Cookie Settings

We use cookies to optimize our website. These include cookies that are necessary for the operation of the site, as well as those that are only used for anonymous statistic. You can decide for yourself which categories you want to allow. Further information can be found in our data privacy protection .


These cookies are necessary to run the core functionalities of this website and cannot be disabled.

Name Webedition CMS
Purpose This cookie is required by the CMS (Content Management System) Webedition for the system to function correctly. Typically, this cookie is deleted when the browser is closed.
Name econda
Purpose Session cookie emos_jcsid for the web analysis software econda. This runs in the “anonymized measurement” mode. There is no personal reference. As soon as the user leaves the site, tracking is ended and all data in the browser are automatically deleted.

These cookies help us understand how visitors interact with our website by collecting and analyzing information anonymously. Depending on the tool, one or more cookies are set by the provider.

Name econda
Purpose Statistics
External media

Content from external media platforms is blocked by default. If cookies from external media are accepted, access to this content no longer requires manual consent.

Name YouTube
Purpose Show YouTube content
Name Twitter
Purpose activate Twitter Feeds
X-Ray Imaging and Computed Tomography

Energy-Selective CT

With the introduction of energy-selective photon counting (PC) detectors to the field of medical applications new possibilities have emerged and are now on the edge of becoming technically realizable in clinical practice. This novel type of detector is employing a semiconductor material (usually CdTe) for the x-ray photon detection instead of the conventional scintillator approach that is the current standard in clinical CT. The scintillator-based detectors convert the energy of the x-ray photon into optical photons, which are then detected by a photodiode. This type of detector is therefore indirectly converting and due to its technology also energy integrating (EI), meaning that the energy information of single photons is lost and only the total amount of deposited energy is registered. In contrast to that, the x-ray photon’s energy is generating a charge cloud in the semiconductor of the photon counting detector, which is directly measurable and the number of generated charges is proportional to the energy of the photon. This detector technology is therefore directly converting and since it has a high temporal resolution, it is able to discriminate and count single photons. Due to restrictions in the application-specific integrated circuit (ASIC) and the often encountered high incoming photon flux, it is difficult to read out the exact photon energy for each incident photon, but it is only possible to compare it with energy thresholds in different comparators of the ASIC. Every exceeded threshold leads to an increase of the corresponding counter. Depending on the number of thresholds in the ASIC, multiple energy bins are simultaneously available.

Currently, dual energy (DE) applications with EI detectors are already established in clinical practice. The dual energy CT (DECT) is realized by carrying out two subsequent scans at different x-ray tube voltages, a rapid switching of the tube voltage from projection to projection or by using a system with two x-ray sources and detectors, which can be operated simultaneously at different tube voltages (often 80 kV or 100 kV together with 140 kV).  A main DE application is the identification of iodinated contrast agent in the blood vessels or in cancerous lesions. The DECT technique can also be used to diagnose gouty arthritis, for example, and offer advantages over single energy CT in the context of angiography with bone suppression. All these techniques rely on dual energy material separation. PC detectors might offer more than just two distinct energy information at once. Therefore it is also called multi energy CT (MECT). This gain in degrees of freedom can be exploited in many ways, like the discrimination of multiple high-Z contrast agents or the decomposition into more than two materials in general. Also classical DE applications could possibly benefit from an image noise reduction from the additional information that MECT will provide. We have developed a statistical material decomposition algorithm to achieve a material image noise reduction based on the additional degrees of freedom. A reduced image noise is beneficial for the patient since it can be translated into a reduced scan dose. In addition, our work focuses on improving DE applications that are already in the clinic by making use of the additional information that MECT could provide.

We have shown in simulation studies that PC detectors with more than two energy bins can significantly reduce the patient dose for a material decomposition into water and iodine in an ideal case. However, PC detectors at the current state-of-the-art have to deal with degrading effects, which affect amongst others their energy resolution. These degrading effects unfortunately lead to a worse performance of the PC detectors compared to the current standard of DECT in the case of a material decomposition into water and iodine. To be able to estimate the performance of PC detectors realistically, accurate modeling of the processes in the semiconductor sensor is required. Therefore, we developed an increment pattern concept describing the counter increases and the resulting correlations on the detector level for all different kinds of events. This model describes the photon-matter interactions and the dynamics of the induced charge clouds.

to top
powered by webEdition CMS