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Chapter 13:
Synthesizing Anatomical Images for Image Understanding
The main goals of research in medical and biomedical imaging are to create "€œbetter"€ imaging systems, more accurate reconstructions, and develop methods of image processing that utilize the most important information present in an image or set of images, for accurate, timely, and cost-effective diagnosis and treatment of disease. How well desired information can be extracted best serves to define image quality and consequently the performance of imaging systems. Furthermore, as we deepen our understanding of medical images, new systems and new methods of image processing and image display may help optimize performance and assist radiologists in their challenge for accurate diagnostics. Image quality is thus best measured by the performance of an observer on specific tasks [1]. The observer may be human such as a physician making a diagnosis, or a mathematical model such as an ideal observer [2], or a computer algorithm. Specific tasks may be the detection of a lesion in a chest x ray [3], the estimation of the percent stenosis of a detected aneurysm [4], or the registration of images such as the superimposition of anatomical atlases on patient data [5]. Regardless of the task considered, however, methods to assess image quality based on task performance are most important. Traditionally, the conception of improved imaging systems in medical imaging has been accomplished by designing and constructing proposed systems and characterizing them from an engineering point of view, by reporting parameters such as resolution, modulation transfer function, and pixel signal-to-noise ratio. Naturally, such evaluation is not sufficient to predict the performance that the system will have in the clinic, given that it does not take into account either information about the types of objects being imaged or the tasks being carried out. To fully assess the system, clinical trials based on specific tasks must be conducted. While clinical trials are required for final system assessment, the reliance on clinical trials for system design and optimization has severe limitations. First, the construction of proposed systems is costly and time consuming. In addition, if the system requires further optimization, it may take multiple trials and errors to find the best set of parameters and tradeoff to satisfy required task-based image-quality criteria. Finally, it is nearly impossible to know the anatomical underlying structures of the acquired clinical images unless the diagnosis is verified by physical examination. Instead, disease states are most often estimated by interpreting the images and looking for correlation with previous cases established statistically as well. The approach of building systems and conducting trial-and-error changes based on clinical images thus constitutes a highly impractical means of optimizing imaging systems.
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