
In clinical reality, cardiologists also need to communicate the results of their investigations to colleagues and physicians of other disciplines, and not infrequently to the patients themselves. It is therefore necessary to be able to visualise the images in a way that can be easily shown to physicians who do not have the expertise and training to interpret images in their native format. 3-D rendered images as well as dynamic cine movies (4-D images) are becoming methods of choice for communicating results between cardiology specialists and clinicians as well as to patients. Advanced rendering workstations, being quite expensive, are often not accessible to a large community of users but are only available in radiology departments.
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