Computed tomography (CT) is a widely used x-ray scanning technique. In its prominent use as a medical imaging device, CT serves as a workhorse in many clinical settings throughout the world. It provides answers to urgent diagnostic tasks such as oncology tumor staging, acute stroke analysis, or radiation therapy planning. Spectral Computed Tomography provides a concise, practical coverage of this important medical tool. The first chapter considers the main clinical motivations for spectral CT applications. In Chapter 2, the measurement properties of spectral CT systems are described. Chapter 3 provides an overview of the current state of research on spectral CT algorithms. Based on this overview, the technical realization of spectral CT systems is evaluated in Chapter 4. Device approaches such as DSCT, kV switching, and energy-resolving detectors are compared. Finally, Chapter 5 summarizes various algorithms for spectral CT reconstructions and spectral CT image postprocessing, and links these algorithms to clinical use cases.

The first computed tomography (CT) system was built by Godfrey Hounsfield in 1971. A few years earlier, his co-inventor Allan McLeod Cormack had used the Radon transform and its inverse to theoretically describe a radiological x-ray scanning machine and image reconstruction method. For their research, Cormack and Hounsfield received the 1979 Nobel Prize in Physiology or Medicine.

Currently, CT is a widely used x-ray scanning technique. In its prominent use as a medical imaging device, CT serves as a workhorse in many clinical settings throughout the world. It provides answers to urgent diagnostic tasks such as oncology tumor staging, acute stroke analysis, or radiation therapy planning. Moreover, CT systems are also used in the quality analysis of industrial products or for security screening of luggage at airports.

Spectral CT was introduced in 1975 as an improvement in measurement technology. CT devices were enabled to gain information on the energy-dependent (i.e., spectral) attenuation properties of the object. Different technical realizations were employed. The straightforward solution is the so-called dual-kVp technique. Two CT scans with different x-ray tube acceleration voltages are performed. The two resulting data sets contain information on the spectral x-ray attenuation characteristics of the object. These spectral data can be used to obtain additional information on the object.

The corresponding spectral CT algorithms have two fundamentally different targets. First, two patient tissue types, such as bone and iodine-contrast- media-filled blood, can produce the same range of attenuation gray values in a CT image. In order to differentiate between the two materials, spectral CT data can be weighted for an optimum contrast-to-noise ratio. This task is readily solved for two specific tissues types. The general solution is achieved when the spectral measurement channels are summed with the inverse cube of their mean detected energy as scaling weights.