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.
In this paper we describe and evaluate an image-based spectral CT method. Its central formula expresses
measured CT data as a spectral integration of the spectral attenuation coefficient multiplied by a LocalWeighting
Function (LWF). The LWF represents the local energy weighting in the image domain, taking into account the
system and reconstruction properties and the object self attenuation. A generalized image-based formulation of
spectral CT algorithms is obtained, with no need for additional corrections of e.g. beam hardening. The iterative
procedure called Local Spectral Reconstruction (LSR) yields both the mass attenuation coefficients of the object
and a representation of the LWF. The quantitative accuracy and precision of the method is investigated in several
applications, including beam hardening correction, attenuation correction for SPECT/CT and PET/CT and a
direct identification of spectral attenuation functions using the LWF result is demonstrated. In all applications
the ground truth of the objects is reproduced with a quantitative accuracy in the sub-percent to two percent
range. An exponential convergence behavior of the iterative procedure is observed, with one to two iteration
steps as a good compromise between quantitative accuracy and precision. We conclude that the method can
be used to perform image-based spectral CT reconstructions with quantitative accuracy. Existing algorithms benefit from the intrinsic treatment of beam hardening and system properties. Novel algorithms are enabled to directly compare material model functions to spectral measurement data.
A generalized method to evaluate the noise transfer properties of the base material decomposition has been developed. We apply the method to a typical dual-energy CT scan with energy weightings and doses of a 80kV / 140kV scan. For sets {P1, P2} of dual-energy projections with Pi = 10-4.5 ... 1, both the water and bone decomposition and the Compton and Photo Effect decomposition are analyzed. As a figure of merit we determine the noise amplification factors A1, A2. They are given by the ratio of the relative noise of the dual-energy projections B1, B2 to the relative noise of the combined projection data P. The B1, B2 and their variance are simulated by numerical inversion and integration. For the water and bone decomposition an average noise amplification of 3 to 5 is shown. For small contributions of one base material, the noise amplification becomes critically large. In this case the water and bone base material decomposition seems not to be usable for quantitative CT. The Compton and Photo effect decomposition are shown to be more robust in this respect. Physically, both coefficients can only reach zero simultaneously. The Compton coefficient has significantly better noise characteristics than the Photo Effect coefficient. For a partial region of the P1, P2 plane it shows better noise performance than the combined raw data P.
We report the implementation and first test results of a two-channel spectral Computed Tomography (CT) prototype. We use an energy-resolving CT detector with a sandwich-like two layer set-up. Compared to dual-energy approaches with tube voltage switching, it yields a low and a high energy channel in a one shot measurement. We explain the basic set-up of the system and its calibration. The effects of spectral weighting are examined and the weighting functions w(E) of the detector channels are calculated. We present spectral image data of a water phantom, a set of calibration materials and an organic sample. Finally, we show how the data can be used for quantitative CT measurements. The system is work in progress and currently not available in the United States.
Course Instructor
SC987: Spectral CT Imaging
This course provides attendees with an advanced knowledge of spectral CT imaging. The course focuses on the properties of a spectral CT measurement and the main applications in spectral CT reconstruction and spectral CT image postprocessing. Many clinical examples of spectral CT imaging applications are provided to illustrate the diagnostic outcome of this technique.
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